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2 Commits
| Author | SHA1 | Date | |
|---|---|---|---|
 Steven Palma
|
b968020ec4 | ||
|
Steven Palma
|
fc019d3902 |
@@ -167,9 +167,9 @@ jobs:
|
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# ── LIBERO TRAIN+EVAL SMOKE ──────────────────────────────────────────────
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# Train SmolVLA for 1 step (batch_size=1, dataset episode 0 only) then
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# immediately runs eval inside the training loop (env_eval_freq=1, 1 episode).
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# immediately runs eval inside the training loop (eval_freq=1, 1 episode).
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# Tests the full train→eval-within-training pipeline end-to-end.
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- name: Run Libero train+eval smoke (1 step, env_eval_freq=1)
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- name: Run Libero train+eval smoke (1 step, eval_freq=1)
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if: env.HF_USER_TOKEN != ''
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run: |
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docker run --name libero-train-smoke --gpus all \
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@@ -196,7 +196,7 @@ jobs:
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--output_dir=/tmp/train-smoke \
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--steps=1 \
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--batch_size=1 \
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--env_eval_freq=1 \
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--eval_freq=1 \
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--eval.n_episodes=1 \
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--eval.batch_size=1 \
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--eval.use_async_envs=false \
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@@ -65,9 +65,6 @@ repos:
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name: Format Markdown with Prettier
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types_or: [markdown, mdx]
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args: [--prose-wrap=preserve]
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# Jinja2 model-card templates use a .md extension but contain {% ... %} /
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# {{ ... }} tags that prettier's Markdown formatter mangles (e.g. table loops).
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exclude: ^src/lerobot/templates/.*\.md$
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##### Security #####
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- repo: https://github.com/gitleaks/gitleaks
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@@ -51,7 +51,6 @@ pre-commit run --all-files # Lint + format (ruff, typo
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## Notes
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- **Mypy is gradual**: strict only for `lerobot.envs`, `lerobot.configs`, `lerobot.optim`, `lerobot.model`, `lerobot.cameras`, `lerobot.motors`, `lerobot.transport`. Add type annotations when modifying these modules.
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- **Imports**: prefer top-level imports; relative (`from .sibling import X`) across sibling files within a module, absolute (`from lerobot.module import X`) across modules.
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- **Optional dependencies**: many policies, envs, and robots are behind extras (e.g., `lerobot[aloha]`, see `pyproject.toml`). Guard optional imports with `TYPE_CHECKING or _foo_available` at module top + a `require_package(...)` check at use time. Reuse the `_foo_available` flags in `utils/import_utils.py`; don't call `is_package_available`.
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- **Optional dependencies**: many policies, envs, and robots are behind extras (e.g., `lerobot[aloha]`). New imports for optional packages must be guarded or lazy. See `pyproject.toml [project.optional-dependencies]`.
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- **Video decoding**: datasets can store observations as video files. `LeRobotDataset` handles frame extraction, but tests need ffmpeg installed.
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- **Prioritize use of `uv run`** to execute Python commands (not raw `python` or `pip`).
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+1
-1
@@ -138,7 +138,7 @@ lerobot-replay --robot.type=so101_follower --robot.port=<FOLLOWER_PORT> --robot.
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--dataset.repo_id=${HF_USER}/my_task --dataset.episode=0
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```
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**4.9 Train** (default: ACT — fastest, lowest memory). Apple silicon: `--policy.device=mps`. No local GPU? Add `--job.target=<flavor>` (e.g. `a10g-small`, list them with `hf jobs hardware`) to run on Hugging Face Jobs instead. See §6/§7 for policy and duration.
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**4.9 Train** (default: ACT — fastest, lowest memory). Apple silicon: `--policy.device=mps`. See §6/§7 for policy and duration.
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```bash
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lerobot-train \
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Binary file not shown.
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After Width: | Height: | Size: 445 KiB |
@@ -58,7 +58,7 @@ test-act-ete-train:
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--dataset.episodes="[0]" \
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--batch_size=2 \
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--steps=4 \
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--env_eval_freq=2 \
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--eval_freq=2 \
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--eval.n_episodes=1 \
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--eval.batch_size=1 \
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--save_freq=2 \
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@@ -96,7 +96,7 @@ test-diffusion-ete-train:
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--dataset.episodes="[0]" \
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--batch_size=2 \
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--steps=2 \
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--env_eval_freq=2 \
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--eval_freq=2 \
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--eval.n_episodes=1 \
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--eval.batch_size=1 \
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--save_checkpoint=true \
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@@ -126,7 +126,7 @@ test-tdmpc-ete-train:
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--dataset.episodes="[0]" \
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--batch_size=2 \
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--steps=2 \
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--env_eval_freq=2 \
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--eval_freq=2 \
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--eval.n_episodes=1 \
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--eval.batch_size=1 \
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--save_checkpoint=true \
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@@ -161,7 +161,7 @@ test-smolvla-ete-train:
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--dataset.episodes="[0]" \
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--batch_size=2 \
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--steps=4 \
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--env_eval_freq=2 \
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--eval_freq=2 \
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--eval.n_episodes=1 \
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--eval.batch_size=1 \
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--save_freq=2 \
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@@ -178,9 +178,3 @@ test-smolvla-ete-eval:
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--env.episode_length=5 \
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--eval.n_episodes=1 \
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--eval.batch_size=1
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# E2E annotation pipeline smoke test against a tiny in-memory fixture
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# dataset. Opt-in (not part of `make test-end-to-end`) and uses a stub VLM
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# backend, so it does not require a real model checkpoint or GPU.
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annotation-e2e:
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uv run python -m tests.annotations.run_e2e_smoke
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@@ -97,7 +97,7 @@ Training a policy is as simple as running a script configuration:
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```bash
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lerobot-train \
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--policy.type=act \
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--policy=act \
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--dataset.repo_id=lerobot/aloha_mobile_cabinet
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```
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@@ -136,7 +136,6 @@ Learn how to implement your own simulation environment or benchmark and distribu
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- **[X](https://x.com/LeRobotHF):** Follow us on X to stay up-to-date with the latest developments.
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- **[Robot Learning Tutorial](https://huggingface.co/spaces/lerobot/robot-learning-tutorial):** A free, hands-on course to learn robot learning using LeRobot.
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- **[T-Shirt Folding Experiment](https://huggingface.co/spaces/lerobot/robot-folding):** An end-to-end demonstration of folding t-shirts with LeRobot.
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- **[LeLab](https://github.com/huggingface/leLab):** A web interface for LeRobot — teleoperate, calibrate, record datasets, replay, and train your SO arm from the browser, no CLI required.
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## Citation
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@@ -0,0 +1,417 @@
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# Decoupled VLA Inference & Edge Control: System Design Proposal
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## 1. Executive Summary
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This document proposes a production-grade system for decoupling GPU-bound VLA (Vision-Language-Action) policy inference from high-frequency, CPU-bound robot control in LeRobot. The system adopts a **Model-as-a-Service (MaaS)** paradigm using **Zenoh** as the sole transport protocol, enabling multiple edge devices to be served by centralized GPU servers with minimal latency and high reliability.
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An initial prototype exists in `src/lerobot/async_inference/` (gRPC-based, single-client). This proposal defines the target architecture, identifies gaps between the prototype and production requirements, documents known bugs, and establishes the design for the new system.
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---
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## 2. Motivation
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LeRobot's standard control loop runs policy inference and robot I/O in the same process. This works for lightweight policies on local GPUs, but breaks down when:
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- **The policy is too large for edge hardware** (e.g., Pi0 at ~3B parameters requires a dedicated GPU).
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- **Multiple robots need the same policy** (redundant GPU allocation per robot).
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- **Inference latency exceeds the control deadline** (e.g., 200ms inference on a 33ms control loop at 30 FPS).
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Decoupling inference from control solves all three: the edge device runs a tight I/O loop on a CPU, while a GPU server handles inference for one or more clients.
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|
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---
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## 3. Core Architectural Principles
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### 3.1 Model-as-a-Service (MaaS)
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Servers initialize models **once at startup** from a configuration manifest. Edge devices do **not** trigger dynamic model loading — they route to pre-warmed servers and validate compatibility via a status endpoint.
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### 3.2 Multi-Tenant & Stateless Inference
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A single GPU server handles multiple edge devices executing the same task. The server is stateless per inference call — `predict_action_chunk()` is a pure function with no side effects on the model. Client isolation is achieved through per-client observation slots and Zenoh key-expression routing.
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> **Invariant**: `predict_action_chunk()` must remain a pure function (no mutation of `self`) for all supported policies. This is what enables safe multi-tenant sharing of a single model instance. This invariant must be documented and tested.
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### 3.3 Zenoh as primary Transport
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The system uses Zenoh's pub/sub model, replacing the current gRPC implementation. Zenoh provides:
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|
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- **Hierarchical key expressions** for routing (natural fit for the cluster/experiment/model/task topology).
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- **Built-in discovery** (no external service discovery needed).
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- **Non-blocking publish** for observations (fire-and-forget with best-effort QoS).
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- **Reliable delivery** configurable per-topic (required for action chunks).
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- **Shared-memory transport** for same-machine deployments (zero-copy) (if available).
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### 3.4 Local Edge CPU
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Edge devices rely on standard CPUs for sensor polling, image compression, payload serialization, motor control, and data logging. No edge-GPU dependency.
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|
||||
---
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||||
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## 4. System Topology
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|
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- **Cluster**: A set of GPU machines. Identified by `cluster_uuid`.
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- **Experiment**: A logical grouping of servers and clients. Identified by `experiment_tag`.
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- **Server**: One model + one task, pre-warmed. Serves N clients for that model/task combination.
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- **Client**: One robot, one task. Publishes observations, subscribes to actions.
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The number of clients a single server can handle is a **user decision** based on model inference time and acceptable latency.
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|
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---
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||||
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## 5. Component Specifications
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### 5.1 The Edge Device (Client)
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**Responsibilities:**
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1. **Observation capture**: Read sensors (cameras, motors) at the control loop frequency.
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2. **Image compression**: JPEG-encode RGB images before transmission.
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3. **Observation publishing**: Non-blocking Zenoh put to the observation topic.
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4. **Action subscription**: Zenoh callback receives action chunks, deposits into local buffer.
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5. **Action execution**: Pop actions from buffer, send to robot at control frequency.
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6. **Action blending**: When a new action chunk overlaps with the current buffer, blend via configurable aggregation function (weighted average, latest-only, etc.).
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7. **Latency compensation**: Calculate one-way latency from RTT, discard expired initial steps of incoming action chunks.
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8. **Fail-safe**: If action buffer empties, logs a warning.
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9. **Data logging**: Record raw observations and executed actions to local `LeRobotDataset` storage for deferred upload.
|
||||
|
||||
**Threading model:**
|
||||
|
||||
- **Control loop thread** (main): Capture observation → deposit in outbox → pop action from buffer → send to robot → sleep to maintain frequency.
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||||
- **Zenoh action callback** (Zenoh-managed): Receives action chunks, processes RTT, trims stale steps, deposits into action buffer.
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- **Observation publisher thread**: Drains the outbox, compresses images, serializes, publishes via Zenoh.
|
||||
|
||||
> **Design note**: The current prototype blocks on `send_observation` inside the control loop (BUG-1, see Section 9). The new design decouples observation publishing from the control loop entirely, using a separate thread and Zenoh's non-blocking put.
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|
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### 5.2 The Inference Server (GPU Pod)
|
||||
|
||||
**Responsibilities:**
|
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|
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1. **Model pre-warming**: Load model and processor pipelines at startup from config manifest (including expected clients & policy parameters).
|
||||
2. **Status publishing**: Expose model capabilities (policy type, expected camera names, resolutions, action dimensions) via Zenoh queryable.
|
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3. **Observation subscription**: Subscribe to observation topics for all clients of this model/task. Maintain per-client observation slots (newest-only semantics).
|
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4. **Inference**: Single inference thread processes observations sequentially (round-robin across clients). Calls `policy.predict_action_chunk()`.
|
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5. **Action publishing**: Publish action chunks to per-client action topics with reliable QoS.
|
||||
|
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> **Thread safety**: PyTorch's `model.forward()` is not guaranteed thread-safe. Inference will be sequential, latency is mostly about the capabilities of the server to serve multiple requests.
|
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|
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---
|
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|
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## 6. Zenoh Routing & Key Expressions
|
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|
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### 6.1 Key Expression Schema
|
||||
|
||||
```
|
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[cluster_uuid] / [experiment_tag] / [model_id] / [model_version] / [application_tag] / [client_uuid] / [topic]
|
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```
|
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|
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**Example key expressions:**
|
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|
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| Key Expression | Direction | Purpose |
|
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| ------------------------------------------------ | ----------------- | ---------------------------------- |
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| `jupiter/fabio2/pi0/v1/cookie/robot_a4b9/obs` | Client → Server | Observation payload |
|
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| `jupiter/fabio2/pi0/v1/cookie/robot_a4b9/action` | Server → Client | Action chunk |
|
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| `jupiter/fabio2/pi0/v1/cookie/*/obs` | Server subscribes | All observations for pi0/v1/cookie |
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| `jupiter/fabio2/pi0/v1/cookie/status` | Server publishes | Model capabilities (queryable) |
|
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|
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### 6.2 QoS Configuration
|
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|
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| Topic | Reliability | Rationale |
|
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| -------- | ----------- | -------------------------------------------------------------------- |
|
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| `obs` | Best-effort | Dropping stale observations is expected behavior. |
|
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| `action` | Reliable | Every action chunk must be delivered; loss causes action starvation. |
|
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| `status` | Reliable | Client needs accurate capability info before starting. |
|
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|
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### 6.3 Discovery Flow
|
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|
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0. Server goes up with the static configuration.
|
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1. Client constructs its target key prefix: `cluster/experiment/model/version/task/`.
|
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2. Client queries `cluster/experiment/model/version/task/status` (Zenoh queryable).
|
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3. Server responds with its capabilities (expected camera names, image resolutions, action dimensions, model metadata).
|
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4. Client validates its own configuration against server capabilities.
|
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5. On match: client starts publishing observations and subscribing to actions.
|
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6. On mismatch: client logs an error and refuses to start.
|
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|
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No dynamic client discovery for now.
|
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|
||||
---
|
||||
|
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## 7. Message Schema
|
||||
|
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### 7.1 Observation Payload (Client → Server)
|
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|
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| Field | Type | Purpose |
|
||||
| ------------- | ------------------ | ----------------------------------------------------------- |
|
||||
| `seq_id` | `uint64` | Incrementing ID for causality tracking and RTT computation. |
|
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| `client_uuid` | `string` | Identifies the sending client. |
|
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| `state` | `bytes` | Proprioceptive state vector (`numpy.tobytes()`). |
|
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| `images` | `dict[str, bytes]` | JPEG-compressed camera images, keyed by camera name. |
|
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| `task` | `string` | Natural-language task instruction (for VLA conditioning). |
|
||||
|
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### 7.2 Action Payload (Server → Client)
|
||||
|
||||
| Field | Type | Purpose |
|
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| -------------------- | --------- | --------------------------------------------------------------- |
|
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| `response_to_seq_id` | `uint64` | Echoes the observation `seq_id` this action corresponds to. |
|
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| `inference_time_ms` | `float32` | Server-side compute duration (for edge RTT math). |
|
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| `actions` | `bytes` | Action chunk as numpy array bytes (`(chunk_size, action_dim)`). |
|
||||
|
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### 7.3 Status Payload (Server, Queryable)
|
||||
|
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| Field | Type | Purpose |
|
||||
| ----------------------- | ------------------- | ------------------------------------------ |
|
||||
| `model_id` | `string` | Policy identifier (e.g., `pi0`). |
|
||||
| `model_version` | `string` | Model version or checkpoint path. |
|
||||
| `expected_cameras` | `dict[str, (H, W)]` | Expected camera names and shapes. |
|
||||
| `action_dim` | `int` | Dimensionality of the action space. |
|
||||
| `max_actions_per_chunk` | `int` | Maximum chunk size the model supports. |
|
||||
| `observation_features` | `dict` | Full feature specification for validation. |
|
||||
|
||||
### 7.4 Serialization Format
|
||||
|
||||
**MessagePack** for all structured metadata (compact, fast, cross-language). Image payloads are raw JPEG bytes embedded in the MessagePack structure. State vectors use `numpy.tobytes()` with shape/dtype metadata for zero-copy reconstruction.
|
||||
|
||||
**No pickle.** The current prototype uses `pickle.dumps`/`pickle.loads` throughout, which allows arbitrary code execution. This is replaced entirely.
|
||||
|
||||
---
|
||||
|
||||
## 8. Latency Compensation
|
||||
|
||||
### 8.1 RTT Calculation
|
||||
|
||||
The edge device tracks in-flight observations:
|
||||
|
||||
```python
|
||||
in_flight: dict[int, float] = {} # seq_id -> time.perf_counter() at send
|
||||
|
||||
# On send:
|
||||
in_flight[seq_id] = time.perf_counter()
|
||||
|
||||
# On receive action chunk:
|
||||
rtt = time.perf_counter() - in_flight[response_to_seq_id]
|
||||
# delete older keys than the one received
|
||||
```
|
||||
|
||||
> **Important**: Delete only the exact `response_to_seq_id` key from `in_flight`, not all keys `<= response_to_seq_id`. With Zenoh's best-effort transport, messages can arrive out of order. Clearing earlier keys would make their RTT unmeasurable.
|
||||
|
||||
### 8.2 Stale Action Trimming
|
||||
|
||||
When an action chunk arrives, the edge calculates how many initial steps have already expired:
|
||||
|
||||
```python
|
||||
expired_steps = int(rtt / environment_dt)
|
||||
valid_actions = action_chunk[expired_steps:]
|
||||
```
|
||||
|
||||
The valid actions are then blended into the action buffer using the configured aggregation function.
|
||||
|
||||
### 8.3 Edge Cases
|
||||
|
||||
| Scenario | Behavior |
|
||||
| -------------------------------------- | -------------------------------------------------------------------------------------- |
|
||||
| **First observation** (no RTT history) | Apply all action steps without trimming. |
|
||||
| **Dropped observations** | Server infers on next received observation. No special handling needed. |
|
||||
| **Dropped action chunks** | Edge continues executing current buffer. If buffer empties, warn & hold last position. |
|
||||
| **Server crash** | Edge exhausts buffer, holds position, warns & re-validates via status query. |
|
||||
|
||||
> **Assumption**: All currently supported robots are position-controlled (SO100, SO101, OMX). For velocity-controlled robots, the fail-safe must send zero-velocity instead of holding position. This should be configurable per-robot.
|
||||
|
||||
---
|
||||
|
||||
## 9. Known Bugs in Current Prototype
|
||||
|
||||
These issues exist in `src/lerobot/async_inference/` and must be addressed in the new implementation.
|
||||
|
||||
### BUG-1: `send_observation` Blocks the Control Loop (Critical)
|
||||
|
||||
**Location**: `robot_client.py:207`
|
||||
|
||||
`self.stub.SendObservations(observation_iterator)` is a synchronous gRPC call inside the 33ms control loop. For multi-camera observations (several MB after pickle), this consumes 10-20ms on the network, leaving no headroom for sensor capture and motor commands. The robot stutters.
|
||||
|
||||
**Resolution in new design**: Observation publishing is moved to a dedicated thread. Zenoh's `session.put()` is non-blocking by default. The control loop only deposits observations into a local outbox.
|
||||
|
||||
### BUG-2: Race Condition in Action Queue Aggregation (Correctness)
|
||||
|
||||
**Location**: `robot_client.py:236-267`
|
||||
|
||||
The lock on `self.action_queue` is acquired to read `internal_queue = self.action_queue.queue` (a reference to the internal deque), then **released** at line 238. The aggregation logic iterates over this reference outside the lock. Meanwhile, the control loop thread can `get_nowait()` from the same queue, mutating the deque during iteration. At line 267, the entire queue is replaced, but actions popped between 238-267 are silently lost.
|
||||
|
||||
**Fix**: Either hold the lock for the entire aggregation, or `list(self.action_queue.queue)` to copy contents before releasing.
|
||||
|
||||
### BUG-3: No RPC Deadlines (Reliability)
|
||||
|
||||
**Location**: `robot_client.py:278`
|
||||
|
||||
`GetActions` blocks indefinitely if the server hangs (GPU OOM, deadlock). The retry policy handles `UNAVAILABLE` but not a hung connection.
|
||||
|
||||
**Resolution in new design**: The polling `GetActions` pattern is replaced by Zenoh subscription callbacks. The client needs a watchdog timer or check when action queue is empty: if no actions are received for `T` seconds, trigger re-validation via the status service.
|
||||
|
||||
### BUG-4: Similarity Check Ignores Images (Correctness for VLAs)
|
||||
|
||||
**Location**: `helpers.py:280-297`
|
||||
|
||||
`observations_similar()` + `must_go` is a workaround for current architecure limitations to avoid filling up the server queue the first seconds of the task & the robot remaining idle.
|
||||
|
||||
**Resolution in new design**: the server always processes the latest observation per client in its inference loop, and doesn't need similarity gating at all. The client can always push.
|
||||
|
||||
---
|
||||
|
||||
## 10. Gaps Between Prototype and Target Architecture
|
||||
|
||||
### 10.1 Critical (Must Address)
|
||||
|
||||
| # | Gap | Current State | Target State |
|
||||
| --- | ------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| G1 | **Single-client server** | One `observation_queue(maxsize=1)`, one `last_processed_obs`, one `_predicted_timesteps`. `_reset_server()` flushes all state on any new connection. | Per-client state (`ClientState` dataclass) keyed by `client_uuid`. Zenoh key-expression routing provides client isolation. |
|
||||
| G2 | **Dynamic model loading** | Client sends `RemotePolicyConfig` → server calls `from_pretrained()` on demand. | Server loads models at startup from config manifest. `SendPolicyInstructions` RPC eliminated. Client validates via status query. |
|
||||
| G3 | **gRPC transport** | Entire `transport/` directory: proto definitions, generated stubs, chunking utils. 4 RPCs: `Ready`, `SendPolicyInstructions`, `SendObservations`, `GetActions`. | Zenoh pub/sub. Client publishes obs, subscribes to actions. Server subscribes to obs, publishes actions. Dispatching via key expressions. |
|
||||
| G4 | **Pickle serialization** | `pickle.dumps`/`pickle.loads` throughout (arbitrary code execution risk, `# nosec` suppression). | MessagePack for structured metadata + raw JPEG bytes for images + `numpy.tobytes()` for state vectors. |
|
||||
|
||||
### 10.2 Important
|
||||
|
||||
| # | Gap | Current State | Target State |
|
||||
| --- | -------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| G5 | **No RTT/latency compensation** | No `seq_id`, no `response_to_seq_id`, no `inference_time_ms`. Timestamps use `time.time()` (unreliable across machines). | Edge-local `perf_counter` + echoed `seq_id` + server inference duration. Stale action step trimming. |
|
||||
| G6 | **No hierarchical routing** | Direct gRPC channel to `host:port`. | Zenoh key expressions: `cluster/experiment/model/version/task/client/topic`. |
|
||||
| G7 | **No data logging** | `control_loop` has access to obs and actions but doesn't persist them. | Edge records via `LeRobotDataset` (`build_dataset_frame` + `dataset.add_frame`). |
|
||||
| G8 | **No authentication** | `grpc.insecure_channel`. | Zenoh TLS + access control lists on key expressions. |
|
||||
| G9 | **ProcessorPipeline divergence** | Server reimplements observation prep in `helpers.py` (custom `resize_robot_observation_image` with `F.interpolate` bilinear). Diverges from standard `RobotProcessorPipeline`. | Use the standard `RobotProcessorPipeline` + `build_dataset_frame` to ensure behavioral equivalence between record and async inference. |
|
||||
|
||||
### 10.3 Nice-to-Have
|
||||
|
||||
| # | Gap | Current State | Target State |
|
||||
| --- | ------------------------------------- | --------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| G11 | **No status/discovery service** | Bare `Ready()` ping. | Zenoh queryable at `cluster/exp/model/version/task/status`. |
|
||||
| G12 | **No monitoring** | `FPSTracker` + `logging.debug`. | Structured metrics via Zenoh telemetry topics. Wildcard subscriptions for centralized monitoring. |
|
||||
| G13 | **No entry points** | Module-level `__main__`. | `lerobot-policy-server` and `lerobot-robot-client` console scripts in `pyproject.toml`. |
|
||||
| G14 | **Ratio-based observation threshold** | `chunk_size_threshold` (0-1 ratio of queue fill). Scales oddly with different `actions_per_chunk` values. | Absolute time threshold: `buffer_time_s` calibrated to observed RTT. Send observation when `queue_size * environment_dt < buffer_time_s`. |
|
||||
|
||||
---
|
||||
|
||||
## 11. Design Decisions & Rationale
|
||||
|
||||
### 11.1 Why Zenoh Over gRPC
|
||||
|
||||
| Aspect | Zenoh | gRPC |
|
||||
| ------------------------- | -------------------------------------------------------------------------- | ---------------------------------------------------------------------------------- |
|
||||
| Communication model | Pub/sub — natural fit for "client publishes obs, server publishes actions" | Request/response — requires polling (`GetActions` loop) or bidirectional streaming |
|
||||
| Multi-tenant routing | Hierarchical key expressions provide built-in per-client topic isolation | Requires manual per-client channel/stream management |
|
||||
| Discovery | Built-in discovery | Requires external service (mDNS, Consul, etc.) |
|
||||
| Observation publishing | Non-blocking put (fire-and-forget) — resolves BUG-1 automatically | Synchronous stream-unary call — blocks the control loop |
|
||||
| Same-machine optimization | Shared-memory transport (zero-copy) | Loopback TCP |
|
||||
| Telemetry | Wildcard subscriptions (`+/+/+/+/+/metrics`) | Requires separate monitoring infrastructure |
|
||||
|
||||
**Tradeoffs of going Zenoh-only:**
|
||||
|
||||
- Smaller community, less tooling for monitoring/tracing vs. gRPC's mature ecosystem.
|
||||
- No built-in schema enforcement (Zenoh sends raw bytes) — serialization correctness is entirely on us.
|
||||
- Default QoS is best-effort (like UDP). Must explicitly configure reliable delivery for action chunks.
|
||||
- `zenoh-python` bindings are less battle-tested than `grpcio`. Needs integration testing under network stress.
|
||||
|
||||
### 11.2 Why Single Inference Thread (Not Batching)
|
||||
|
||||
True GPU batching across clients requires collecting observations from multiple clients and running a single forward pass. This is difficult because:
|
||||
|
||||
- Clients send observations at different times — waiting to batch adds latency.
|
||||
- Different clients may have slightly different image resolutions.
|
||||
- Error in one client's observation shouldn't affect others.
|
||||
|
||||
**Decision**: Start with sequential processing (single inference thread, round-robin across clients). Profile GPU utilization.
|
||||
|
||||
### 11.4 Why MessagePack (Not Protobuf, Not FlatBuffers)
|
||||
|
||||
- **Protobuf**: Strong schema enforcement but heavier toolchain (proto compilation, generated code). Since we're dropping gRPC, the protobuf dependency becomes unnecessary overhead.
|
||||
- **MessagePack**: Fast, compact, schema-less (enforced by application), excellent Python support (`msgpack` package), good for nested dicts with mixed types. Natural fit for observation/action payloads.
|
||||
|
||||
Images are embedded as raw JPEG bytes within the MessagePack structure. State vectors use `numpy.tobytes()` with shape/dtype metadata for zero-copy reconstruction.
|
||||
|
||||
### 11.5 Action Aggregation Strategy
|
||||
|
||||
When a new action chunk overlaps with the existing buffer, the overlapping timesteps must be blended. The current prototype supports configurable aggregation functions:
|
||||
|
||||
| Function | Formula | Character |
|
||||
| ------------------ | ----------------------- | ------------------------------------------ |
|
||||
| `weighted_average` | `0.3 * old + 0.7 * new` | Smooth transitions, favors new predictions |
|
||||
| `latest_only` | `new` | Most responsive, can cause discontinuities |
|
||||
| `average` | `0.5 * old + 0.5 * new` | Equal weight |
|
||||
| `conservative` | `0.7 * old + 0.3 * new` | Smooth, slow to adapt |
|
||||
|
||||
Ultimately, this should be the user's decision. Default to `weighted_average`. The goal of async is not to do temporal ensembling, but to provide a solution when we want to decouple inference and execution.
|
||||
|
||||
---
|
||||
|
||||
## 12. Configuration
|
||||
|
||||
### 12.1 Server Configuration (Manifest)
|
||||
|
||||
Servers are configured via a YAML manifest that declares which models to pre-warm & clients to serve:
|
||||
|
||||
```yaml
|
||||
cluster_uuid: jupiter
|
||||
experiment_tag: fabio2
|
||||
server:
|
||||
- model_id: pi0
|
||||
model_version: v1
|
||||
pretrained_path: lerobot/pi0-cookie-v1
|
||||
application_tag: cookie
|
||||
device: cuda:0
|
||||
fps: 30
|
||||
endpoint: tcp/192.168.1.50:7447
|
||||
clients:
|
||||
- client_uuid: cookie-worker-4269
|
||||
```
|
||||
|
||||
### 12.2 Client Configuration
|
||||
|
||||
Clients are configured via draccus dataclass (CLI-compatible):
|
||||
|
||||
```python
|
||||
@dataclass
|
||||
class AsyncClientConfig:
|
||||
# Zenoh routing
|
||||
cluster_uuid: str
|
||||
experiment_tag: str
|
||||
model_id: str
|
||||
model_version: str
|
||||
application_tag: str
|
||||
client_uuid: str
|
||||
endpoint: str
|
||||
|
||||
# Robot
|
||||
robot: RobotConfig
|
||||
|
||||
# Control
|
||||
fps: int = 30
|
||||
actions_per_chunk: int = 50
|
||||
aggregate_fn_name: str = "weighted_average"
|
||||
jpeg_quality: int = 90
|
||||
|
||||
# Fail-safe
|
||||
max_empty_cycles_before_warning: int = 10
|
||||
|
||||
# Datset recording
|
||||
dataset_repo_id: str | None = None # None = no logging
|
||||
|
||||
# Task
|
||||
task: str = ""
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 14. Data Logging Integration
|
||||
|
||||
The client records observations and executed actions into a local `LeRobotDataset` for deferred upload to the training dataset:
|
||||
|
||||
```python
|
||||
# In control_loop, after executing an action:
|
||||
if self.dataset is not None:
|
||||
frame = build_dataset_frame(
|
||||
self.dataset.features,
|
||||
processed_observation,
|
||||
prefix=OBS_STR,
|
||||
)
|
||||
frame["action"] = executed_action_tensor
|
||||
self.dataset.add_frame(frame)
|
||||
```
|
||||
@@ -0,0 +1,498 @@
|
||||
# Decoupled VLA Inference & Edge Control v2: Async Network Inference for `lerobot-rollout`
|
||||
|
||||
> **Status**: supersedes the v1 proposal in full. v1 was written against the standalone `src/lerobot/async_inference/` prototype, before `lerobot-rollout` existed. This revision re-grounds the design in the current codebase, keeps v1's decisions that survived contact with it (marked **KEPT** throughout), reverses the ones that didn't, and adds the safety, multi-tenancy, and operations specifications v1 lacked.
|
||||
|
||||
## 1. Executive Summary
|
||||
|
||||
This document specifies a production-grade system for decoupling GPU-bound policy inference from high-frequency robot control, targeting power users running **hundreds of robots** against centralized GPU clusters. The system keeps v1's **Model-as-a-Service (MaaS)** paradigm and **Zenoh** transport, but changes the integration architecture fundamentally:
|
||||
|
||||
- **The client is not a standalone CLI.** It is `--inference.type=remote`, a new `InferenceEngine` backend inside `lerobot-rollout` (`src/lerobot/rollout/inference/`). Every rollout strategy (base, sentry, highlight, dagger, episodic) gets network inference for free — including dataset recording, DAgger pause/resume, Rerun visualization, and safe teardown.
|
||||
- **The client is weightless.** No policy weights, no policy processors on the edge. `--policy.path` resolves to a config-only `PreTrainedConfig` (no weight download) used for pre-flight validation and action ordering.
|
||||
- **The server is stateless per request.** All RTC chunk state (leftover prefixes, latency tracking, delay computation) lives client-side in the existing `ActionQueue`/`LatencyTracker` machinery — the client ships prefixes + a delay hint with each observation. A server crash loses zero control state; reconnects and horizontal scaling are trivial.
|
||||
- **Multi-tenancy is engineered, not assumed.** The real hazards are stateful processor pipelines and episode-scoped policy state — not `predict_action_chunk` purity (which holds for ACT/Pi0/Pi0.5/SmolVLA but _not_ diffusion). The server uses per-session processor instances, a chunk-stateless allowlist, and an exclusive serving mode for policies that need it.
|
||||
- **The legacy module dies.** `src/lerobot/async_inference/` (~1,900 lines, pickle-over-gRPC, single-client, four confirmed bugs) is deleted in the same PR that lands the new backend. No deprecation cycle: the module is experimental, its CLI undocumented in the main flow, and every config field has a mapped successor (§13.4).
|
||||
|
||||
---
|
||||
|
||||
## 2. Motivation (unchanged from v1) — **KEPT**
|
||||
|
||||
LeRobot's standard control loop runs policy inference and robot I/O in the same process. This breaks down when:
|
||||
|
||||
- **The policy is too large for edge hardware** (Pi0-class models need a dedicated GPU).
|
||||
- **Multiple robots need the same policy** (redundant GPU allocation per robot).
|
||||
- **Inference latency exceeds the control deadline** (e.g. 150 ms inference on a 33 ms control tick).
|
||||
|
||||
Decoupling solves all three: the edge runs a tight CPU loop; a GPU server performs inference for N clients.
|
||||
|
||||
What changed since v1: the _local_ version of this decoupling already shipped. `RTCInferenceEngine` (`src/lerobot/rollout/inference/rtc.py`) runs inference in a background thread against a thread-safe `ActionQueue` with latency-aware chunk merging. **The network system is that same architecture with the thread boundary replaced by a network boundary.** This is the design's central simplification: reuse, don't reinvent.
|
||||
|
||||
---
|
||||
|
||||
## 3. Gap Analysis: v1 Proposal vs. Modern Codebase
|
||||
|
||||
| Topic | v1 assumed | Modern reality | Verdict |
|
||||
| ----------------------------------------- | --------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------- | --------------------------------------- |
|
||||
| Client architecture | Standalone robot-client CLI (§5.1 of v1) | `InferenceEngine` ABC seam in `lerobot-rollout` (`rollout/inference/base.py`); strategies are backend-agnostic | **Superseded** — backend, not CLI |
|
||||
| Chunk blending | Configurable aggregation zoo (`weighted_average`, …) | `ActionQueue` replace-with-delay-trim (RTC) / append (non-RTC) (`policies/rtc/action_queue.py:147-217`) | **Superseded** — drop blending entirely |
|
||||
| Latency compensation | Hand-rolled RTT trim (`expired_steps = int(rtt/dt)`, v1 §8.2) | `ActionQueue.merge(..., real_delay, idx_before)` + `LatencyTracker` already do this, validated | **Superseded** |
|
||||
| Multi-tenancy invariant | "`predict_action_chunk()` pure ⇒ safe to share" | Processor state + episode-scoped policy state are the real hazards (§7) | **Incomplete** — fixed in §8.3 |
|
||||
| Data logging | Client-side `build_dataset_frame` + `add_frame` sketch (v1 §14) | Recording strategies (sentry/episodic/dagger) already log obs + executed actions | **Superseded** — free via rollout |
|
||||
| MaaS pre-warm, no dynamic loading | ✓ | Still right; legacy `SendPolicyInstructions` is a pickle/RCE + capacity-planning disaster | **KEPT** |
|
||||
| JPEG observation compression | ✓ | Still right (§10.1) | **KEPT** |
|
||||
| Status/capability validation before start | ✓ (Zenoh queryable) | Still right; extended into a hard sync-safety contract (§8.4) | **KEPT, extended** |
|
||||
| Time-based send threshold (v1 G14) | ✓ | Adopted as `buffer_time_s` | **KEPT** |
|
||||
| Zenoh pub/sub data plane | ✓ | Confirmed; QoS corrected (§6.3), control plane moved to queryables, liveliness added | **KEPT, hardened** |
|
||||
| MessagePack serialization | ✓ | Endorsed (zenoh's `ext` serializer cannot encode numpy); must be version-gated (§10.4) | **KEPT, with schema discipline** |
|
||||
| QoS table (v1 §6.2) | "obs best-effort, actions reliable" | Conflates transport reliability with congestion control; BLOCK on actions is dangerous | **Revised** (§6.3) |
|
||||
| Bugs BUG-1…BUG-4, gaps G1…G14 | Listed as work items | Every one resolved _structurally_ by this design (§13.5 mapping) | **Resolved by design** |
|
||||
|
||||
---
|
||||
|
||||
## 4. Critical Pushbacks on v1
|
||||
|
||||
Each pushback: claim → evidence → consequence for this design.
|
||||
|
||||
**P1 — A standalone client duplicates `lerobot-rollout`.**
|
||||
v1 §5.1 assigns the client: observation capture, action execution at frequency, fail-safe, data logging. Every one of those is already owned by rollout strategies and `send_next_action` (`rollout/strategies/core.py:269-304`), which tolerates `None` actions, runs the interpolator, and routes through the canonical robot processors. A standalone client re-implements loop timing, recording, DAgger UX, Rerun, and teardown safety — and then drifts. _Consequence_: the client is `RemoteInferenceEngine`, registered as `--inference.type=remote` next to `sync` and `rtc`.
|
||||
|
||||
**P2 — The aggregation-function zoo fabricates actions no policy predicted.**
|
||||
`0.3*old + 0.7*new` produces hybrid actions that exist in no policy's output distribution; the logged action becomes unexplainable (bad for the reproducibility story) and the implementation hosted a real lock-release race (BUG-2, `async_inference/robot_client.py:236-267`). RTC's prefix-conditioned chunk generation is the principled mechanism for smooth chunk transitions; plain append covers non-RTC chunking. _Consequence_: `ActionQueue` replace/append are the only two merge semantics. The zoo is deleted.
|
||||
|
||||
**P3 — "predict_action_chunk pure ⇒ multi-tenant safe" is incomplete.**
|
||||
Verified in-tree: (a) `RelativeActionsProcessorStep` caches `_last_state` at preprocess (`processor/relative_action_processor.py:131`) and the postprocessor reads it back (`:189`) — a shared pipeline across clients is a race; (b) `DiffusionPolicy.predict_action_chunk` reads `self._queues`, which only `select_action` populates (`policies/diffusion/modeling_diffusion.py:90-108`) — it is **not** chunk-stateless; (c) SAC/SARM have no `predict_action_chunk` at all. _Consequence_: per-session processor instances (mandatory), a chunk-stateless allowlist, `serving_mode: exclusive` for diffusion-family, refusal at startup for SAC/SARM, and `policy.reset()` is **never** called in shared mode (§8.3).
|
||||
|
||||
**P4 — v1 re-derives latency compensation that already exists, on top of broken clocks.**
|
||||
v1 §8 specifies an in-flight RTT dict and manual stale-step trimming. `ActionQueue.merge(original, processed, real_delay, idx_before)` already trims `real_delay` stale steps and cross-validates against actions consumed in flight (`action_queue.py:219-246`). Worse, the legacy code compares wall clocks across machines (`robot_client.py:420` stamps `time.time()` "to compare timestamps across client and server"; `policy_server.py:178` compares it) — NTP skew is the same order as the latencies being measured. _Consequence_: the **monotonic iron rule** (§11): instants never cross machines; client timestamps are opaque echoed tokens; servers report only durations. `delay_steps = ceil((rtt + inference)/dt)` is computed client-side from client-local `perf_counter` samples and shipped per request.
|
||||
|
||||
**P5 — One-in-flight per client is a correctness requirement, not a tuning choice.**
|
||||
At send time the client snapshots `idx_before = queue.get_action_index()` and the leftover prefixes; `merge` validates against them. Two in-flight requests carry conflicting snapshots — the second merge corrupts both RTC replace mode and append mode. The local RTC thread is also strictly one-inference-at-a-time; one-in-flight preserves exact parity. _Consequence_: the worker publishes one observation, waits for its chunk (or timeout), then sends the next. v1 §8.1's out-of-order in-flight dict is dead weight; a late chunk is accepted only if it answers the _latest_ outstanding `seq_id`, otherwise dropped.
|
||||
|
||||
**P6 — v1's QoS table conflates transport reliability with congestion behavior.**
|
||||
"Reliable delivery for actions" sounds right but the dangerous knob is congestion control: a publisher configured `BLOCK` on the action topic can stall the **server's** publish path on one robot's dead uplink (Zenoh blocks up to `wait_before_close`, then may close the transport). A dropped action chunk is _recoverable by design_ — the client's queue keeps the robot moving and the next chunk replaces it. _Consequence_ (§6.3): actions = `reliability=RELIABLE` (hop-level) + `congestion_control=DROP` + `express=True` + `priority=INTERACTIVE_HIGH`; observations = `DROP` + `DATA`. If WAN loss proves material, upgrade the action topic to Zenoh Advanced Pub/Sub (cache + recovery, zenoh ≥ 1.5) rather than BLOCK.
|
||||
|
||||
**P7 — Schema-less MessagePack invites silent version drift across a 300-robot fleet.**
|
||||
msgpack stays (zenoh's `ext` serializer cannot encode numpy/dataclasses, and the team's choice stands), but naked msgpack dicts across heterogeneous fleet versions fail at runtime, on the robot. _Consequence_ (§10.4): a packed little-endian **attachment header** (`schema_version`, `seq_id`, `episode_id`, `client_mono_ns` — the rmw_zenoh pattern) so routing/correlation never deserializes the body; `schema_version` negotiated at the session handshake; additive-only evolution; golden codec tests. Protobuf-over-ZBytes is the documented fallback if drift bites in practice.
|
||||
|
||||
**P8 — "Deterministic rollout reproducibility" is unattainable on real robots.**
|
||||
No seed controls hardware, sensor noise, or network jitter; RTC's latency-driven trimming is inherently timing-dependent. _Consequence_: the contract is **fully logged + replayable** (§12): recording strategies already persist observations and executed actions; the remote engine adds `(session_id, seq_id, episode_id)` provenance so client datasets join server audit logs mechanically.
|
||||
|
||||
**P9 — v1 has no safety specification.**
|
||||
"Log a warning when the buffer empties" is not a fail-safe for a 300-robot fleet. _Consequence_ (§9): a staleness bound (`max_action_age_s` — never execute an action older than X relative to its source observation), an explicit fallback ladder (`hold` / `repeat_last` / `zero` — zero-command required for future velocity-controlled robots), and a DEAD state that triggers the existing strategy shutdown path (return-to-initial-pose, disconnect) via the same `shutdown_event` mechanism RTC uses (`rtc.py:359-360`).
|
||||
|
||||
**P10 — Capacity must be formula-driven, not "a user decision".**
|
||||
v1 §4 says clients-per-server "is a user decision". With `t` = server time per request, `r` = per-client request rate, `H` = RTC execution horizon, `dt` = control period:
|
||||
`N_max = min( 0.8 / (r·t), (H·dt/2 − RTT_net) / t )`
|
||||
→ ACT @ 20 ms, 1 Hz: ~40 clients/GPU. Pi0 @ 150 ms, 1 Hz: ~5 clients/GPU. 300 robots on Pi0 ≈ 60 GPU pods. _Consequence_: the manifest carries `max_sessions`; the server rejects session opens beyond it (with current load in the reply) so clients retry another replica. Micro-batching is deferred — blocked on a real API issue (`predict_action_chunk` takes a _scalar_ `inference_delay`; batched clients have different delays) — behind a `Scheduler` seam so it can land later without redesign (§8.5).
|
||||
|
||||
**P11 — Discovery ≠ multicast.**
|
||||
Zenoh's multicast scouting does not cross WAN, NAT, or most k8s CNIs. _Consequence_: multicast scouting disabled; clients use static `connect.endpoints` (DNS name of the router) + gossip; presence and liveness come from Zenoh **liveliness tokens** (§6.4), not discovery. "Discovery" for a robot fleet is configuration.
|
||||
|
||||
---
|
||||
|
||||
## 5. System Topology
|
||||
|
||||
|
||||
_(Diagram unchanged from v1 — the topology survives; transport/QoS/session details in it are superseded by §6.)_
|
||||
|
||||
- **Router tier**: one or more `zenohd` routers (k8s Deployment + Service, TLS on 7447). Robots **dial out** to the router (NAT-friendly: labs only need outbound 7447/443). GPU servers join as peers via cluster DNS.
|
||||
- **Server**: one process = one `(model_repo, revision, dtype, device)` on one GPU, pre-warmed from a YAML manifest (**KEPT** from v1, amended: `pin_task: bool` — VLA prompts may vary per session unless pinned).
|
||||
- **Client**: one robot running `lerobot-rollout --inference.type=remote`. Weightless: config-only policy metadata.
|
||||
- **Identity**: `client_uuid` per robot; `session_id` per connection epoch; both in every log line on both sides.
|
||||
|
||||
---
|
||||
|
||||
## 6. Zenoh Design
|
||||
|
||||
All Zenoh claims below were verified against zenoh / zenoh-python 1.x (eclipse-zenoh 1.9.0). Pin: `eclipse-zenoh>=1.9,<2.0`; keep `zenohd` on the same minor as the Python binding. Wheels cover manylinux x86_64/aarch64/armv7l/armv6l + macOS — Raspberry Pi edge clients are covered.
|
||||
|
||||
### 6.1 Key-expression schema
|
||||
|
||||
```
|
||||
@lerobot/<model_id>/<revision>/<task_slug>/<client_uuid>/obs client → server
|
||||
@lerobot/<model_id>/<revision>/<task_slug>/<client_uuid>/action server → client
|
||||
@lerobot/<model_id>/<revision>/<task_slug>/status queryable (capabilities)
|
||||
@lerobot/<model_id>/<revision>/<task_slug>/session queryable (open/validate)
|
||||
@lerobot/<model_id>/<revision>/<task_slug>/<client_uuid>/reset queryable (episode boundary)
|
||||
@lerobot/<model_id>/<revision>/<task_slug>/<client_uuid>/alive liveliness token (client)
|
||||
@lerobot/<model_id>/<revision>/<task_slug>/server/alive liveliness token (server)
|
||||
```
|
||||
|
||||
Rules (hard, enforced by a `sanitize_keyexpr()` helper):
|
||||
|
||||
- Root at the **verbatim chunk** `@lerobot` — verbatim chunks are only matched by identical chunks, so third-party `**` subscribers on a shared router can never scrape the tree.
|
||||
- Sanitize every user-supplied segment (model ids, task strings, uuids): non-empty, no `* $ ? # /`, no leading/trailing/double `/`. A task string containing `/` must be slugified before it becomes a key chunk.
|
||||
- Server subscribes with a **single-depth** wildcard (`.../*/obs`) — never `**` (it would also match `status`, `alive`, …).
|
||||
- v1's `cluster/experiment` prefix segments are dropped from the key schema; they return as free-form `tags` metadata in the session handshake (telemetry/labeling, not routing). Routing topology belongs to deployment (which router you dial), not to key depth.
|
||||
|
||||
### 6.2 Data plane vs. control plane (the rmw_zenoh split)
|
||||
|
||||
- **Data plane = pub/sub** (KEPT from v1): observations up, action chunks down, correlated by `seq_id` in **attachments** (§10.4). Pub/sub rather than query-per-inference because: a timed-out query's late reply is _dropped by the transport_ (wasted inference), whereas a late pub/sub chunk is still mergeable if it answers the latest outstanding seq; and pub/sub leaves room for server-initiated messages (drain notices). The one-in-flight discipline (P5) is enforced in the client worker, not by the transport.
|
||||
- **Control plane = queryables** (request/reply with explicit timeouts; the pattern rmw*zenoh uses for ROS 2 services): `status` (pre-flight capability fetch, 2 s timeout), `session` (open/validate → ack with capabilities + `session_id`), `reset` (episode boundary — \_acknowledged*, so episodic strategies know the server-side episode state is clean). Always pass an explicit `timeout` to `session.get()` — the config default is 10 s, far too long for our watchdogs.
|
||||
- **Episode ordering**: under one-in-flight there is no obs/reset race window in the data plane, but as belt-and-braces the first observation of each episode also carries `episode_start=True` + the new `episode_id` in its header.
|
||||
|
||||
### 6.3 QoS (revised from v1 §6.2 — see P6)
|
||||
|
||||
| Topic | reliability | congestion_control | express | priority | Why |
|
||||
| ------------------ | ----------- | ---------------------- | -------- | ---------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
|
||||
| `obs` | default | **DROP** | false | DATA | Intentional drop already happened at the client's one-slot holder; if the uplink stalls, dropping a frame protects the control loop. |
|
||||
| `action` | RELIABLE | **DROP** (never BLOCK) | **true** | INTERACTIVE_HIGH | Hop-level reliability over TCP; express skips batching for the small (4–50 KB) latency-critical payload; DROP so one dead robot uplink can never stall the server's publish path. Chunk loss is recoverable: the client buffer rides through it. |
|
||||
| control queryables | RELIABLE | default | — | — | Correctness over latency; explicit timeouts bound them. |
|
||||
|
||||
Upgrade path if WAN chunk loss proves material: `AdvancedPublisher`/`AdvancedSubscriber` (zenoh ≥ 1.5) with a small cache + heartbeat-based recovery **on the action topic only**. Hop-by-hop RELIABLE is not end-to-end reliability — Zenoh has no broker persistence; a disconnected subscriber's data is gone. The design assumes this (client state machine, §9).
|
||||
|
||||
### 6.4 Liveliness (presence + watchdogs)
|
||||
|
||||
- Client declares a liveliness token on `.../<client_uuid>/alive`. The server liveliness-subscribes with `history=True`: token appear → ensure session state; token drop → GC the session (mailbox, processor instances) after a grace period.
|
||||
- Server declares `.../server/alive`. The client liveliness-subscribes: on drop → treat as RECONNECTING (§9), hold/fallback per config, re-run the `status`/`session` handshake when the token reappears.
|
||||
- Tune the transport lease down from its default so ungraceful-death detection is seconds, not tens of seconds (verify the default in the pinned version; it is config `transport/link/tx/lease`).
|
||||
- Liveliness cannot detect a _hung-but-connected_ server. The client's per-request timeout (`request_timeout_s`) is the authoritative watchdog — this is the structural fix for legacy BUG-3 (no deadlines on `GetActions`).
|
||||
|
||||
### 6.5 Threading constraints (zenoh-python facts that shape both processes)
|
||||
|
||||
- **No asyncio API** in zenoh-python — both client and server are thread-based. This matches the existing RTC engine pattern exactly.
|
||||
- Each callback-based subscriber spawns a dedicated Python thread; **blocking Zenoh calls inside callbacks are disallowed**. Callbacks must be deposit-only (write a slot, set an event, return).
|
||||
- Channel handlers (`FifoChannel`, `RingChannel`) are Rust-side; `try_recv()` polls without spawning Python threads. `RingChannel(1)` is native latest-only semantics.
|
||||
- No zero-copy path for our payloads (SHM API is `@_unstable` and same-host-only; `ZBytes` copy behavior undocumented). At ~200 KB × a few Hz per robot, one memcpy is irrelevant.
|
||||
|
||||
### 6.6 Router deployment
|
||||
|
||||
- `zenohd` official image as a k8s Deployment (1–N replicas; routers mesh and reroute around failures) behind a `LoadBalancer`/`NodePort` Service exposing TLS 7447. No official Helm chart exists — roll-your-own manifests.
|
||||
- `scouting.multicast.enabled: false`; `scouting.gossip.enabled: true`; clients/servers use static `connect.endpoints`.
|
||||
- **Auth**: mTLS per robot (`transport.link.tls` with `enable_mtls`) + router **ACL** keyed on `cert_common_names`: a robot's cert may only `put` to `@lerobot/**/<its-uuid>/obs` and receive on `.../<its-uuid>/action`. Caveat (flagged): ACL config reloads require a router restart — plan cert/ACL changes as rolling router restarts.
|
||||
- Security review input: the third-party Zenoh protocol security analysis (Census Labs, 2025) should be read before exposing 7447 publicly.
|
||||
|
||||
---
|
||||
|
||||
## 7. The Statelessness Boundary (the load-bearing section)
|
||||
|
||||
**Where the network cut goes.** The local RTC pipeline is:
|
||||
|
||||
```
|
||||
obs (robot-processed dict)
|
||||
→ build_dataset_frame(hw_features, obs, "observation") CLIENT (cheap, hardware-coupled)
|
||||
─────────────────────────── network ───────────────────────────
|
||||
→ prepare_observation_for_inference(...) SERVER (policy-coupled, heavy)
|
||||
→ per-session preprocessor(...) SERVER (stateful within the request)
|
||||
→ policy.predict_action_chunk(obs, inference_delay, prefix) SERVER (pure for allowlisted policies)
|
||||
→ per-session postprocessor(...) SERVER (reads state cached at preprocess)
|
||||
─────────────────────────── network ───────────────────────────
|
||||
→ ActionQueue.merge(original, processed, real_delay, idx_before) CLIENT
|
||||
```
|
||||
|
||||
Three consequences:
|
||||
|
||||
1. **The server needs no cross-request state.** `RelativeActionsProcessorStep` writes `_last_state` at preprocess and the postprocessor reads it back _within the same request_. Per-session pipeline instances + one-request-at-a-time-per-session give correctness with zero persistent state.
|
||||
2. **RTC state stays client-side**, exactly where `RTCInferenceEngine` already keeps it. Each request ships: `inference_delay_steps = ceil(L_max/dt)` (from the client `LatencyTracker`, whose samples are full network-inclusive cycle times — RTT compensation falls out for free), `prefix_model = queue.get_left_over()[:H]`, and `prefix_robot = queue.get_processed_left_over()[:H]` (needed for server-side relative-prefix re-anchoring, mirroring `rtc.py:287-305`). The response returns **both** the model-space and robot-space chunks because `merge` needs both. ≤ `execution_horizon × action_dim` float32 each — a few hundred bytes.
|
||||
3. **G9 dies structurally.** No bespoke client resize (`F.interpolate` in legacy `helpers.py`), no client-side normalization. Clients ship native camera resolution; the server's canonical processor path does everything — serve-time preprocessing is byte-identical to train-time.
|
||||
|
||||
**What the server _does_ hold** (and what it means):
|
||||
|
||||
- Per-session processor instances (cheap; normalization stat tensors shared read-only).
|
||||
- Per-session episode counter + stats. Episode reset = reset the session's pipelines, clear its mailbox. **`policy.reset()` is never called in shared mode** — it is global to the shared policy instance and unnecessary for chunk-pure policies (ACT's ensembler and Pi0/SmolVLA's queues live in `select_action`, not `predict_action_chunk` — verified).
|
||||
- Policies that are _not_ chunk-pure get `serving_mode: exclusive` (§8.3).
|
||||
|
||||
---
|
||||
|
||||
## 8. The Inference Server: `lerobot-policy-server`
|
||||
|
||||
New package `src/lerobot/policy_server/`; console script `lerobot-policy-server --manifest manifest.yaml`.
|
||||
|
||||
### 8.1 Process model — **KEPT** from v1, amended
|
||||
|
||||
One process = one model+task on one GPU, loaded and warmed at startup (`warmup_inferences` dummy forwards; covers torch.compile). Multi-GPU nodes run N processes (`CUDA_VISIBLE_DEVICES` pinning). Dynamic model loading (`SendPolicyInstructions`) is **rejected**: pickle/RCE surface, arbitrary-download surface, and it destroys capacity planning. Amendment: `pin_task: false` (default) lets VLA clients set the task per session; `pin_task: true` rejects mismatched tasks at session open.
|
||||
|
||||
### 8.2 Concurrency (pure threads — no asyncio in zenoh-python)
|
||||
|
||||
```
|
||||
zenoh subscriber (.../*/obs) inference worker (1 thread, owns GPU)
|
||||
deposit-only callback: loop:
|
||||
slots[client_uuid] = sample ──► pick next session with pending obs (RR ring)
|
||||
(per-client latest-only) decode JPEG → per-session preprocess
|
||||
predict_action_chunk(delay, prefix)
|
||||
control queryables (status/session/ per-session postprocess → encode
|
||||
reset): validate, mutate session publisher.put(.../<uuid>/action)
|
||||
registry, reply (publishing from the worker thread is fine)
|
||||
```
|
||||
|
||||
- **Per-client latest-only mailbox**: a wildcard subscriber with a deposit-only callback writing per-client slots (scales to dynamic fleets), or — when the manifest enumerates clients — one `RingChannel(1)` subscriber per client polled via `try_recv()`. Either way: newest observation wins; a superseded request is counted (`superseded_seqs` in the next response) so drops are visible. This deletes legacy BUG-4 (`observations_similar` + `must_go`) by construction — the **client** decides when to request; the server never second-guesses observation content.
|
||||
- **Single inference worker**: torch releases the GIL inside `forward`, callbacks stay responsive. Strict round-robin over sessions with pending observations: each gets exactly one inference per cycle; starvation is structurally impossible. Overload degrades into longer cycle times → larger (but correct) client `delay_steps` → eventually the client staleness bound trips and the robot holds — safe by construction.
|
||||
|
||||
### 8.3 Chunk-stateless allowlist and serving modes
|
||||
|
||||
At startup the server classifies the loaded policy:
|
||||
|
||||
| Class | Policies (verified) | Mode |
|
||||
| --------------- | ------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| chunk-stateless | ACT, Pi0, Pi0.5, SmolVLA (and any policy whose `predict_action_chunk` touches no instance state) | `shared`: N sessions, per-session pipelines, `policy.reset()` never called |
|
||||
| chunk-stateful | Diffusion family (`predict_action_chunk` reads `select_action`-fed `self._queues`) | `exclusive`: `max_sessions=1` enforced; episode reset additionally calls `policy.reset()`; second session open → rejected with a self-explanatory error |
|
||||
| no chunk API | SAC, SARM | refused at startup |
|
||||
|
||||
Implemented as a registry in `policy_server/validation.py`; the cleaner follow-up is a `supports_stateless_chunking` class attribute on `PreTrainedPolicy` (needs a pass over policy families — roadmap §14).
|
||||
|
||||
### 8.4 Session open & capability validation (fail fast, fail loud)
|
||||
|
||||
`session` queryable payload: `client_uuid`, `policy_type`, `fps`, feature summary (post-rename observation feature names + shapes, ordered action keys), `schema_version`, RTC intent, `tags`. Checks:
|
||||
|
||||
| Check | Rule | On mismatch |
|
||||
| -------------------------- | --------------------------------------------------------------- | ---------------------------------------------------------------------------------- |
|
||||
| Action names **and order** | must equal server's `action_feature_names` exactly | **hard reject** — this is the sync-safety contract mapping chunk columns to motors |
|
||||
| Camera names | client set must cover `policy.config.input_features` image keys | hard reject |
|
||||
| Resolution | any H×W accepted (server resizes canonically) | warn if aspect ratio differs from training |
|
||||
| State dim | flattened dim must match | hard reject |
|
||||
| `schema_version` | client within server's supported range | hard reject |
|
||||
| fps | vs. manifest `trained_fps` | warn (reject only when `strict_fps: true`) |
|
||||
| Task | when `pin_task: true`, must equal `default_task` | reject |
|
||||
| RTC | client RTC requires policy RTC kwargs support | downgrade to append mode + warning |
|
||||
| Capacity | `active_sessions < max_sessions` | reject with current load → client retries another replica |
|
||||
|
||||
Reply: `session_id`, model info (repo, revision — consider a checkpoint hash, §15), `action_feature_names`, `chunk_size`, `trained_fps`, `supports_rtc`, `serving_mode`, `warmed_up`, `schema_version`, warnings. **rename_map is applied client-side** so the wire format is canonical policy-feature keys across heterogeneous robots (also a prerequisite for future batching).
|
||||
|
||||
### 8.5 Scheduler seam (micro-batching later, not in v1)
|
||||
|
||||
The worker calls a `Scheduler.select(ready: list[Session]) -> list[Session]`; v1 ships `RoundRobin` (`return ready[:1]`). Cross-session batching is blocked on the policy API (`inference_delay` is scalar; batched clients have different delays/prefixes) — when that lands, a `MicroBatch` scheduler groups same-shape sessions. The seam costs nothing now and prevents a redesign later.
|
||||
|
||||
### 8.6 Manifest
|
||||
|
||||
```yaml
|
||||
model:
|
||||
{
|
||||
repo_or_path: lerobot/pi0_towels,
|
||||
revision: main,
|
||||
dtype: bfloat16,
|
||||
device: cuda,
|
||||
}
|
||||
default_task: "fold the towel"
|
||||
pin_task: false
|
||||
serving_mode: shared # forced to exclusive for chunk-stateful policies
|
||||
max_sessions: 5 # from the §P10 formula: Pi0 @150ms, 1 Hz refresh
|
||||
warmup_inferences: 2
|
||||
strict_fps: false
|
||||
zenoh:
|
||||
connect_endpoints: ["tls/router.gpu-cluster.internal:7447"]
|
||||
tls:
|
||||
{
|
||||
connect_certificate: ...,
|
||||
connect_private_key: ...,
|
||||
root_ca_certificate: ...,
|
||||
}
|
||||
health_port: 9100 # HTTP health + Prometheus metrics
|
||||
debug: { capture_dir: null, capture_max: 256 }
|
||||
```
|
||||
|
||||
Draccus dataclass in `policy_server/manifest.py`; YAML via `--manifest`, individual overrides via CLI.
|
||||
|
||||
---
|
||||
|
||||
## 9. The Edge Client: `RemoteInferenceEngine`
|
||||
|
||||
New file `src/lerobot/rollout/inference/remote.py`, registered `@InferenceEngineConfig.register_subclass("remote")`.
|
||||
|
||||
### 9.1 Threading model
|
||||
|
||||
| Thread | Role |
|
||||
| -------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| Main (strategy loop) | `notify_observation(obs)` → lock-protected latest-only slot (identical to `rtc.py` `_obs_holder`). `get_action()` → `ActionQueue.get()` + staleness check. **Never any I/O.** Structurally fixes legacy BUG-1 (blocking send inside the 33 ms loop). |
|
||||
| Network worker (1 daemon thread) | Cycle: wait until `queue_remaining·dt ≤ buffer_time_s` and active → snapshot `idx_before`, prefixes, `delay_steps = ceil(L_max/dt)` → encode (JPEG q=`jpeg_quality`) → `publisher.put(obs, attachment=header)` → await chunk on the action subscriber channel (timeout `request_timeout_s`) → `merge(original, processed, ceil(L/dt), idx_before)` → `latency_tracker.add(L)`. Owns the state machine, reconnects, and control queries. One-in-flight (P5). |
|
||||
| Zenoh action subscriber | `FifoChannel(2)` handler drained by the worker (no Python callback thread on the hot path); liveliness subscriber callback is deposit-only (sets an event). |
|
||||
|
||||
Reused unchanged: `ActionQueue` (`policies/rtc/action_queue.py`), `LatencyTracker`, `ActionInterpolator` (lives in strategies — `interpolation_multiplier` works with remote for free). Deleted concepts: aggregation zoo, `observations_similar`, `must_go`, `TimedObservation`/`TimedAction` pickles.
|
||||
|
||||
### 9.2 Fail-safe state machine
|
||||
|
||||
```
|
||||
ok no chunk for degraded_after_s
|
||||
CONNECTING ─────► STREAMING ───────────────────────────────► DEGRADED
|
||||
│ ▲ ▲ │ queue empty OR max_action_age_s hit │
|
||||
│ │ backoff, │ └───────────────────────────────────► STALLED ◄──┘
|
||||
│ │ re-handshake │ first successful merge │
|
||||
│ └─ RECONNECTING ◄── timeout streak / server liveliness drop ◄─┘
|
||||
│ │ offline > max_offline_s, capability/schema mismatch, auth failure
|
||||
└──────► DEAD (failed=True → shutdown_event → strategy teardown: return-to-initial-pose)
|
||||
```
|
||||
|
||||
- **DEGRADED**: requests failing but the queue still holds actions — the robot keeps executing; chunks _are_ the fault-tolerance buffer (1–3 s of coverage makes blips and clean server drains invisible).
|
||||
- **STALLED**: queue empty or staleness bound hit → apply `fallback`: `hold` (`get_action` → `None`; `send_next_action` already tolerates it), `repeat_last`, or `zero` (required for velocity-controlled robots, where "send nothing" means "keep last velocity").
|
||||
- **Staleness bound** (sync safety): every merge records `(chunk_start_index, t_send)`; `get_action` refuses any action whose source observation is older than `max_action_age_s` (default 3.0 s ≈ 90 steps @ 30 fps). Bounds open-loop execution after a network stall.
|
||||
- **DEAD**: only after `max_offline_s` (default 60 s) or a hard contract violation (capability/schema mismatch on reconnect — e.g. the server restarted with a different model; never execute wrong-model chunks). Uses the exact mechanism RTC uses (`failed=True` + global `shutdown_event`) so existing teardown runs unchanged.
|
||||
- **Watchdog layering**: per-request timeout (hung server — the BUG-3 fix) → server liveliness token (dead server/router) → staleness bound (the robot-side invariant that holds regardless of why data stopped).
|
||||
- **Pause/resume (DAgger)**: `pause()` stops the worker publishing (slot keeps refreshing, ignored); queue intact — parity with `RTCInferenceEngine.pause`. DAgger's existing `interpolator.reset(); engine.reset(); engine.resume()` sequence works unchanged.
|
||||
- **`reset()` (episode boundary)**: clear `ActionQueue` + staleness bookkeeping, bump `episode_id`, fire the acked `reset` query (1 s timeout, failure logged — the server has nothing it _must_ do thanks to per-request statelessness), flag `episode_start` on the next observation. `LatencyTracker` intentionally survives reset (latency is episode-invariant; parity with local RTC).
|
||||
- **`ready`** = session opened ∧ capabilities validated ∧ server `warmed_up`. First-chunk gating is implicit (`get_action` → `None` until the first merge).
|
||||
|
||||
### 9.3 Weightless client — exact integration changes
|
||||
|
||||
- `rollout/context.py`: `PolicyContext.{policy, preprocessor, postprocessor}` become `| None`. For remote configs, skip step 1 (weight load / PEFT / `.to(device)` / torch.compile / `init_rtc_processor`) and step 6 (`make_pre_post_processors`). Verified safe: strategies only consume `ctx.policy.inference`. Keep steps 2–5 (robot processors, hardware, features, dataset) — they are robot-derived. Keep the visual pre-flight check (`context.py:309-324`): `--policy.path` already loads config-only (`rollout/configs.py:324-328`, no weight download) and failing before dialing the server is free. `use_torch_compile` / explicit `--device` → warn-and-ignore for remote.
|
||||
- `rollout/inference/factory.py`: signature loosens to `policy: PreTrainedPolicy | None` (+ `policy_config: PreTrainedConfig`); `sync`/`rtc` branches guard `policy is None`; the `remote` branch lazy-imports (`eclipse-zenoh` stays an optional extra).
|
||||
- The authoritative validation moves to session open (§8.4); the local check becomes a fast-fail convenience.
|
||||
|
||||
### 9.4 Config
|
||||
|
||||
```python
|
||||
@InferenceEngineConfig.register_subclass("remote")
|
||||
@dataclass
|
||||
class RemoteInferenceConfig(InferenceEngineConfig):
|
||||
connect_endpoint: str = "tls/localhost:7447" # zenoh router endpoint
|
||||
tls_cert: str | None = None; tls_key: str | None = None; tls_ca: str | None = None
|
||||
client_uuid: str = "" # "" → uuid4 at start()
|
||||
jpeg_quality: int = 90 # 0 = raw (LAN/debug)
|
||||
buffer_time_s: float = 0.5 # send next obs when queue playback ≤ this (v1 G14) — KEPT
|
||||
max_action_age_s: float = 3.0 # staleness bound (safety)
|
||||
degraded_after_s: float = 1.0
|
||||
request_timeout_s: float = 5.0
|
||||
reconnect_initial_backoff_s: float = 0.5
|
||||
reconnect_max_backoff_s: float = 10.0
|
||||
max_offline_s: float = 60.0
|
||||
fallback: FallbackBehavior = FallbackBehavior.HOLD # hold | repeat_last | zero
|
||||
rtc: RTCConfig = field(default_factory=RTCConfig) # enabled → replace mode; horizon caps prefix
|
||||
tags: dict[str, str] = field(default_factory=dict) # ex-cluster/experiment labels
|
||||
```
|
||||
|
||||
```bash
|
||||
# Remote RTC + sentry recording (the reproducibility path)
|
||||
lerobot-rollout \
|
||||
--strategy.type=sentry \
|
||||
--policy.path=lerobot/pi0_towels \ # config-only: no weights downloaded
|
||||
--inference.type=remote \
|
||||
--inference.connect_endpoint=tls/router.gpu-cluster.internal:7447 \
|
||||
--inference.rtc.execution_horizon=10 \
|
||||
--robot.type=so100_follower --robot.port=/dev/ttyACM0 \
|
||||
--robot.cameras="{front: {type: opencv, index_or_path: 0, width: 640, height: 480, fps: 30}}" \
|
||||
--dataset.repo_id=user/rollout_fleet_a --dataset.single_task="fold the towel"
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 10. Wire Schema
|
||||
|
||||
### 10.1 Payload anatomy & rates — **KEPT** (JPEG) with numbers
|
||||
|
||||
Upstream per request: joints (24–128 B) + JPEG frames (480p q90 ≈ 40–90 KB each; 720p ≈ 110–230 KB) + RTC prefixes (≤ a few KB) → 60–450 KB depending on cameras. Downstream: `2 × chunk_size × action_dim × 4 B` + metadata → 3–50 KB. Effective request rate is self-clocked by `buffer_time_s` to ~1–4 Hz per robot (not the 30 Hz control rate). 300 robots ≈ 0.3–10 Mbps each — the wire is never the bottleneck; bandwidth budgeting is about camera count/resolution, and each GPU pod only ever sees its own ≤ `max_sessions` clients. Zenoh fragments >64 KiB payloads transparently; multi-MB messages are fine.
|
||||
|
||||
### 10.2 Attachment header (fixed-layout, packed little-endian — parsed without touching the body)
|
||||
|
||||
| Field | Type | Notes |
|
||||
| ---------------- | ---- | -------------------------------------------------------------- |
|
||||
| `schema_version` | u16 | negotiated at session open |
|
||||
| `msg_type` | u8 | OBS / CHUNK / EVENT |
|
||||
| `seq_id` | u64 | per-session monotonic; echoed in the chunk |
|
||||
| `episode_id` | u32 | bumped by `reset()` |
|
||||
| `client_mono_ns` | i64 | client `monotonic_ns()`; **opaque to the server, echoed back** |
|
||||
| `session_epoch` | u32 | bumped per (re)connect; stale-epoch chunks dropped |
|
||||
|
||||
### 10.3 msgpack bodies
|
||||
|
||||
**ObservationMsg** (client → server): `state: {names_ref, data: f32 LE bytes}`, `images: {name: {codec: jpeg|raw, bytes, (h,w,c) if raw}}`, `task: str`, `inference_delay_steps: int`, `prefix_model: tensor?`, `prefix_robot: tensor?` (tensors = raw LE bytes + dtype + shape), `episode_start: bool`.
|
||||
**ActionChunkMsg** (server → client): `seq_id_echo`, `client_mono_ns_echo`, `chunk_model: tensor`, `chunk_robot: tensor`, `queue_wait_ms: f32`, `inference_ms: f32`, `superseded_seqs: u32`, `server_load: f32`.
|
||||
**Status / SessionOpen / SessionAck / ResetMsg**: as specified in §8.4.
|
||||
|
||||
### 10.4 Schema discipline (P7)
|
||||
|
||||
`schema_version` gates at handshake; evolution is additive-only (new optional msgpack keys; unknown keys ignored); attachment layout changes require a version bump; golden codec round-trip tests (tensor exactness, JPEG RGB-channel-order regression — a silent BGR swap poisons every VLA in the fleet) are part of the test suite. **No pickle anywhere** — KEPT from v1 and now structural: nothing in the schema can carry code.
|
||||
|
||||
---
|
||||
|
||||
## 11. Latency Budget & the Clock Iron Rule
|
||||
|
||||
| Stage | LAN | WAN (50 ms RTT) |
|
||||
| ------------------------------ | --------------- | --------------- |
|
||||
| JPEG encode ×3 (edge CPU) | 2–9 ms | 2–9 ms |
|
||||
| Serialize | <1 ms | <1 ms |
|
||||
| Uplink (tx + ½RTT) | ~2 ms | ~54 ms |
|
||||
| Server queue wait | 0 → 1×inference | 0 → 1×inference |
|
||||
| Decode + canonical preprocess | 4–10 ms | 4–10 ms |
|
||||
| **Inference** | **15–150 ms** | **15–150 ms** |
|
||||
| Postprocess + downlink + merge | ~2 ms | ~27 ms |
|
||||
| **Total (Pi0-class)** | **~110–175 ms** | **~190–250 ms** |
|
||||
|
||||
Inference is 60–85 % of end-to-end on LAN; the entire transport+serialization stack is <10 ms. WAN adds propagation + uplink bandwidth — identical under any transport. At 30 fps this lands `delay_steps` ≈ 4–8, comfortably inside RTC execution horizons: WAN degrades smoothness parameters, never correctness. _This table is the standing answer to transport-performance bikeshedding._
|
||||
|
||||
**Clock iron rule** (P4): wall-clock instants never cross machines. Client stamps `monotonic_ns`, the server echoes it opaquely; `RTT = now − echo`. The server reports only **durations** (`queue_wait_ms`, `inference_ms`) measured on its own monotonic clock; `network_time = RTT − queue_wait − inference` for diagnostics. The schema has no field in which a foreign wall-clock instant can be compared — the legacy `time.time()` bug is unrepresentable.
|
||||
|
||||
---
|
||||
|
||||
## 12. Reproducibility & Audit (P8)
|
||||
|
||||
The contract is **fully logged + replayable**, not "deterministic":
|
||||
|
||||
- **Client = source of truth.** Recording strategies already persist observations + executed actions to `LeRobotDataset`. The remote engine logs, per executed action, the `(session_id, seq_id, episode_id)` of its source chunk plus the echoed `queue_wait_ms`/`inference_ms` (dataset-extras columns are a follow-up; client logs in v1).
|
||||
- **Server audit line per request** (structured JSON): `{ts, session_id, client_uuid, seq_id, episode_id, queue_wait_ms, inference_ms, chunk_range, superseded_seqs, outcome}`.
|
||||
- **Optional bounded capture**: `debug.capture_dir` writes a ring of request/response pairs (safetensors) for byte-exact offline replay through the same server pipeline.
|
||||
- **Runbook — "robot #217 stuttered at 14:03"**: (1) Grafana `session_staleness{client="217"}` — spike ⇒ server side, flat ⇒ client/network. (2) Server side: audit lines — `queue_wait_ms` rising across _all_ sessions ⇒ overloaded replica (check `active_sessions` vs `max_sessions`); `superseded_seqs` streak on 217 only ⇒ that client over-requesting; `outcome=error` ⇒ adjacent stack trace. (3) Client side: state-machine transitions + reconnects in the client log; dataset rows show which seq's chunk was executing and where `None` ticks occurred. Every hop shares `(session_id, seq_id)` — the join is mechanical.
|
||||
|
||||
---
|
||||
|
||||
## 13. Integration & Migration Plan
|
||||
|
||||
### 13.1 New
|
||||
|
||||
| Path | Content |
|
||||
| --------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| `src/lerobot/policy_server/{__init__,schema,codec,manifest,session,scheduler,validation,server}.py` | wire schema constants, msgpack/attachment codecs, manifest dataclasses, `Session` + mailbox, `Scheduler` seam, capability rules + chunk-stateless registry, zenoh servicer + inference worker + drain + HTTP health/metrics |
|
||||
| `src/lerobot/rollout/inference/remote.py` | `RemoteInferenceEngine` (~600 lines; mirrors `rtc.py` structure) |
|
||||
| `src/lerobot/scripts/lerobot_policy_server.py` + `[project.scripts]` entry | thin `main()` |
|
||||
| `docker/Dockerfile.policy-server` | CUDA runtime base + uv; manifest via ConfigMap |
|
||||
| `docs/source/remote_inference.mdx` (+ `_toctree.yml`) | replaces `async.mdx` |
|
||||
|
||||
### 13.2 Modified
|
||||
|
||||
`rollout/inference/factory.py` (config + Optional-typed signature + lazy import) · `rollout/context.py` (weightless branch) · `rollout/inference/__init__.py` · `scripts/lerobot_rollout.py` docstring · `pyproject.toml`: `[async]` extra becomes `eclipse-zenoh>=1.9,<2.0` + `msgpack` (grpcio/matplotlib leave it; grpcio remains under `[hilserl]`/`dev` for the RL stack).
|
||||
|
||||
### 13.3 Removed — same landing PR
|
||||
|
||||
`src/lerobot/async_inference/` · `tests/async_inference/` · `docs/source/async.mdx` + its `_toctree.yml` entry · the `AsyncInference` service + `Observation`/`Actions`/`PolicySetup` messages from `src/lerobot/transport/services.proto` (regenerate pb2; **`LearnerService` untouched** — `transport/` is shared with HIL-SERL (`src/lerobot/rl/`); the RL test suite gates this change).
|
||||
|
||||
### 13.4 Legacy config → successor mapping
|
||||
|
||||
| Legacy (`RobotClientConfig`/`PolicyServerConfig`) | Successor |
|
||||
| ------------------------------------------------- | ---------------------------------------------------------- |
|
||||
| `server_address` | `--inference.connect_endpoint` (zenoh router) |
|
||||
| `policy_type`, `pretrained_name_or_path` | `--policy.path` (config-only) + server manifest |
|
||||
| `chunk_size_threshold` (0–1 ratio) | `--inference.buffer_time_s` (seconds) |
|
||||
| `actions_per_chunk` | server manifest (validated at session open) |
|
||||
| `aggregate_fn_name` + `AGGREGATE_FUNCTIONS` | **dropped** — `ActionQueue` replace/append |
|
||||
| `policy_device`, `client_device` | **dropped** — server concern / chunks arrive CPU f32 |
|
||||
| `debug_visualize_queue_size` | **dropped** — Rerun (`--display_data`) + engine stats |
|
||||
| `PolicyServerConfig.{host,port}` | manifest `zenoh.connect_endpoints` |
|
||||
| `inference_latency`, `obs_queue_timeout` | **dropped** — latency client-measured; no server obs queue |
|
||||
| `SendPolicyInstructions` | **dropped** — MaaS manifest + session validation |
|
||||
| `observations_similar` / `must_go` | **dropped** — latest-only slots + client send gate |
|
||||
| pickle envelopes | **dropped** — msgpack + attachment headers |
|
||||
|
||||
### 13.5 Legacy bugs/gaps → structural resolution
|
||||
|
||||
BUG-1 → worker thread owns all I/O. BUG-2 → aggregation deleted; `ActionQueue` is internally locked. BUG-3 → per-request timeout + liveliness. BUG-4 → client-side send gating; server newest-wins. G1 → per-session registry. G2 → manifest. G4 → msgpack+attachments. G5 → monotonic echo + `delay_steps`. G7 → recording strategies. G8 → mTLS + ACL. G9 → server-side canonical processors. G11 → `status` queryable. G12 → Prometheus + audit logs. G13 → `lerobot-policy-server` console script. G14 → `buffer_time_s`.
|
||||
|
||||
### 13.6 Tests
|
||||
|
||||
- **Unit**: codec round-trips (tensor exact; JPEG RGB-order regression), capability-validation matrix (§8.4 as parametrized cases), scheduler fairness + newest-wins supersession (mock policy with configurable sleep), manifest parsing, key-expr sanitization.
|
||||
- **Loopback integration** (CPU, fast CI): client+server in one process over zenoh peer-to-peer (or a localhost `zenohd` started by the fixture), tiny-ACT, fake 2-camera robot, N=8 concurrent sessions. The headline regression: two sessions with different joint states must not cross-contaminate `RelativeActionsProcessorStep` postprocessing — the test that proves the multi-tenancy claim.
|
||||
- **Chaos**: kill the server mid-episode → client returns `None`, never raises into the control loop, `failed` stays False within `max_offline_s`, resumes on restart; `docker kill zenohd` → liveliness flap → safe state → re-handshake (explicitly tests re-declaration behavior, flagged unverified upstream); SIGTERM drain → in-flight chunk completes, clients reconnect invisibly.
|
||||
- **Golden parity**: remote RTC vs local `RTCInferenceEngine` on identical observation sequences → byte-identical merged queues (the re-anchoring contract test). Gate for any real-robot remote-RTC use.
|
||||
|
||||
---
|
||||
|
||||
## 14. Roadmap
|
||||
|
||||
1. **PR1 — schema & codecs** (no torch deps): `policy_server/{schema,codec,manifest}.py`, key-expr sanitizer, golden codec tests.
|
||||
2. **PR2 — server core**: session registry, scheduler, validation/allowlist, inference worker with mock policy, loopback harness.
|
||||
3. **PR3 — client engine**: `RemoteInferenceEngine`, factory/context weightless integration, loopback integration + chaos + golden-parity tests.
|
||||
4. **PR4 — ops & docs**: Dockerfile, health/metrics, drain, ACL examples, `remote_inference.mdx`, rollout docstring.
|
||||
5. **Landing PR — legacy deletion**: remove `async_inference/` + tests + docs + proto service (RL suite gates), `[async]` extra swap.
|
||||
6. **Pre-release field validation**: one real robot on a lossy network (watchdog default tuning); JPEG q90 vs raw A/B on one policy (train/serve shift).
|
||||
7. **Future**: micro-batching (needs per-sample `inference_delay` across policy families), client-side downscale-to-policy-resolution (config-only shapes make it possible), Advanced Pub/Sub on the action topic, per-robot quotas, dataset provenance columns, `supports_stateless_chunking` attribute upstreamed to policy classes.
|
||||
|
||||
---
|
||||
|
||||
## 15. Open Risks
|
||||
|
||||
| Risk | Mitigation / decision needed |
|
||||
| ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| Re-anchoring parity (server-side relative-prefix re-anchor vs `rtc.py`) | Golden parity test (§13.6) is a hard gate before robot use; likely failure mode is normalizer dtype/device drift |
|
||||
| First-chunk over-trim when idle: `merge` trims `ceil(L/dt)` even when nothing was consumed (queue empty at episode start) — wasteful at network latencies (600 ms ⇒ 18 steps) | Proposed clamp `real_delay = min(real_delay, last_index - idx_before)` touches the shared `ActionQueue` used by local RTC — needs sign-off + regression tests |
|
||||
| JPEG train/serve distribution shift | Unmeasured; A/B before locking q90 default (roadmap §14.6) |
|
||||
| Watchdog defaults untuned (`request_timeout_s=5`, `degraded_after_s=1`, `max_action_age_s=3`) | Field validation on wired and Wi-Fi; consider named profiles |
|
||||
| Capability check can pass while semantics differ (different finetune, different normalization stats, identical feature names) | Add checkpoint hash/revision pinning to SessionAck — decide in PR2 |
|
||||
| zenoh-python long-session maturity: re-declaration after router restart partially verified; SHM unstable; no asyncio | Chaos tests own this; thread-based design avoids the asyncio gap entirely |
|
||||
| Router ACL reload requires restart | Operational runbook: cert/ACL changes = rolling router restart |
|
||||
| `fallback=zero` has no consumer until velocity actions land in rollout (only `.pos` features routed today) | Validate the enum against robot capabilities when velocity support lands |
|
||||
| Per-client mailbox memory under fleet-scale wildcard subscription | One decoded-obs slot per client is small; add an LRU GC tied to liveliness drops |
|
||||
@@ -0,0 +1,82 @@
|
||||
# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
# This Dockerfile builds a GPU inference pod for `lerobot-policy-server`
|
||||
# (remote inference over Zenoh). It starts from an NVIDIA CUDA base image;
|
||||
# the cu128 PyTorch wheels bundle their own CUDA runtime (driver floor 570.86,
|
||||
# see pyproject.toml [tool.uv]).
|
||||
|
||||
# docker build -f docker/Dockerfile.policy-server -t lerobot-policy-server .
|
||||
# docker run --gpus all -v ./server.yaml:/etc/lerobot/server.yaml lerobot-policy-server
|
||||
#
|
||||
# Extra policy-family dependencies (e.g. pi0/smolvla need transformers) can be
|
||||
# added at build time:
|
||||
# docker build -f docker/Dockerfile.policy-server \
|
||||
# --build-arg LEROBOT_EXTRAS="async pi0" -t lerobot-policy-server .
|
||||
|
||||
# Configure the base image (same CUDA family as Dockerfile.internal)
|
||||
ARG CUDA_VERSION=12.8.1
|
||||
ARG OS_VERSION=24.04
|
||||
FROM nvidia/cuda:${CUDA_VERSION}-base-ubuntu${OS_VERSION}
|
||||
|
||||
# Define Python version and lerobot extras arguments
|
||||
ARG PYTHON_VERSION=3.12
|
||||
ARG LEROBOT_EXTRAS="async"
|
||||
|
||||
# Configure environment variables
|
||||
ENV DEBIAN_FRONTEND=noninteractive \
|
||||
PATH=/lerobot/.venv/bin:$PATH
|
||||
|
||||
# Install system dependencies and uv (as root).
|
||||
# Kept lean: no hardware/teleop libraries — this image only serves policies.
|
||||
RUN apt-get update && apt-get install -y --no-install-recommends \
|
||||
git curl ca-certificates libglib2.0-0 ffmpeg \
|
||||
&& curl -LsSf https://astral.sh/uv/install.sh | sh \
|
||||
&& mv /root/.local/bin/uv /usr/local/bin/uv \
|
||||
&& useradd --create-home --shell /bin/bash user_lerobot \
|
||||
&& apt-get clean && rm -rf /var/lib/apt/lists/*
|
||||
|
||||
# Create application directory and set permissions
|
||||
WORKDIR /lerobot
|
||||
RUN chown -R user_lerobot:user_lerobot /lerobot
|
||||
|
||||
# Switch to the non-root user
|
||||
USER user_lerobot
|
||||
|
||||
# Model checkpoints are cached under HF_HOME — mount it as a volume
|
||||
# (or a PVC in Kubernetes) so warm restarts skip the Hub download.
|
||||
ENV HOME=/home/user_lerobot \
|
||||
HF_HOME=/home/user_lerobot/.cache/huggingface \
|
||||
HF_LEROBOT_HOME=/home/user_lerobot/.cache/huggingface/lerobot \
|
||||
TORCH_HOME=/home/user_lerobot/.cache/torch \
|
||||
TRITON_CACHE_DIR=/home/user_lerobot/.cache/triton
|
||||
|
||||
# Create the virtual environment (Python provisioned by uv)
|
||||
RUN uv venv --python ${PYTHON_VERSION}
|
||||
|
||||
# Install lerobot from the build context with the async extra
|
||||
# (eclipse-zenoh + msgpack — see pyproject.toml [project.optional-dependencies])
|
||||
COPY --chown=user_lerobot:user_lerobot setup.py pyproject.toml uv.lock README.md MANIFEST.in ./
|
||||
COPY --chown=user_lerobot:user_lerobot src/ src/
|
||||
|
||||
RUN uv sync --locked --no-cache $(printf -- '--extra %s ' ${LEROBOT_EXTRAS})
|
||||
|
||||
# HTTP health + Prometheus metrics (manifest `health_port`, 0 disables)
|
||||
EXPOSE 9100
|
||||
|
||||
# The manifest is typically mounted as a ConfigMap (Kubernetes) or a bind
|
||||
# mount (docker run -v) at /etc/lerobot/server.yaml; any field can also be
|
||||
# overridden on the command line, e.g. --model.repo_or_path=lerobot/pi0_towels
|
||||
ENTRYPOINT ["lerobot-policy-server"]
|
||||
CMD ["--manifest", "/etc/lerobot/server.yaml"]
|
||||
@@ -45,8 +45,6 @@
|
||||
title: Language Columns and Recipes
|
||||
- local: tools
|
||||
title: Tools
|
||||
- local: annotation_pipeline
|
||||
title: Annotation Pipeline
|
||||
- local: video_encoding_parameters
|
||||
title: Video encoding parameters
|
||||
- local: streaming_video_encoding
|
||||
@@ -69,8 +67,6 @@
|
||||
title: VLA-JEPA
|
||||
- local: eo1
|
||||
title: EO-1
|
||||
- local: fastwam
|
||||
title: FastWAM
|
||||
- local: groot
|
||||
title: NVIDIA GR00T N1.5
|
||||
- local: xvla
|
||||
@@ -91,8 +87,8 @@
|
||||
- sections:
|
||||
- local: inference
|
||||
title: Policy Deployment (lerobot-rollout)
|
||||
- local: async
|
||||
title: Use Async Inference
|
||||
- local: remote_inference
|
||||
title: Remote Inference (lerobot-policy-server)
|
||||
- local: rtc
|
||||
title: Real-Time Chunking (RTC)
|
||||
title: "Inference"
|
||||
|
||||
@@ -1,291 +0,0 @@
|
||||
# Annotation Pipeline
|
||||
|
||||
`lerobot-annotate` watches each episode's video with a vision-language
|
||||
model (VLM) and writes natural-language annotations back into your
|
||||
dataset. It fills the two language columns from the
|
||||
[Language Columns and Recipes](./language_and_recipes) page —
|
||||
`language_persistent` and `language_events` — straight into
|
||||
`data/chunk-*/file-*.parquet`.
|
||||
|
||||
In short: point it at a LeRobot dataset, and it adds subtasks, plans,
|
||||
memory, interjections, speech, and visual Q&A that a policy can be
|
||||
trained on.
|
||||
|
||||
## How it fits together
|
||||
|
||||
```text
|
||||
your dataset lerobot-annotate
|
||||
(LeRobot v3.1)
|
||||
│
|
||||
▼
|
||||
┌─────────────────────────────────────────────────────┐
|
||||
│ read episodes │
|
||||
└──────────────────────────┬──────────────────────────┘
|
||||
│
|
||||
┌────────────────────┼────────────────────┐
|
||||
▼ ▼ ▼
|
||||
┌──────────┐ ┌───────────────┐ ┌──────────┐ one shared Qwen-VL
|
||||
│ plan │ │ interjections │ │ vqa │ ◀── server (vLLM, OpenAI
|
||||
└────┬─────┘ └───────┬───────┘ └────┬─────┘ API) drives all three
|
||||
└────────────────────┼─────────────────────┘
|
||||
│ each module stages raw JSONL
|
||||
▼ into .annotate_staging/
|
||||
┌─────────────────┐
|
||||
│ validator │ ◀── checks everything
|
||||
└────────┬────────┘
|
||||
▼
|
||||
┌─────────────────┐
|
||||
│ writer │
|
||||
└────────┬────────┘
|
||||
▼
|
||||
data/chunk-*/file-*.parquet
|
||||
(+ meta/info.json tools)
|
||||
```
|
||||
|
||||
Three modules (`plan`, `interjections`, `vqa`) all talk to **one** shared
|
||||
VLM. Each module stages its output to disk, a validator checks it, and a
|
||||
single writer rewrites the dataset shards in place.
|
||||
|
||||
## What the pipeline produces
|
||||
|
||||
Each module emits a few kinds of annotation ("styles"), routed to one of
|
||||
the two language columns:
|
||||
|
||||
| Style / atom | Column | Module |
|
||||
| ------------------------------------------- | --------------------- | --------------- |
|
||||
| `subtask` (Pi0.7-style "how, not what") | `language_persistent` | `plan` |
|
||||
| `plan` (initial + refresh on interjection) | `language_persistent` | `plan` |
|
||||
| `memory` (MEM-style compression) | `language_persistent` | `plan` |
|
||||
| `task_aug` (rephrasings of the task) | `language_persistent` | `plan` |
|
||||
| `interjection` | `language_events` | `interjections` |
|
||||
| speech tool-call atom (`style=null`, `say`) | `language_events` | `interjections` |
|
||||
| `vqa` (user / assistant pair) | `language_events` | `vqa` |
|
||||
|
||||
### How subtasks are generated
|
||||
|
||||
The `plan` module doesn't ask the VLM for subtasks in one shot. Instead
|
||||
it uses a two-step **describe → segment** flow:
|
||||
|
||||
1. **Describe** — the VLM narrates only what it actually sees in the
|
||||
chosen camera (no guessing about the task).
|
||||
2. **Segment** — that description is fed back in, and the VLM splits the
|
||||
episode into consecutive atomic subtasks.
|
||||
|
||||
Both passes see the episode as **timestamped contact sheets** — frames
|
||||
sampled at `frames_per_second` (0.5s by default) and packed into JPEG
|
||||
grids with each frame's time burned into its corner, so the VLM cites
|
||||
exact boundary times directly. This is far cheaper in vision tokens than
|
||||
one image per frame, so the sampling can stay dense; episodes longer than
|
||||
`max_frames_per_prompt` are split into windows at the same density and
|
||||
merged. Both prompts also carry a causal **event-boundary** definition (a
|
||||
new event starts when an object becomes held / is released / reaches a new
|
||||
location / a lid changes state / contents move) to sharpen where cuts land.
|
||||
|
||||
The resulting spans are then stitched into a gap-free, full-episode
|
||||
cover, so **every frame has exactly one active subtask**. See
|
||||
[`run_hf_job.py`](https://github.com/huggingface/lerobot/blob/main/examples/annotations/run_hf_job.py)
|
||||
for the production settings (single camera, timestamped contact sheets,
|
||||
auto-windowed subtask generation).
|
||||
|
||||
### Tools
|
||||
|
||||
The writer does **not** add a `tools` column to the parquet. The tool
|
||||
catalog lives in `meta/info.json["tools"]` instead (see [Tools](./tools)).
|
||||
After every run, the pipeline makes sure the canonical `say` schema is in
|
||||
that list, keeping any tools you declared beforehand.
|
||||
|
||||
Want to add your own tool? Edit `meta/info.json["tools"]` directly — the
|
||||
pipeline preserves whatever is already there. That makes the tool visible
|
||||
to the chat template, so the model can learn to _generate_ the call. The
|
||||
runtime layer that actually _executes_ a generated call (the `Tool`
|
||||
protocol / `TOOL_REGISTRY` under `src/lerobot/tools/`) is not part of
|
||||
this PR — the [Tools](./tools) doc marks those pieces as
|
||||
not-yet-implemented.
|
||||
|
||||
## Running on Hugging Face Jobs
|
||||
|
||||
Annotation runs on [Hugging Face Jobs](https://huggingface.co/docs/hub/en/jobs).
|
||||
The repo ships a launcher script you copy and tweak for your dataset:
|
||||
|
||||
```bash
|
||||
HF_TOKEN=hf_... uv run python examples/annotations/run_hf_job.py
|
||||
```
|
||||
|
||||
[`run_hf_job.py`](https://github.com/huggingface/lerobot/blob/main/examples/annotations/run_hf_job.py)
|
||||
starts a single-GPU `h200` job (bump it to `h200x4` for big datasets)
|
||||
that:
|
||||
|
||||
1. installs `lerobot` (from `main`) plus the annotation extras,
|
||||
2. boots one vLLM server per GPU (using the `vllm/vllm-openai` image) and
|
||||
drives it over the OpenAI-compatible API,
|
||||
3. runs the `plan` / `interjections` / `vqa` modules across the dataset
|
||||
with `lerobot-annotate`,
|
||||
4. with `--push_to_hub=true`, uploads the result to `--new_repo_id` (or
|
||||
back to `--repo_id` in place if you leave that unset).
|
||||
|
||||
To use a different dataset, model, or hub repo, edit the `CMD` block in
|
||||
the script. Every flag there maps directly to a `lerobot-annotate` flag
|
||||
(run `lerobot-annotate --help` for the full list).
|
||||
|
||||
## Key options
|
||||
|
||||
These are the flags you'll reach for most often. Run
|
||||
`lerobot-annotate --help` for everything else; the defaults are tuned for
|
||||
short manipulation episodes.
|
||||
|
||||
### Dataset in / out
|
||||
|
||||
| Flag | Default | What it does |
|
||||
| ----------------- | ------- | ----------------------------------------------------------------------- |
|
||||
| `--repo_id` | — | Hub dataset to annotate (downloaded if `--root` unset). |
|
||||
| `--root` | — | Annotate a local dataset directory instead. |
|
||||
| `--new_repo_id` | — | Push the result to a new repo (leaves the source repo untouched). |
|
||||
| `--push_to_hub` | `false` | Upload after annotating (to `--new_repo_id`, else back to `--repo_id`). |
|
||||
| `--only_episodes` | all | Annotate just these episode indices (handy for a test run). |
|
||||
| `--seed` | `1729` | Seeds the RNGs that pick interjection timestamps + VQA question types. |
|
||||
|
||||
### Which modules run
|
||||
|
||||
Every module is on by default and can be toggled independently (set to
|
||||
`false` to skip it, e.g. to iterate on one module at a time):
|
||||
|
||||
| Flag | Default | Turns off |
|
||||
| ------------------------- | ------- | ----------------------------------- |
|
||||
| `--plan.enabled` | `true` | subtasks + plan + memory + task_aug |
|
||||
| `--interjections.enabled` | `true` | interjections + speech atoms |
|
||||
| `--vqa.enabled` | `true` | the VQA pairs |
|
||||
|
||||
### The VLM (`--vlm.*`)
|
||||
|
||||
| Flag | Default | What it does |
|
||||
| -------------------------- | ------------------ | ----------------------------------------------------------------------------------- |
|
||||
| `--vlm.model_id` | `Qwen/Qwen3.6-27B` | The model to serve and prompt. |
|
||||
| `--vlm.camera_key` | first `images.*` | Which camera every prompt is grounded on. |
|
||||
| `--vlm.serve_command` | auto | The exact `vllm serve …` command (set TP size, GPU memory, `--max-model-len` here). |
|
||||
| `--vlm.parallel_servers` | `1` | Independent servers for round-robin routing (one per GPU). |
|
||||
| `--vlm.num_gpus` | `0` | GPUs per server (`0` = one each). |
|
||||
| `--vlm.client_concurrency` | `16` | In-flight requests across all servers. |
|
||||
| `--vlm.max_new_tokens` | `512` | Generation cap per call. |
|
||||
| `--vlm.temperature` | `0.2` | Sampling temperature. |
|
||||
|
||||
### Subtasks / plan / memory (`--plan.*`)
|
||||
|
||||
| Flag | Default | What it does |
|
||||
| ------------------------------- | ---------- | ------------------------------------------------------------------------------------------------------------------------- |
|
||||
| `--plan.frames_per_second` | `2.0` | Frame sampling rate for the contact sheets (`2.0` = one frame every 0.5s). |
|
||||
| `--plan.max_frames_per_prompt` | `60` | Frame budget per VLM call. Episodes whose sampling exceeds this are auto-windowed at the same density, then stitched. |
|
||||
| `--plan.contact_sheet_columns` | `5` | Columns per contact-sheet grid (`contact_sheet_frames_per_sheet` tiles, time row-major). |
|
||||
| `--plan.plan_max_steps` | `8` | Upper bound on subtasks per episode. |
|
||||
| `--plan.subtask_describe_first` | `true` | Run the describe→segment grounding pass (best subtask quality; +1 call/episode). |
|
||||
| `--plan.emit_plan` | `true` | Emit the numbered `plan` rows (`false` = subtasks + memory only). |
|
||||
| `--plan.emit_memory` | `true` | Emit the `memory` rows (`false` = subtasks + plan only); symmetric to `emit_plan`. |
|
||||
| `--plan.n_task_rephrasings` | `10` | How many `task_aug` rephrasings to emit (`0` disables). |
|
||||
| `--plan.derive_task_from_video` | `if_short` | Use the dataset task as-is (`off`), only when it's missing/short (`if_short`), or always re-derive from video (`always`). |
|
||||
|
||||
### Interjections + VQA
|
||||
|
||||
| Flag | Default | What it does |
|
||||
| ----------------------------------------------- | ------- | ---------------------------------------------------------- |
|
||||
| `--interjections.max_interjections_per_episode` | `3` | Cap on interjection/speech pairs per episode. |
|
||||
| `--vqa.vqa_emission_hz` | `1.0` | How often VQA pairs are emitted. |
|
||||
| `--vqa.restrict_to_default_camera` | `false` | Ground VQA only on `--vlm.camera_key` (else every camera). |
|
||||
| `--executor.episode_parallelism` | `16` | Episodes processed concurrently within each phase. |
|
||||
|
||||
## Contributing new modules
|
||||
|
||||
The pipeline is built to grow, and **contributions are very welcome** —
|
||||
a brand-new module (say, trajectory traces or affordances), a new prompt
|
||||
template, a smarter grounding flow, or quality fixes to the existing
|
||||
`plan` / `interjections` / `vqa` modules.
|
||||
|
||||
Every module lives under
|
||||
`src/lerobot/annotations/steerable_pipeline/modules/`, shares the VLM
|
||||
client and the keyframe cache, writes its raw output to the staging
|
||||
tree, and plugs into the executor as its own phase. Got an idea? Open an
|
||||
issue or PR on [the repo](https://github.com/huggingface/lerobot).
|
||||
|
||||
## How recipes consume the output
|
||||
|
||||
The annotations are meant to be read by recipes (see
|
||||
[Language Columns and Recipes](./language_and_recipes)). Typically:
|
||||
|
||||
- low-level / high-level / memory-update branches read
|
||||
`subtask` / `plan` / `memory` from `language_persistent`.
|
||||
- an interjection-response branch reads `interjection` events plus the
|
||||
paired speech atom (merged into one assistant turn via `tool_calls_from`)
|
||||
and the matching `plan` refresh at the same timestamp.
|
||||
- a VQA branch reads the `(vqa, user)` and `(vqa, assistant)` pairs from
|
||||
`language_events`.
|
||||
|
||||
## Why state and events are split
|
||||
|
||||
Two ideas shape the design:
|
||||
|
||||
1. **Persistent state vs. exact events.** Persistent rows (`subtask`,
|
||||
`plan`, `memory`) apply to the whole episode and answer "what's true
|
||||
right now?". Event rows (`interjection`, `vqa`, speech) appear only on
|
||||
the one frame whose timestamp matches. Timestamps are copied straight
|
||||
from the source parquet — never recomputed in floating point.
|
||||
2. **One VLM pass.** All three modules share a single VLM client (the
|
||||
OpenAI-compatible client talking to the job's vLLM server), so you pay
|
||||
for one model load per dataset, not three.
|
||||
|
||||
## Re-running a single module
|
||||
|
||||
Each module stages its raw output to
|
||||
`<root>/.annotate_staging/episode_{N:06d}/<module>.jsonl`. This makes
|
||||
prompt iteration cheap: re-running one module overwrites only its own
|
||||
JSONL, then the writer recomposes the final parquet. Disable modules you
|
||||
don't want with `--plan.enabled=false` (and likewise
|
||||
`--interjections.enabled` / `--vqa.enabled`) to test one at a time.
|
||||
|
||||
## What the validator checks
|
||||
|
||||
Before the writer runs, `StagingValidator` confirms:
|
||||
|
||||
- every event row lands exactly on a real frame timestamp;
|
||||
- no speech / interjection pairs are left orphaned;
|
||||
- `plan` is refreshed at every interjection timestamp;
|
||||
- `memory` rows fall on subtask boundaries (a warning, not an error);
|
||||
- each VQA assistant `content` is valid JSON in one of the
|
||||
bbox / keypoint / count / attribute / spatial shapes;
|
||||
- every row goes to the column chosen by `column_for_style(style)`.
|
||||
|
||||
Any error aborts the writer. Pass `--skip_validation=true` to override
|
||||
while debugging.
|
||||
|
||||
## Where each module's ideas come from
|
||||
|
||||
- **`plan` — subtasks.** Hi Robot ([Shi 2025](https://arxiv.org/abs/2502.19417))
|
||||
for atom granularity ("pick up one piece of lettuce", "place bowl to
|
||||
box"); Pi0.7 ([Physical Intelligence 2025](https://pi.website/pi07))
|
||||
for "how, not what" detail.
|
||||
- **`plan` — memory.** MEM ([Torne 2026](https://arxiv.org/abs/2603.03596)):
|
||||
keep only the minimal relevant information — preserve outcomes, drop
|
||||
specific attributes.
|
||||
- **`interjections`.** Hi Robot's scenario taxonomy: negative task,
|
||||
situated correction, specific constraint, preference. Speech is a
|
||||
tool-call-only atom
|
||||
(`tool_calls=[{type:function, function:{name:"say", arguments:{text:...}}}]`).
|
||||
- **`vqa`.** ECoT ([Zawalski 2024](https://arxiv.org/abs/2407.08693)) for
|
||||
grounded features (pixel bounding boxes `[x_min, y_min, x_max, y_max]`,
|
||||
keypoints) and Steerable VLA Policies
|
||||
([Zhao 2025](https://arxiv.org/abs/2509.07626)) for multi-abstraction
|
||||
grounding. Pi0.7 also grounds answers across abstraction levels.
|
||||
|
||||
When improving a module, tweak its prompt template in
|
||||
`src/lerobot/annotations/steerable_pipeline/prompts/` rather than
|
||||
rewriting from scratch.
|
||||
|
||||
## Roughly how much it costs
|
||||
|
||||
Per episode, the pipeline makes about `max_steps` plan calls,
|
||||
`max_interjections_per_episode` interjection calls, and
|
||||
`vqa_emission_hz × episode_seconds` VQA calls. With the defaults (8
|
||||
subtasks, 1 interjection, 1 Hz × 3 pairs) on a 30-second episode, that's
|
||||
~50 VLM calls.
|
||||
|
||||
Storage stays small: `language_persistent` is at most tens of KB per
|
||||
episode (parquet dictionary-encodes the one entry that repeats across
|
||||
frames), and `language_events` is empty on most frames — its size scales
|
||||
with the number of emissions, not `num_frames × num_emissions`.
|
||||
@@ -1,313 +0,0 @@
|
||||
# Asynchronous Inference
|
||||
|
||||
With our [SmolVLA](https://huggingface.co/papers/2506.01844) we introduced a new way to run inference on real-world robots, **decoupling action prediction from action execution**.
|
||||
In this tutorial, we'll show how to use asynchronous inference (_async inference_) using a finetuned version of SmolVLA, and all the policies supported by LeRobot.
|
||||
**Try async inference with all the policies** supported by LeRobot!
|
||||
|
||||
**What you'll learn:**
|
||||
|
||||
1. Why asynchronous inference matters and how it compares to, more traditional, sequential inference.
|
||||
2. How to spin-up a `PolicyServer` and connect a `RobotClient` from the same machine, and even over the network.
|
||||
3. How to tune key parameters (`actions_per_chunk`, `chunk_size_threshold`) for your robot and policy.
|
||||
|
||||
If you get stuck, hop into our [Discord community](https://discord.gg/s3KuuzsPFb)!
|
||||
|
||||
In a nutshell: with _async inference_, your robot keeps acting while the policy server is already busy computing the next chunk of actions---eliminating "wait-for-inference" lags and unlocking smoother, more reactive behaviours.
|
||||
This is fundamentally different from synchronous inference (sync), where the robot stays idle while the policy computes the next chunk of actions.
|
||||
|
||||
---
|
||||
|
||||
## Getting started with async inference
|
||||
|
||||
You can read more information on asynchronous inference in our [blogpost](https://huggingface.co/blog/async-robot-inference). This guide is designed to help you quickly set up and run asynchronous inference in your environment.
|
||||
|
||||
First, install `lerobot` with the `async` tag, to install the extra dependencies required to run async inference.
|
||||
|
||||
```shell
|
||||
pip install -e ".[async]"
|
||||
```
|
||||
|
||||
Then, spin up a policy server (in one terminal, or in a separate machine) specifying the host address and port for the client to connect to.
|
||||
You can spin up a policy server running:
|
||||
|
||||
```shell
|
||||
python -m lerobot.async_inference.policy_server \
|
||||
--host=127.0.0.1 \
|
||||
--port=8080
|
||||
```
|
||||
|
||||
This will start a policy server listening on `127.0.0.1:8080` (`localhost`, port 8080). At this stage, the policy server is empty, as all information related to which policy to run and with which parameters are specified during the first handshake with the client. Spin up a client with:
|
||||
|
||||
```shell
|
||||
python -m lerobot.async_inference.robot_client \
|
||||
--server_address=127.0.0.1:8080 \ # SERVER: the host address and port of the policy server
|
||||
--robot.type=so100_follower \ # ROBOT: your robot type
|
||||
--robot.port=/dev/tty.usbmodem585A0076841 \ # ROBOT: your robot port
|
||||
--robot.id=follower_so100 \ # ROBOT: your robot id, to load calibration file
|
||||
--robot.cameras="{ laptop: {type: opencv, index_or_path: 0, width: 1920, height: 1080, fps: 30}, phone: {type: opencv, index_or_path: 0, width: 1920, height: 1080, fps: 30}}" \ # POLICY: the cameras used to acquire frames, with keys matching the keys expected by the policy
|
||||
--task="dummy" \ # POLICY: The task to run the policy on (`Fold my t-shirt`). Not necessarily defined for all policies, such as `act`
|
||||
--policy_type=your_policy_type \ # POLICY: the type of policy to run (smolvla, act, etc)
|
||||
--pretrained_name_or_path=user/model \ # POLICY: the model name/path on server to the checkpoint to run (e.g., lerobot/smolvla_base)
|
||||
--policy_device=mps \ # POLICY: the device to run the policy on, on the server (cuda, mps, xpu, cpu)
|
||||
--actions_per_chunk=50 \ # POLICY: the number of actions to output at once
|
||||
--chunk_size_threshold=0.5 \ # CLIENT: the threshold for the chunk size before sending a new observation to the server
|
||||
--aggregate_fn_name=weighted_average \ # CLIENT: the function to aggregate actions on overlapping portions
|
||||
--debug_visualize_queue_size=True # CLIENT: whether to visualize the queue size at runtime
|
||||
```
|
||||
|
||||
In summary, you need to specify instructions for:
|
||||
|
||||
- `SERVER`: the address and port of the policy server
|
||||
- `ROBOT`: the type of robot to connect to, the port to connect to, and the local `id` of the robot
|
||||
- `POLICY`: the type of policy to run, and the model name/path on server to the checkpoint to run. You also need to specify which device should the sever be using, and how many actions to output at once (capped at the policy max actions value).
|
||||
- `CLIENT`: the threshold for the chunk size before sending a new observation to the server, and the function to aggregate actions on overlapping portions. Optionally, you can also visualize the queue size at runtime, to help you tune the `CLIENT` parameters.
|
||||
|
||||
Importantly,
|
||||
|
||||
- `actions_per_chunk` and `chunk_size_threshold` are key parameters to tune for your setup.
|
||||
- `aggregate_fn_name` is the function to aggregate actions on overlapping portions. You can either add a new one to a registry of functions, or add your own in `robot_client.py` (see [here](NOTE:addlinktoLOC))
|
||||
- `debug_visualize_queue_size` is a useful tool to tune the `CLIENT` parameters.
|
||||
|
||||
## Done! You should see your robot moving around by now 😉
|
||||
|
||||
## Async vs. synchronous inference
|
||||
|
||||
Synchronous inference relies on interleaving action chunk prediction and action execution. This inherently results in _idle frames_, frames where the robot awaits idle the policy's output: a new action chunk.
|
||||
In turn, inference is plagued by evident real-time lags, where the robot simply stops acting due to the lack of available actions.
|
||||
With robotics models increasing in size, this problem risks becoming only more severe.
|
||||
|
||||
<p align="center">
|
||||
<img
|
||||
src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/async-inference/sync.png"
|
||||
width="80%"
|
||||
></img>
|
||||
</p>
|
||||
<p align="center">
|
||||
<i>Synchronous inference</i> makes the robot idle while the policy is
|
||||
computing the next chunk of actions.
|
||||
</p>
|
||||
|
||||
To overcome this, we design async inference, a paradigm where action planning and execution are decoupled, resulting in (1) higher adaptability and, most importantly, (2) no idle frames.
|
||||
Crucially, with async inference, the next action chunk is computed _before_ the current one is exhausted, resulting in no idleness.
|
||||
Higher adaptability is ensured by aggregating the different action chunks on overlapping portions, obtaining an up-to-date plan and a tighter control loop.
|
||||
|
||||
<p align="center">
|
||||
<img
|
||||
src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/async-inference/async.png"
|
||||
width="80%"
|
||||
></img>
|
||||
</p>
|
||||
<p align="center">
|
||||
<i>Asynchronous inference</i> results in no idleness because the next chunk is
|
||||
computed before the current chunk is exhausted.
|
||||
</p>
|
||||
|
||||
---
|
||||
|
||||
## Start the Policy Server
|
||||
|
||||
Policy servers are wrappers around a `PreTrainedPolicy` interfacing them with observations coming from a robot client.
|
||||
Policy servers are initialized as empty containers which are populated with the requested policy specified in the initial handshake between the robot client and the policy server.
|
||||
As such, spinning up a policy server is as easy as specifying the host address and port. If you're running the policy server on the same machine as the robot client, you can use `localhost` as the host address.
|
||||
|
||||
<hfoptions id="start_policy_server">
|
||||
<hfoption id="Command">
|
||||
```bash
|
||||
python -m lerobot.async_inference.policy_server \
|
||||
--host=127.0.0.1 \
|
||||
--port=8080
|
||||
```
|
||||
</hfoption>
|
||||
<hfoption id="API example">
|
||||
|
||||
<!-- prettier-ignore-start -->
|
||||
```python
|
||||
from lerobot.async_inference.configs import PolicyServerConfig
|
||||
from lerobot.async_inference.policy_server import serve
|
||||
|
||||
config = PolicyServerConfig(
|
||||
host="localhost",
|
||||
port=8080,
|
||||
)
|
||||
serve(config)
|
||||
```
|
||||
<!-- prettier-ignore-end -->
|
||||
|
||||
</hfoption>
|
||||
</hfoptions>
|
||||
|
||||
This listens on `localhost:8080` for an incoming connection from the associated`RobotClient`, which will communicate which policy to run during the first client-server handshake.
|
||||
|
||||
---
|
||||
|
||||
## Launch the Robot Client
|
||||
|
||||
`RobotClient` is a wrapper around a `Robot` instance, which `RobotClient` connects to the (possibly remote) `PolicyServer`.
|
||||
The `RobotClient` streams observations to the `PolicyServer`, and receives action chunks obtained running inference on the server (which we assume to have better computational resources than the robot controller).
|
||||
|
||||
<hfoptions id="start_robot_client">
|
||||
<hfoption id="Command">
|
||||
```bash
|
||||
python -m lerobot.async_inference.robot_client \
|
||||
--server_address=127.0.0.1:8080 \ # SERVER: the host address and port of the policy server
|
||||
--robot.type=so100_follower \ # ROBOT: your robot type
|
||||
--robot.port=/dev/tty.usbmodem585A0076841 \ # ROBOT: your robot port
|
||||
--robot.id=follower_so100 \ # ROBOT: your robot id, to load calibration file
|
||||
--robot.cameras="{ laptop: {type: opencv, index_or_path: 0, width: 1920, height: 1080, fps: 30}, phone: {type: opencv, index_or_path: 0, width: 1920, height: 1080, fps: 30}}" \ # POLICY: the cameras used to acquire frames, with keys matching the keys expected by the policy
|
||||
--task="dummy" \ # POLICY: The task to run the policy on (`Fold my t-shirt`). Not necessarily defined for all policies, such as `act`
|
||||
--policy_type=your_policy_type \ # POLICY: the type of policy to run (smolvla, act, etc)
|
||||
--pretrained_name_or_path=user/model \ # POLICY: the model name/path on server to the checkpoint to run (e.g., lerobot/smolvla_base)
|
||||
--policy_device=mps \ # POLICY: the device to run the policy on, on the server
|
||||
--actions_per_chunk=50 \ # POLICY: the number of actions to output at once
|
||||
--chunk_size_threshold=0.5 \ # CLIENT: the threshold for the chunk size before sending a new observation to the server
|
||||
--aggregate_fn_name=weighted_average \ # CLIENT: the function to aggregate actions on overlapping portions
|
||||
--debug_visualize_queue_size=True # CLIENT: whether to visualize the queue size at runtime
|
||||
```
|
||||
</hfoption>
|
||||
<hfoption id="API example">
|
||||
|
||||
<!-- prettier-ignore-start -->
|
||||
```python
|
||||
import threading
|
||||
from lerobot.robots.so_follower import SO100FollowerConfig
|
||||
from lerobot.cameras.opencv import OpenCVCameraConfig
|
||||
from lerobot.async_inference.configs import RobotClientConfig
|
||||
from lerobot.async_inference.robot_client import RobotClient
|
||||
from lerobot.async_inference.helpers import visualize_action_queue_size
|
||||
|
||||
# 1. Create the robot instance
|
||||
"""Check out the cameras available in your setup by running `python lerobot/find_cameras.py`"""
|
||||
# these cameras must match the ones expected by the policy
|
||||
# check the config.json on the Hub for the policy you are using
|
||||
camera_cfg = {
|
||||
"top": OpenCVCameraConfig(index_or_path=0, width=640, height=480, fps=30),
|
||||
"side": OpenCVCameraConfig(index_or_path=1, width=640, height=480, fps=30)
|
||||
}
|
||||
|
||||
robot_cfg = SO100FollowerConfig(
|
||||
port="/dev/tty.usbmodem585A0076841",
|
||||
id="follower_so100",
|
||||
cameras=camera_cfg
|
||||
)
|
||||
|
||||
# 3. Create client configuration
|
||||
client_cfg = RobotClientConfig(
|
||||
robot=robot_cfg,
|
||||
server_address="localhost:8080",
|
||||
policy_device="mps",
|
||||
client_device="cpu",
|
||||
policy_type="smolvla",
|
||||
pretrained_name_or_path="<user>/smolvla_async",
|
||||
chunk_size_threshold=0.5,
|
||||
actions_per_chunk=50, # make sure this is less than the max actions of the policy
|
||||
)
|
||||
|
||||
# 4. Create and start client
|
||||
client = RobotClient(client_cfg)
|
||||
|
||||
# 5. Specify the task
|
||||
task = "Don't do anything, stay still"
|
||||
|
||||
if client.start():
|
||||
# Start action receiver thread
|
||||
action_receiver_thread = threading.Thread(target=client.receive_actions, daemon=True)
|
||||
action_receiver_thread.start()
|
||||
|
||||
try:
|
||||
# Run the control loop
|
||||
client.control_loop(task)
|
||||
except KeyboardInterrupt:
|
||||
client.stop()
|
||||
action_receiver_thread.join()
|
||||
# (Optionally) plot the action queue size
|
||||
visualize_action_queue_size(client.action_queue_size)
|
||||
```
|
||||
<!-- prettier-ignore-end -->
|
||||
|
||||
</hfoption>
|
||||
</hfoptions>
|
||||
|
||||
The following two parameters are key in every setup:
|
||||
|
||||
<table>
|
||||
<thead>
|
||||
<tr>
|
||||
<th>Hyperparameter</th>
|
||||
<th>Default</th>
|
||||
<th>What it does</th>
|
||||
</tr>
|
||||
</thead>
|
||||
<tbody>
|
||||
<tr>
|
||||
<td>
|
||||
<code>actions_per_chunk</code>
|
||||
</td>
|
||||
<td>50</td>
|
||||
<td>
|
||||
How many actions the policy outputs at once. Typical values: 10-50.
|
||||
</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>
|
||||
<code>chunk_size_threshold</code>
|
||||
</td>
|
||||
<td>0.7</td>
|
||||
<td>
|
||||
When the queue is ≤ 50% full, the client sends a fresh observation.
|
||||
Value in [0, 1].
|
||||
</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
|
||||
<Tip>
|
||||
Different values of `actions_per_chunk` and `chunk_size_threshold` do result
|
||||
in different behaviours.
|
||||
</Tip>
|
||||
|
||||
On the one hand, increasing the value of `actions_per_chunk` will result in reducing the likelihood of ending up with no actions to execute, as more actions will be available when the new chunk is computed.
|
||||
However, larger values of `actions_per_chunk` might also result in less precise actions, due to the compounding errors consequent to predicting actions over longer timespans.
|
||||
|
||||
On the other hand, increasing the value of `chunk_size_threshold` will result in sending out to the `PolicyServer` observations for inference more often, resulting in a larger number of updates action chunks, overlapping on significant portions. This results in high adaptability, in the limit predicting one action chunk for each observation, which is in turn only marginally consumed while a new one is produced.
|
||||
This option does also put more pressure on the inference pipeline, as a consequence of the many requests. Conversely, values of `chunk_size_threshold` close to 0.0 collapse to the synchronous edge case, whereby new observations are only sent out whenever the current chunk is exhausted.
|
||||
|
||||
We found the default values of `actions_per_chunk` and `chunk_size_threshold` to work well in the experiments we developed for the [SmolVLA paper](https://huggingface.co/papers/2506.01844), but recommend experimenting with different values to find the best fit for your setup.
|
||||
|
||||
### Tuning async inference for your setup
|
||||
|
||||
1. **Choose your computational resources carefully.** [PI0](https://huggingface.co/lerobot/pi0) occupies 14GB of memory at inference time, while [SmolVLA](https://huggingface.co/lerobot/smolvla_base) requires only ~2GB. You should identify the best computational resource for your use case keeping in mind smaller policies require less computational resources. The combination of policy and device used (CPU-intensive, using MPS, or the number of CUDA cores on a given NVIDIA GPU) directly impacts the average inference latency you should expect.
|
||||
2. **Adjust your `fps` based on inference latency.** While the server generates a new action chunk, the client is not idle and is stepping through its current action queue. If the two processes happen at fundamentally different speeds, the client might end up with an empty queue. As such, you should reduce your fps if you consistently run out of actions in queue.
|
||||
3. **Adjust `chunk_size_threshold`**.
|
||||
- Values closer to `0.0` result in almost sequential behavior. Values closer to `1.0` → send observation every step (more bandwidth, relies on good world-model).
|
||||
- We found values around 0.5-0.6 to work well. If you want to tweak this, spin up a `RobotClient` setting the `--debug_visualize_queue_size` to `True`. This will plot the action queue size evolution at runtime, and you can use it to find the value of `chunk_size_threshold` that works best for your setup.
|
||||
|
||||
<p align="center">
|
||||
<img
|
||||
src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/async-inference/queues.png"
|
||||
width="80%"
|
||||
></img>
|
||||
</p>
|
||||
<p align="center">
|
||||
<i>
|
||||
The action queue size is plotted at runtime when the
|
||||
`--debug_visualize_queue_size` flag is passed, for various levels of
|
||||
`chunk_size_threshold` (`g` in the SmolVLA paper).
|
||||
</i>
|
||||
</p>
|
||||
|
||||
---
|
||||
|
||||
## Conclusion
|
||||
|
||||
Asynchronous inference represents a significant advancement in real-time robotics control, addressing the fundamental challenge of inference latency that has long plagued robotics applications. Through this tutorial, you've learned how to implement a complete async inference pipeline that eliminates idle frames and enables smoother, more reactive robot behaviors.
|
||||
|
||||
**Key Takeaways:**
|
||||
|
||||
- **Paradigm Shift**: Async inference decouples action prediction from execution, allowing robots to continue acting while new action chunks are computed in parallel
|
||||
- **Performance Benefits**: Eliminates "wait-for-inference" lags that are inherent in synchronous approaches, becoming increasingly important as policy models grow larger
|
||||
- **Flexible Architecture**: The server-client design enables distributed computing, where inference can run on powerful remote hardware while maintaining real-time robot control
|
||||
- **Tunable Parameters**: Success depends on properly configuring `actions_per_chunk` and `chunk_size_threshold` for your specific hardware, policy, and task requirements
|
||||
- **Universal Compatibility**: Works with all LeRobot-supported policies, from lightweight ACT models to vision-language models like SmolVLA
|
||||
|
||||
Start experimenting with the default parameters, monitor your action queue sizes, and iteratively refine your setup to achieve optimal performance for your specific use case.
|
||||
If you want to discuss this further, hop into our [Discord community](https://discord.gg/s3KuuzsPFb), or open an issue on our [GitHub repository](https://github.com/huggingface/lerobot/issues).
|
||||
@@ -165,8 +165,6 @@ Batches are flat dictionaries keyed by the constants in [`lerobot.utils.constant
|
||||
|
||||
LeRobot uses `PolicyProcessorPipeline`s to normalize inputs and de-normalize outputs around your policy. For a concrete reference, see [`processor_act.py`](https://github.com/huggingface/lerobot/blob/main/src/lerobot/policies/act/processor_act.py) or [`processor_diffusion.py`](https://github.com/huggingface/lerobot/blob/main/src/lerobot/policies/diffusion/processor_diffusion.py).
|
||||
|
||||
Pay close attention here: processors are the most common reproducibility pain point. A mismatch in normalization mode (`IDENTITY` vs `MEAN_STD` vs `MIN_MAX` vs `QUANTILES`/`QUANTILE10`) or in which features get normalized will train and eval without erroring, yet silently wreck results. Make sure the modes match how the checkpoint was trained, that the required stats exist (e.g. `QUANTILES` needs `q01`/`q99`), and that the pre- and post-processors stay consistent.
|
||||
|
||||
```python
|
||||
# processor_my_policy.py
|
||||
from typing import Any
|
||||
@@ -373,7 +371,6 @@ The general expectations are in [`CONTRIBUTING.md`](https://github.com/huggingfa
|
||||
- [ ] Optional deps live behind a `[project.optional-dependencies]` extra and the `TYPE_CHECKING + require_package` guard.
|
||||
- [ ] `tests/policies/` updated; backward-compat artifact committed & policy-specific tests.
|
||||
- [ ] `src/lerobot/policies/<name>/README.md` symlinked into `docs/source/policy_<name>_README.md`; user-facing `docs/source/<name>.mdx` written and added to `_toctree.yml`.
|
||||
- [ ] `lerobot-train --policy.type my_policy ...` runs end-to-end for at least a few steps + save a checkpoint that can be loaded and run by `lerobot-eval` or `lerobot-rollout`.
|
||||
- [ ] At least one reproducible benchmark eval in the policy MDX with a published checkpoint (sim benchmark, or real-robot dataset + checkpoint).
|
||||
|
||||
The fastest way to get a clean PR is to copy the directory of the existing policy closest to yours, rename, and replace contents method by method. Don't wait until everything is polished — open a draft PR early and iterate with us; reviewers would much rather give feedback on a half-finished branch than a fully-merged one.
|
||||
|
||||
@@ -157,14 +157,6 @@ finally:
|
||||
</hfoption>
|
||||
</hfoptions>
|
||||
|
||||
### Working with depth
|
||||
|
||||
The Intel RealSense and Reachy 2 cameras can capture both color and depth in lockstep. Calling `read()` returns the **color** frame as `(H, W, 3)` `uint8`. Calling `read_depth()` returns the **depth map** as `(H, W, 1)` `uint16`, where each pixel value is the distance from the sensor expressed in **millimetres**. A pixel value of `0` typically means "no measurement available" (out-of-range, occluded, or low-confidence).
|
||||
|
||||
During recording, the control loop peeks the freshest buffered frames non-blockingly via `read_latest()` (color) and `read_latest_depth()` (depth), adding the depth map as a sibling feature (e.g. `front_depth` next to `front`).
|
||||
|
||||
For how depth streams are stored and encoded when recording a dataset, see the [Depth streams](./video_encoding_parameters#depth-streams) section of the video encoding guide.
|
||||
|
||||
## Use your phone's camera
|
||||
|
||||
<hfoptions id="use phone">
|
||||
|
||||
@@ -89,36 +89,6 @@ Control the data recording flow using keyboard shortcuts:
|
||||
- Press **Left Arrow (`←`)**: Delete current episode and retry.
|
||||
- Press **Escape (`ESC`)**: Stop, encode videos, and upload.
|
||||
|
||||
### Recording depth
|
||||
|
||||
Intel RealSense cameras (`type: intelrealsense`) record a depth stream when you set `use_depth: true`. Depth is quantized to 12-bit codes and stored as its own video.
|
||||
|
||||
```bash
|
||||
lerobot-record \
|
||||
... \
|
||||
--robot.cameras="{ head: {type: intelrealsense, serial_number_or_name: \"0123456789\", width: 640, height: 480, fps: 30, use_depth: true} }" \
|
||||
--dataset.repo_id=${HF_USER}/so101_depth_test \
|
||||
--dataset.single_task="put the red brick in a bowl" \
|
||||
--dataset.depth_encoder.depth_min=0.01 \
|
||||
--dataset.depth_encoder.depth_max=10.0 \
|
||||
--dataset.depth_encoder.shift=0.0 \
|
||||
--dataset.depth_encoder.use_log=true
|
||||
```
|
||||
|
||||
### Video encoding parameters
|
||||
|
||||
RGB and depth streams are encoded independently via the `--dataset.rgb_encoder.*` and `--dataset.depth_encoder.*` keys.
|
||||
|
||||
```bash
|
||||
lerobot-record \
|
||||
... \
|
||||
--dataset.rgb_encoder.vcodec=h264 \
|
||||
--dataset.rgb_encoder.pix_fmt=yuv420p \
|
||||
--dataset.rgb_encoder.crf=23 \
|
||||
--dataset.depth_encoder.vcodec=hevc \
|
||||
--dataset.depth_encoder.extra_options='{"x265-params": "lossless=1"}'
|
||||
```
|
||||
|
||||
### Training
|
||||
|
||||
Depending on your hardware training the policy might take a few hours. That's how you train simple `ACT` policy:
|
||||
@@ -150,14 +120,6 @@ lerobot-train \
|
||||
--steps=20000
|
||||
```
|
||||
|
||||
No local GPU? Add `--job.target=<flavor>` (e.g. `a10g-small`) to either command and `lerobot-train` runs it on [Hugging Face Jobs](https://huggingface.co/docs/hub/jobs) instead — it uploads a local-only dataset for you and pushes the trained model. List flavors with `hf jobs hardware`.
|
||||
|
||||
To resume, point `--config_path` at a checkpoint and add `--resume=true`. It accepts a local path or a Hub repo id (the latest checkpoint is fetched), and works locally or on a job by adding `--job.target=<flavor>`:
|
||||
|
||||
```bash
|
||||
lerobot-train --config_path=${HF_USER}/policy_test --resume=true --job.target=a10g-small
|
||||
```
|
||||
|
||||
### Inference
|
||||
|
||||
Inference means running the trained policy/model on a robot. For that we use `lerobot-rollout`. You will need to provide a path to your policy. It can be a local path or a path to Hugging Face for example "lerobot/folding_latest". Your cameras configuration needs to match what was used when collecting the dataset. Duration is in seconds if unspecified, it will run forever.
|
||||
|
||||
@@ -194,7 +194,7 @@ lerobot-record \
|
||||
--dataset.single_task="Navigate around obstacles" \
|
||||
--dataset.streaming_encoding=true \
|
||||
--dataset.encoder_threads=2 \
|
||||
# --dataset.rgb_encoder.vcodec=auto \
|
||||
# --dataset.camera_encoder.vcodec=auto \
|
||||
--display_data=true
|
||||
```
|
||||
|
||||
|
||||
@@ -1,167 +0,0 @@
|
||||
# FastWAM
|
||||
|
||||
FastWAM is a World Action Model policy for robot control. The LeRobot integration exposes FastWAM through the standard policy API so it can be configured with `policy.type=fastwam`, trained with `lerobot-train`, and loaded through the LeRobot pretrained policy interface.
|
||||
|
||||
## Model Overview
|
||||
|
||||
FastWAM keeps video modeling during training, but uses direct action prediction at inference time instead of iteratively generating future observations. This LeRobot policy wraps the FastWAM action model, adapts LeRobot batches to FastWAM training samples, and provides the standard processor pipeline for normalization and action postprocessing.
|
||||
|
||||
The implementation initializes the visual world-model components from `Wan-AI/Wan2.2-TI2V-5B` by default and predicts action chunks with shape `[batch, action_horizon, action_dim]`.
|
||||
|
||||
### What the LeRobot Integration Covers
|
||||
|
||||
- Standard `policy.type=fastwam` configuration through LeRobot
|
||||
- Image, state, action, and language-task batch adaptation
|
||||
- Action chunk inference through `select_action` and `predict_action_chunk`
|
||||
- Checkpoint save/load through the LeRobot policy APIs
|
||||
- Configurable LIBERO gripper action postprocessing
|
||||
|
||||
## Installation Requirements
|
||||
|
||||
Install LeRobot from source, then install FastWAM dependencies:
|
||||
|
||||
```bash
|
||||
pip install -e ".[fastwam]"
|
||||
```
|
||||
|
||||
This installs the FastWAM policy extra from `pyproject.toml`: `transformers`,
|
||||
`diffusers`, `ftfy`, and `regex`, plus LeRobot's base dependencies.
|
||||
|
||||
For LIBERO evaluation, install the benchmark dependencies too:
|
||||
|
||||
```bash
|
||||
pip install -e ".[fastwam,libero]"
|
||||
```
|
||||
|
||||
This installs both extras. In addition to the FastWAM dependencies above, the
|
||||
`libero` extra installs LeRobot dataset dependencies, `hf-libero` on Linux, and
|
||||
`scipy`.
|
||||
|
||||
FastWAM uses the Wan2.2 TI2V backbone. The default model id is:
|
||||
|
||||
```python
|
||||
policy.model_id=Wan-AI/Wan2.2-TI2V-5B
|
||||
```
|
||||
|
||||
## Data Requirements
|
||||
|
||||
FastWAM expects a LeRobot dataset with:
|
||||
|
||||
- one or more visual observations whose widths concatenate to `policy.image_size[1]`
|
||||
- `observation.state` when `policy.proprio_dim` is not `None`
|
||||
- `action`
|
||||
- a language task instruction through the dataset task field, or precomputed `context` and `context_mask` tensors
|
||||
|
||||
The default visual setup is one image feature named `observation.images.image` with shape `(3, 224, 448)`. If the dataset uses two cameras, configure `policy.input_features` so their heights match `224` and their widths sum to `448`.
|
||||
|
||||
## Usage
|
||||
|
||||
Create a new FastWAM policy with:
|
||||
|
||||
```bash
|
||||
lerobot-train \
|
||||
--dataset.repo_id=your-org/your-dataset \
|
||||
--policy.type=fastwam \
|
||||
--policy.action_dim=7 \
|
||||
--policy.proprio_dim=8 \
|
||||
--policy.action_horizon=32 \
|
||||
--policy.n_action_steps=10 \
|
||||
--policy.image_size='[224,448]' \
|
||||
--output_dir=./outputs/fastwam_training \
|
||||
--job_name=fastwam_training \
|
||||
--steps=300000 \
|
||||
--batch_size=8 \
|
||||
--policy.device=cuda
|
||||
```
|
||||
|
||||
Evaluate an existing LeRobot-format checkpoint on LIBERO-10 with:
|
||||
|
||||
```bash
|
||||
lerobot-eval \
|
||||
--policy.path=ZibinDong/fastwam_libero_uncond_2cam224 \
|
||||
--policy.device=cuda \
|
||||
--policy.torch_dtype=float32 \
|
||||
--policy.n_action_steps=10 \
|
||||
--env.type=libero \
|
||||
--env.task=libero_10 \
|
||||
--env.observation_height=224 \
|
||||
--env.observation_width=224 \
|
||||
--eval.batch_size=1 \
|
||||
--eval.n_episodes=50 \
|
||||
--seed=0 \
|
||||
--env.episode_length=600
|
||||
```
|
||||
|
||||
For `libero_goal`, `libero_spatial`, and `libero_object`, use
|
||||
`--env.episode_length=300`.
|
||||
|
||||
For real-robot rollout, use the same checkpoint path:
|
||||
|
||||
```bash
|
||||
lerobot-rollout \
|
||||
--robot.type=so101_follower \
|
||||
--robot.port=/dev/ttyACM0 \
|
||||
--policy.path=your-org/fastwam-real-robot
|
||||
```
|
||||
|
||||
## Configuration Notes
|
||||
|
||||
### Image Features
|
||||
|
||||
`policy.image_size` is the size of the concatenated FastWAM image tensor as `(height, width)`. Each configured image feature must have shape `(3, height, camera_width)`, and all camera widths must sum to the configured width.
|
||||
|
||||
### Action Chunking
|
||||
|
||||
`policy.action_horizon` controls the number of future actions supervised during training and predicted during inference. `policy.n_action_steps` controls how many actions are consumed before the policy predicts a fresh chunk. `policy.n_action_steps` must be less than or equal to `policy.action_horizon`.
|
||||
|
||||
### Wan Components
|
||||
|
||||
FastWAM loads the Wan VAE, video DiT, text encoder, and tokenizer from the configured Wan model directory or Hugging Face Hub model id. LeRobot-format FastWAM checkpoints saved by `save_pretrained` also copy the local Wan component files needed by `from_pretrained`.
|
||||
|
||||
### Attention Backend
|
||||
|
||||
FastWAM's DiT uses PyTorch's `scaled_dot_product_attention` (SDPA) for all attention. It does **not** use FlashAttention: its Mixture-of-Transformers (MoT) routing needs arbitrary boolean `[query, key]` attention masks, which the FlashAttention varlen API cannot express. Installing the `flash-attn` package therefore has no effect on the FastWAM path. (Note that SDPA itself may still select PyTorch's own flash / memory-efficient / math kernel internally — this is unrelated to the `flash-attn` package.)
|
||||
|
||||
### LIBERO Action Toggle
|
||||
|
||||
FastWAM LIBERO checkpoints use `policy.toggle_action_dimensions=[-1]` by
|
||||
default to match the gripper action convention used by the original FastWAM
|
||||
evaluation pipeline:
|
||||
|
||||
```bash
|
||||
--policy.toggle_action_dimensions='[-1]'
|
||||
```
|
||||
|
||||
## Results
|
||||
|
||||
Evaluated on LIBERO with [`ZibinDong/fastwam_libero_uncond_2cam224`](https://huggingface.co/ZibinDong/fastwam_libero_uncond_2cam224):
|
||||
|
||||
| Suite | Success rate | n_episodes |
|
||||
| -------------- | -----------: | ---------: |
|
||||
| libero_spatial | 97.6% | 500 |
|
||||
| libero_object | 99.0% | 500 |
|
||||
| libero_goal | 95.0% | 500 |
|
||||
| libero_10 | 94.0% | 500 |
|
||||
| **average** | **96.4%** | 2000 |
|
||||
|
||||
Reproduce: `lerobot-eval --policy.path=ZibinDong/fastwam_libero_uncond_2cam224 --policy.device=cuda --policy.torch_dtype=float32 --policy.n_action_steps=10 --env.type=libero --env.task=libero_spatial --env.observation_height=256 --env.observation_width=256 --eval.batch_size=1 --eval.n_episodes=50 --seed=0 --env.episode_length=300` (1x H20 140 GB).
|
||||
|
||||
## References
|
||||
|
||||
- [Fast-WAM paper](https://arxiv.org/abs/2603.16666)
|
||||
- [Fast-WAM project page](https://yuantianyuan01.github.io/FastWAM/)
|
||||
- [Fast-WAM code](https://github.com/yuantianyuan01/FastWAM)
|
||||
- [Released upstream checkpoints](https://huggingface.co/yuanty/fastwam)
|
||||
- [Wan2.2 TI2V 5B](https://huggingface.co/Wan-AI/Wan2.2-TI2V-5B)
|
||||
|
||||
## Citation
|
||||
|
||||
```bibtex
|
||||
@article{yuan2026fastwam,
|
||||
title = {Fast-WAM: Do World Action Models Need Test-time Future Imagination?},
|
||||
author = {Tianyuan Yuan and Zibin Dong and Yicheng Liu and Hang Zhao},
|
||||
journal = {arXiv preprint arXiv:2603.16666},
|
||||
year = {2026},
|
||||
url = {https://arxiv.org/abs/2603.16666}
|
||||
}
|
||||
```
|
||||
@@ -124,7 +124,7 @@ lerobot-rollout\
|
||||
--dataset.single_task="Grab and handover the red cube to the other arm" \
|
||||
--dataset.streaming_encoding=true \
|
||||
--dataset.encoder_threads=2 \
|
||||
# --dataset.rgb_encoder.vcodec=auto \
|
||||
# --dataset.camera_encoder.vcodec=auto \
|
||||
--policy.path=<user>/groot-bimanual \ # your trained model
|
||||
--duration=600
|
||||
```
|
||||
|
||||
@@ -96,4 +96,3 @@ Notes:
|
||||
- The leading `nvidia-smi` is a quick sanity check that CUDA is visible inside the container — useful to fail fast if the flavor or driver mismatched.
|
||||
- The default Job timeout is 30 minutes; pass `--timeout 4h` (or longer) for real training.
|
||||
- `--flavor` maps onto the table above: `t4-small`/`t4-medium` (T4, ACT only), `l4x1`/`l4x4` (L4 24 GB), `a10g-small/large/largex2/largex4` (A10G 24 GB scaled out), `a100-large` (A100). For the current full catalogue + pricing see [https://huggingface.co/docs/hub/jobs](https://huggingface.co/docs/hub/jobs).
|
||||
- Prefer not to write the `hf jobs run` wrapper yourself? `lerobot-train` can submit the job for you: just add `--job.target=<flavor>` to a normal training command and it handles dataset upload, log streaming, and the final model push. See the [imitation-learning training guide](./il_robots).
|
||||
|
||||
@@ -57,11 +57,11 @@ The `lerobot-rollout --strategy.type=dagger` mode requires **teleoperators with
|
||||
|
||||
**Compatible teleoperators:**
|
||||
|
||||
- `bi_openarm_mini` - Bimanual OpenArm Mini
|
||||
- `openarm_mini` - OpenArm Mini
|
||||
- `so_leader` - SO100 / SO101 leader arm
|
||||
|
||||
> [!IMPORTANT]
|
||||
> The provided commands default to `bi_openarm_follower` + `bi_openarm_mini`.
|
||||
> The provided commands default to `bi_openarm_follower` + `openarm_mini`.
|
||||
> `so_follower` + `so_leader` configs are also registered and can be used via CLI flags.
|
||||
|
||||
---
|
||||
@@ -104,9 +104,9 @@ lerobot-rollout --strategy.type=dagger \
|
||||
--robot.right_arm_config.port=can0 \
|
||||
--robot.right_arm_config.side=right \
|
||||
--robot.cameras='{left_wrist: {type: opencv, index_or_path: "/dev/video0", width: 1280, height: 720, fps: 30}, right_wrist: {type: opencv, index_or_path: "/dev/video4", width: 1280, height: 720, fps: 30}, base: {type: opencv, index_or_path: "/dev/video2", width: 640, height: 480, fps: 30}}' \
|
||||
--teleop.type=bi_openarm_mini \
|
||||
--teleop.left_arm_config.port=/dev/ttyACM0 \
|
||||
--teleop.right_arm_config.port=/dev/ttyACM1 \
|
||||
--teleop.type=openarm_mini \
|
||||
--teleop.port_left=/dev/ttyACM0 \
|
||||
--teleop.port_right=/dev/ttyACM1 \
|
||||
--policy.path=outputs/pretrain/checkpoints/last/pretrained_model \
|
||||
--dataset.repo_id=your-username/rollout_hil_dataset \
|
||||
--dataset.single_task="Fold the T-shirt properly" \
|
||||
@@ -131,9 +131,9 @@ lerobot-rollout --strategy.type=dagger \
|
||||
--robot.right_arm_config.port=can0 \
|
||||
--robot.right_arm_config.side=right \
|
||||
--robot.cameras='{left_wrist: {type: opencv, index_or_path: "/dev/video0", width: 1280, height: 720, fps: 30}, right_wrist: {type: opencv, index_or_path: "/dev/video4", width: 1280, height: 720, fps: 30}, base: {type: opencv, index_or_path: "/dev/video2", width: 640, height: 480, fps: 30}}' \
|
||||
--teleop.type=bi_openarm_mini \
|
||||
--teleop.left_arm_config.port=/dev/ttyACM0 \
|
||||
--teleop.right_arm_config.port=/dev/ttyACM1 \
|
||||
--teleop.type=openarm_mini \
|
||||
--teleop.port_left=/dev/ttyACM0 \
|
||||
--teleop.port_right=/dev/ttyACM1 \
|
||||
--policy.path=outputs/pretrain/checkpoints/last/pretrained_model \
|
||||
--dataset.repo_id=your-username/rollout_hil_rtc_dataset \
|
||||
--dataset.single_task="Fold the T-shirt properly" \
|
||||
|
||||
@@ -719,7 +719,7 @@ Example configuration for training the [reward classifier](https://huggingface.c
|
||||
"num_workers": 4,
|
||||
"steps": 5000,
|
||||
"log_freq": 10,
|
||||
"env_eval_freq": 1000,
|
||||
"eval_freq": 1000,
|
||||
"save_freq": 1000,
|
||||
"save_checkpoint": true,
|
||||
"seed": 2,
|
||||
|
||||
@@ -232,7 +232,7 @@ lerobot-record \
|
||||
--dataset.private=true \
|
||||
--dataset.streaming_encoding=true \
|
||||
--dataset.encoder_threads=2 \
|
||||
# --dataset.rgb_encoder.vcodec=auto \
|
||||
# --dataset.camera_encoder.vcodec=auto \
|
||||
--display_data=true
|
||||
```
|
||||
|
||||
@@ -278,6 +278,6 @@ lerobot-record \
|
||||
--dataset.num_episodes=10 \
|
||||
--dataset.streaming_encoding=true \
|
||||
--dataset.encoder_threads=2 \
|
||||
# --dataset.rgb_encoder.vcodec=auto \
|
||||
# --dataset.camera_encoder.vcodec=auto \
|
||||
--policy.path=outputs/train/hopejr_hand/checkpoints/last/pretrained_model
|
||||
```
|
||||
|
||||
@@ -207,7 +207,7 @@ lerobot-record \
|
||||
--dataset.num_episodes=5 \
|
||||
--dataset.single_task="Grab the black cube" \
|
||||
--dataset.streaming_encoding=true \
|
||||
# --dataset.rgb_encoder.vcodec=auto \
|
||||
# --dataset.camera_encoder.vcodec=auto \
|
||||
--dataset.encoder_threads=2
|
||||
```
|
||||
</hfoption>
|
||||
@@ -390,17 +390,9 @@ Set the flow of data recording using command-line arguments:
|
||||
|
||||
Control the data recording flow using keyboard shortcuts:
|
||||
|
||||
- Press **Right Arrow (`→`)** or **`n`**: Early stop the current episode or reset time and move to the next.
|
||||
- Press **Left Arrow (`←`)** or **`r`**: Cancel the current episode and re-record it.
|
||||
- Press **Escape (`ESC`)** or **`q`**: Immediately stop the session, encode videos, and upload the dataset.
|
||||
|
||||
<Tip>
|
||||
|
||||
These control-flow shortcuts work on **X11, Wayland, and headless/SSH** sessions. When a global keyboard backend isn't available (Wayland, a headless machine, or macOS without Accessibility permission), `lerobot-record` automatically reads the same keys from the terminal — launch it from an interactive terminal and keep it focused. You can also use the letter equivalents **`n`** (next, same as `→`), **`r`** (re-record, same as `←`) and **`q`** (quit, same as `ESC`). No `$DISPLAY` setup is required.
|
||||
|
||||
This applies to the recording control flow only. Keyboard **teleoperation** (driving the robot with the keyboard) still needs a global key backend, so it works only on an X11 session, a Windows desktop, or macOS with Accessibility/Input Monitoring granted — not on Wayland or headless sessions.
|
||||
|
||||
</Tip>
|
||||
- Press **Right Arrow (`→`)**: Early stop the current episode or reset time and move to the next.
|
||||
- Press **Left Arrow (`←`)**: Cancel the current episode and re-record it.
|
||||
- Press **Escape (`ESC`)**: Immediately stop the session, encode videos, and upload the dataset.
|
||||
|
||||
#### Tips for gathering data
|
||||
|
||||
@@ -414,7 +406,7 @@ If you want to dive deeper into this important topic, you can check out the [blo
|
||||
|
||||
#### Troubleshooting:
|
||||
|
||||
- On Linux, the recording control-flow keys (arrow keys, Escape) work on X11, Wayland, and headless/SSH sessions as long as `lerobot-record` runs in an interactive terminal — no `$DISPLAY` setup is needed. If the keys have no effect, make sure you are in an interactive (TTY) terminal, not a piped/non-TTY session, and that it is focused; the letter equivalents `n` / `r` / `q` also work. Keyboard _teleoperation_ (as opposed to the recording control flow) still requires a global key backend — an X11 session, a Windows desktop, or macOS with Accessibility/Input Monitoring granted — and is unavailable on Wayland or headless machines. See [pynput limitations](https://pynput.readthedocs.io/en/latest/limitations.html#linux).
|
||||
- On Linux, if the left and right arrow keys and escape key don't have any effect during data recording, make sure you've set the `$DISPLAY` environment variable. See [pynput limitations](https://pynput.readthedocs.io/en/latest/limitations.html#linux).
|
||||
|
||||
## Visualize a dataset
|
||||
|
||||
@@ -514,12 +506,6 @@ lerobot-train \
|
||||
--resume=true
|
||||
```
|
||||
|
||||
`--config_path` also accepts a **Hub repo id**: if a run pushed its checkpoints to the Hub (with `--save_checkpoint_to_hub=true`), you can resume straight from the repo — its latest checkpoint is downloaded and training continues, restoring the optimizer, scheduler, step counter and data order:
|
||||
|
||||
```bash
|
||||
lerobot-train --config_path=${HF_USER}/my_policy --resume=true
|
||||
```
|
||||
|
||||
If you do not want to push your model to the hub after training use `--policy.push_to_hub=false`.
|
||||
|
||||
Additionally you can provide extra `tags` or specify a `license` for your model or make the model repo `private` by adding this: `--policy.private=true --policy.tags=\[ppo,rl\] --policy.license=mit`
|
||||
@@ -532,9 +518,7 @@ If your local computer doesn't have a powerful GPU you could utilize Google Cola
|
||||
|
||||
Hugging Face jobs let's you easily select hardware and run the training in the cloud. So if you don't have a powerful GPU or you need more VRAM or just want to train a model much faster use HF Jobs! It's pay as you go and you simply pay for each second of use, you can see the pricing and additional information [here](https://huggingface.co/docs/hub/jobs).
|
||||
|
||||
> **Tip:** if you just want to launch a standard training run, you can skip building the command below and use the integrated **Train on HF Jobs via `--job.target`** flow described further down — `lerobot-train` then submits the job, uploads a local-only dataset for you, and streams the logs.
|
||||
|
||||
To run the training manually use this command:
|
||||
To run the training use this command:
|
||||
|
||||
<hfoptions id="train_with_hf_jobs">
|
||||
<hfoption id="Command">
|
||||
@@ -607,51 +591,6 @@ Once the training is started you can go to [Jobs](https://huggingface.co/setting
|
||||
|
||||
After training the model will be pushed to hub and you can use it as any other model with LeRobot.
|
||||
|
||||
#### Train on HF Jobs via `--job.target` (integrated CLI)
|
||||
|
||||
`lerobot-train` runs locally by default. To run on a HuggingFace GPU without constructing the Docker command yourself, pass `--job.target` with a hardware flavor name:
|
||||
|
||||
```bash
|
||||
lerobot-train \
|
||||
--dataset.repo_id=${HF_USER}/so101_test \
|
||||
--policy.type=act \
|
||||
--policy.repo_id=${HF_USER}/my_policy \
|
||||
--job.target=a10g-small
|
||||
```
|
||||
|
||||
List available flavors and pricing with `hf jobs hardware`. The run streams its logs to your terminal; press Ctrl-C to detach (the job keeps running in the cloud). Re-attach or cancel with:
|
||||
|
||||
```bash
|
||||
hf jobs logs <job-id>
|
||||
hf jobs cancel <job-id>
|
||||
```
|
||||
|
||||
If your dataset exists only locally (not yet on the Hub), it is automatically pushed to a **private** Hub repo so the job can download it by `repo_id` (nothing is made public). The trained model is pushed to the model repo at the end of the run. To also push every intermediate checkpoint to the Hub as it is saved (so you can monitor progress mid-run), add `--save_checkpoint_to_hub=true` — this requires a runtime image that includes this feature.
|
||||
|
||||
Every job (and any dataset pushed by the run) is tagged `lerobot` so it's easy to find on the Hub. Add your own with `--job.tags '["my-tag"]'`.
|
||||
|
||||
By default the job is capped at `2d` (48h) of wall-clock. Override it with an HF Jobs duration string, e.g. `--job.timeout=4h` to fail faster or `--job.timeout=7d` for a longer run.
|
||||
|
||||
> **Note:** the model repo is created up front (it holds the staged training config the job runs from). If a run fails before the model is pushed, that repo is left on the Hub so you can inspect it — it is not deleted automatically, so repeated failures can leave empty repos behind. Remove one with `hf repo delete <repo-id>`.
|
||||
|
||||
**Prerequisites:** run `hf auth login` before submitting. For Weights & Biases integration, run `wandb login` or set `WANDB_API_KEY` on your machine — the key is forwarded to the job automatically.
|
||||
|
||||
**Resuming on a job.** Adding `--job.target` to a resume command runs the resume in the cloud — the same command works locally or remotely. The checkpoint repo is the source of truth, and new checkpoints continue the lineage in the same repo:
|
||||
|
||||
```bash
|
||||
# resume a Hub run on a job (its checkpoints are already on the Hub)
|
||||
lerobot-train --config_path=${HF_USER}/my_policy --resume=true --job.target=a10g-small
|
||||
|
||||
# resume a LOCAL run on a job — the checkpoint is uploaded to a private Hub repo first,
|
||||
# then the job resumes from it (a local-only dataset is uploaded the same way)
|
||||
lerobot-train \
|
||||
--config_path=outputs/train/act_so101_test/checkpoints/last/pretrained_model/train_config.json \
|
||||
--resume=true \
|
||||
--job.target=a10g-small
|
||||
```
|
||||
|
||||
Job settings come from the current command, so override `--job.target`, `--job.timeout`, etc. as needed; for the resumed run to itself be resumable later, keep `--save_checkpoint_to_hub=true`.
|
||||
|
||||
#### Upload policy checkpoints
|
||||
|
||||
Once training is done, upload the latest checkpoint with:
|
||||
@@ -673,8 +612,6 @@ hf upload ${HF_USER}/act_so101_test${CKPT} \
|
||||
|
||||
Use `lerobot-rollout` to deploy a trained policy on your robot. You can choose different strategies depending on your needs:
|
||||
|
||||
The examples below load the model from `--policy.path`. To pin a specific pushed version — useful once `--save_checkpoint_to_hub=true` has committed several checkpoints — add `--policy.pretrained_revision` with a commit hash, branch, or tag. Each pushed checkpoint is tagged with its step (e.g. `--policy.pretrained_revision=010000`), so you can recover a checkpoint by step without looking up its commit sha.
|
||||
|
||||
<hfoptions id="eval">
|
||||
<hfoption id="Base mode (no recording)">
|
||||
```bash
|
||||
|
||||
@@ -117,7 +117,7 @@ lerobot-rollout \
|
||||
--strategy.num_episodes=20 \
|
||||
--policy.path=outputs/pretrain/checkpoints/last/pretrained_model \
|
||||
--robot.type=bi_openarm_follower \
|
||||
--teleop.type=bi_openarm_mini \
|
||||
--teleop.type=openarm_mini \
|
||||
--dataset.repo_id=${HF_USER}/rollout_hil_data \
|
||||
--dataset.single_task="Fold the T-shirt"
|
||||
```
|
||||
|
||||
@@ -319,7 +319,7 @@ If you want to dive deeper into this important topic, you can check out the [blo
|
||||
|
||||
#### Troubleshooting:
|
||||
|
||||
- On Linux, the recording control-flow keys (arrow keys, Escape) work on X11, Wayland, and headless/SSH sessions as long as you run the recording from an interactive terminal (keep it focused) — no `$DISPLAY` setup is needed; the letter equivalents `n` / `r` / `q` also work. Note that **keyboard teleoperation of the LeKiwi base** is different: it relies on a global key backend and therefore works only on an X11 session, a Windows desktop, or macOS with Accessibility/Input Monitoring granted — not on Wayland or headless machines. See [pynput limitations](https://pynput.readthedocs.io/en/latest/limitations.html#linux).
|
||||
- On Linux, if the left and right arrow keys and escape key don't have any effect during data recording, make sure you've set the `$DISPLAY` environment variable. See [pynput limitations](https://pynput.readthedocs.io/en/latest/limitations.html#linux).
|
||||
|
||||
## Replay an episode
|
||||
|
||||
|
||||
@@ -44,7 +44,7 @@ lerobot-record \
|
||||
--dataset.num_episodes=5 \
|
||||
--dataset.single_task="Grab the black cube" \
|
||||
--dataset.streaming_encoding=true \
|
||||
# --dataset.rgb_encoder.vcodec=auto \
|
||||
# --dataset.camera_encoder.vcodec=auto \
|
||||
--dataset.encoder_threads=2
|
||||
```
|
||||
|
||||
|
||||
@@ -143,7 +143,7 @@ lerobot-train \
|
||||
--batch_size=4 \
|
||||
--eval.batch_size=1 \
|
||||
--eval.n_episodes=1 \
|
||||
--env_eval_freq=1000
|
||||
--eval_freq=1000
|
||||
```
|
||||
|
||||
## Reproducing published results
|
||||
|
||||
@@ -173,7 +173,7 @@ lerobot-train \
|
||||
--batch_size=4 \
|
||||
--eval.batch_size=1 \
|
||||
--eval.n_episodes=1 \
|
||||
--env_eval_freq=1000
|
||||
--eval_freq=1000
|
||||
```
|
||||
|
||||
## Relationship to LIBERO
|
||||
|
||||
@@ -120,11 +120,11 @@ lerobot-train \
|
||||
--batch_size=4 \
|
||||
--eval.batch_size=1 \
|
||||
--eval.n_episodes=1 \
|
||||
--env_eval_freq=1000
|
||||
--eval_freq=1000
|
||||
```
|
||||
|
||||
## Practical tips
|
||||
|
||||
- Use the one-hot task conditioning for multi-task training (MT10/MT50 conventions) so policies have explicit task context.
|
||||
- Inspect the dataset task descriptions and the `info["is_success"]` keys when writing post-processing or logging so your success metrics line up with the benchmark.
|
||||
- Adjust `batch_size`, `steps`, and `env_eval_freq` to match your compute budget.
|
||||
- Adjust `batch_size`, `steps`, and `eval_freq` to match your compute budget.
|
||||
|
||||
@@ -17,7 +17,7 @@ the paper, see [allenai/molmoact2](https://github.com/allenai/molmoact2).
|
||||
Install LeRobot with the MolmoAct2 optional dependencies:
|
||||
|
||||
```bash
|
||||
uv sync --locked --extra molmoact2
|
||||
pip install -e ".[molmoact2]"
|
||||
```
|
||||
|
||||
To run the models in this repository, you need an NVIDIA GPU. The measurements
|
||||
@@ -46,8 +46,8 @@ The repo has been tested with Ubuntu 22.04.
|
||||
|
||||
To use MolmoAct2 in a LeRobot training config, set:
|
||||
|
||||
```bash
|
||||
--policy.type=molmoact2
|
||||
```python
|
||||
policy.type=molmoact2
|
||||
```
|
||||
|
||||
## Training
|
||||
@@ -103,7 +103,7 @@ accelerate launch \
|
||||
--batch_size=32 \
|
||||
--num_workers=4 \
|
||||
--log_freq=20 \
|
||||
--env_eval_freq=-1 \
|
||||
--eval_freq=-1 \
|
||||
--save_checkpoint=true \
|
||||
--save_freq=2000
|
||||
```
|
||||
@@ -142,7 +142,7 @@ accelerate launch \
|
||||
--batch_size=32 \
|
||||
--num_workers=4 \
|
||||
--log_freq=20 \
|
||||
--env_eval_freq=-1 \
|
||||
--eval_freq=-1 \
|
||||
--save_checkpoint=true \
|
||||
--save_freq=2000
|
||||
```
|
||||
@@ -386,68 +386,6 @@ These results demonstrate MolmoAct2's strong performance across diverse robotic
|
||||
manipulation tasks. To reproduce them, follow the instructions in the LIBERO
|
||||
evaluation section.
|
||||
|
||||
## Hardware Deployment (lerobot-rollout)
|
||||
|
||||
LeRobot-format checkpoints are available on the Hub for direct use with
|
||||
`lerobot-rollout`. Each checkpoint uses specific camera names that must
|
||||
match your robot's camera configuration.
|
||||
|
||||
### Camera naming convention
|
||||
|
||||
Each checkpoint expects specific `observation.images.*` keys.
|
||||
If your robot cameras have different names, use `--rename_map` to map them:
|
||||
|
||||
| Checkpoint | Camera keys | Description |
|
||||
| ----------------------------- | ---------------------- | ------------------------ |
|
||||
| MolmoAct2-LIBERO-LeRobot | `image`, `wrist_image` | LIBERO sim cameras |
|
||||
| MolmoAct2-BimanualYAM-LeRobot | `top`, `left`, `right` | YAM 3-camera setup |
|
||||
| MolmoAct2-DROID-LeRobot | `cam0`, `cam1` | External + wrist |
|
||||
| MolmoAct2-SO100_101-LeRobot | `cam0`, `cam1` | Primary + secondary view |
|
||||
|
||||
Example with an SO-100 robot using top and side cameras:
|
||||
|
||||
```bash
|
||||
lerobot-rollout \
|
||||
--policy.path=lerobot/MolmoAct2-SO100_101-LeRobot \
|
||||
--rename_map='{"observation.images.top": "observation.images.cam0", "observation.images.side": "observation.images.cam1"}' \
|
||||
--robot.type=so100_follower \
|
||||
--robot.port=/dev/ttyACM0 \
|
||||
--robot.cameras='{
|
||||
top: {type: opencv, index_or_path: 0, width: 640, height: 480, fps: 30},
|
||||
side: {type: opencv, index_or_path: 2, width: 640, height: 480, fps: 30}
|
||||
}' \
|
||||
--task="pick up the red cube" --duration=30
|
||||
```
|
||||
|
||||
To use a wrist camera instead, just change the rename mapping:
|
||||
|
||||
```bash
|
||||
--rename_map='{"observation.images.top": "observation.images.cam0", "observation.images.wrist": "observation.images.cam1"}'
|
||||
```
|
||||
|
||||
### Joint frame transform (SO-100/101 zero-shot)
|
||||
|
||||
<Tip warning={true}>
|
||||
The MolmoAct2-SO100_101 checkpoint was trained on data that uses a different
|
||||
joint calibration convention than LeRobot >= 0.5.0. Without a frame
|
||||
correction, the arm may move in the wrong direction.
|
||||
|
||||
This affects both **zero-shot deployment** and **fine-tuning** from the
|
||||
original checkpoint. The pretrained weights expect the old convention, so
|
||||
all joint data (observations and actions) must be transformed to match.
|
||||
|
||||
The converted LeRobot checkpoint (`lerobot/MolmoAct2-SO100_101-LeRobot`)
|
||||
already includes this correction in its processor pipeline. If you convert
|
||||
or fine-tune the checkpoint yourself, set the following in the policy config (`configuration_molmoact2.py`):
|
||||
|
||||
- `joint_signs`: `[1, -1, 1, 1, 1, 1]` (flips shoulder_lift direction)
|
||||
- `joint_offsets`: `[0, 90, 90, 0, 0, 0]` (shifts shoulder_lift and elbow_flex by 90°)
|
||||
|
||||
See the [backward compatibility guide](./backwardcomp) for details on the
|
||||
calibration change.
|
||||
|
||||
</Tip>
|
||||
|
||||
## Differences From the Original Implementation
|
||||
|
||||
This LeRobot port is intended to match MolmoAct2 behavior while using LeRobot's
|
||||
|
||||
@@ -95,7 +95,7 @@ If you want to scale your hyperparameters when using multiple GPUs, you should d
|
||||
accelerate launch --num_processes=2 $(which lerobot-train) \
|
||||
--optimizer.lr=2e-4 \
|
||||
--dataset.repo_id=lerobot/pusht \
|
||||
--policy.type=act
|
||||
--policy=act
|
||||
```
|
||||
|
||||
**Training Steps Scaling:**
|
||||
@@ -110,64 +110,9 @@ accelerate launch --num_processes=2 $(which lerobot-train) \
|
||||
--batch_size=8 \
|
||||
--steps=50000 \
|
||||
--dataset.repo_id=lerobot/pusht \
|
||||
--policy.type=act
|
||||
--policy=act
|
||||
```
|
||||
|
||||
## Training Large Models with FSDP
|
||||
|
||||
DDP replicates the full model on every GPU, so a model that doesn't fit on one GPU won't fit under
|
||||
DDP either. For large models, use **FSDP** (Fully Sharded Data Parallel), which shards parameters,
|
||||
gradients, and optimizer state across GPUs. See the [accelerate FSDP guide](https://huggingface.co/docs/accelerate/usage_guides/fsdp) for background.
|
||||
|
||||
An example on how to launch LeRobot training with FSDP across 4 GPUs (1 machine):
|
||||
|
||||
```bash
|
||||
accelerate launch --config_file fsdp.yaml --num_processes=4 $(which lerobot-train) \
|
||||
--dataset.repo_id=${HF_USER}/my_dataset \
|
||||
--policy.type=<your_policy> \
|
||||
--output_dir=outputs/train/my_policy_fsdp
|
||||
```
|
||||
|
||||
A minimal `fsdp.yaml` (FSDP1; shards params/grads/optimizer — ZeRO-3-equivalent):
|
||||
|
||||
```yaml
|
||||
compute_environment: LOCAL_MACHINE
|
||||
distributed_type: FSDP
|
||||
mixed_precision: bf16
|
||||
num_machines: 1
|
||||
num_processes: 4
|
||||
fsdp_config:
|
||||
fsdp_version: 1
|
||||
fsdp_sharding_strategy: FULL_SHARD # params + grads + optimizer (ZeRO-3)
|
||||
fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
|
||||
fsdp_transformer_layer_cls_to_wrap: <YourTransformerBlock> # repeated block class to shard
|
||||
fsdp_use_orig_params: true # required: optimizer is built pre-prepare
|
||||
fsdp_state_dict_type: FULL_STATE_DICT
|
||||
```
|
||||
|
||||
Set `fsdp_transformer_layer_cls_to_wrap` to your model's repeated transformer-block class so each
|
||||
block is sharded as its own unit. `fsdp_use_orig_params: true` is required because LeRobot builds the
|
||||
optimizer before `accelerator.prepare()`.
|
||||
|
||||
### FSDP checkpoints
|
||||
|
||||
LeRobot gathers the full state dict across all ranks and the main process writes it as a single
|
||||
`model.safetensors`, loadable as usual with `Policy.from_pretrained(...)`. Two things to look out for:
|
||||
|
||||
- **Checkpoints store fp32 weights.** Under mixed precision (`bf16`/`fp16`) FSDP keeps an fp32 master
|
||||
copy, and the checkpoint saves it (~2× the bf16 size on disk) so training can resume consistently
|
||||
with the fp32 optimizer state; `from_pretrained` casts back to the policy dtype on load. FSDP-specific
|
||||
caveat: an fp32 checkpoint is materialized in full precision on the target device _before_ casting,
|
||||
so loading it for inference on a tight GPU can OOM even when the bf16 model would fit — load on CPU
|
||||
first, or cast `model.safetensors` to the deployment dtype offline.
|
||||
- The sharded optimizer state is gathered into a full (world-size-independent) state dict and saved
|
||||
alongside the model in the same `optimizer_state.safetensors` / `optimizer_param_groups.json`
|
||||
format as single-GPU training, so **resume-from-checkpoint is supported** with `--resume=true`.
|
||||
Resume reshards both the model and the optimizer state to the _current_ FSDP topology, so you can
|
||||
resume an FSDP checkpoint on a different number of GPUs. Note that the data sampler is only
|
||||
sample-exact when the world size and batch size match the original run (a warning is logged
|
||||
otherwise); the optimizer/model state itself is unaffected.
|
||||
|
||||
## Notes
|
||||
|
||||
- The `--policy.use_amp` flag in `lerobot-train` is only used when **not** running with accelerate. When using accelerate, mixed precision is controlled by accelerate's configuration.
|
||||
|
||||
@@ -314,7 +314,7 @@ lerobot-train \
|
||||
--steps=30000 \
|
||||
--save_freq=1000 \
|
||||
--log_freq=100 \
|
||||
--env_eval_freq=1000 \
|
||||
--eval_freq=1000 \
|
||||
--policy.type=multi_task_dit \
|
||||
--policy.device=cuda \
|
||||
--policy.horizon=32 \
|
||||
|
||||
@@ -96,7 +96,7 @@ lerobot-train \
|
||||
--policy.type=pi0_fast \
|
||||
--output_dir=./outputs/pi0fast_training \
|
||||
--job_name=pi0fast_training \
|
||||
--policy.pretrained_path=lerobot/pi0fast-base \
|
||||
--policy.pretrained_path=lerobot/pi0_fast_base \
|
||||
--policy.dtype=bfloat16 \
|
||||
--policy.gradient_checkpointing=true \
|
||||
--policy.chunk_size=10 \
|
||||
@@ -187,7 +187,7 @@ lerobot-train \
|
||||
--dataset.repo_id=lerobot/libero \
|
||||
--output_dir=outputs/libero_pi0fast \
|
||||
--job_name=libero_pi0fast \
|
||||
--policy.path=lerobot/pi0fast-base \
|
||||
--policy.path=lerobot/pi0fast_base \
|
||||
--policy.dtype=bfloat16 \
|
||||
--steps=100000 \
|
||||
--save_freq=20000 \
|
||||
|
||||
@@ -1,56 +0,0 @@
|
||||
## Research Paper
|
||||
|
||||
Paper: https://arxiv.org/abs/2603.16666
|
||||
|
||||
## Repository
|
||||
|
||||
Code: https://github.com/yuantianyuan01/FastWAM
|
||||
|
||||
Project page: https://yuantianyuan01.github.io/FastWAM/
|
||||
|
||||
## Citation
|
||||
|
||||
```bibtex
|
||||
@article{yuan2026fastwam,
|
||||
title = {Fast-WAM: Do World Action Models Need Test-time Future Imagination?},
|
||||
author = {Tianyuan Yuan and Zibin Dong and Yicheng Liu and Hang Zhao},
|
||||
journal = {arXiv preprint arXiv:2603.16666},
|
||||
year = {2026},
|
||||
url = {https://arxiv.org/abs/2603.16666}
|
||||
}
|
||||
```
|
||||
|
||||
## Additional Resources
|
||||
|
||||
Base video model: https://huggingface.co/Wan-AI/Wan2.2-TI2V-5B
|
||||
|
||||
Released upstream checkpoints: https://huggingface.co/yuanty/fastwam
|
||||
|
||||
## Results
|
||||
|
||||
Evaluated on LIBERO with [`ZibinDong/fastwam_libero_uncond_2cam224`](https://huggingface.co/ZibinDong/fastwam_libero_uncond_2cam224):
|
||||
|
||||
| Suite | Success rate | n_episodes |
|
||||
| -------------- | -----------: | ---------: |
|
||||
| libero_spatial | 97.6% | 500 |
|
||||
| libero_object | 99.0% | 500 |
|
||||
| libero_goal | 95.0% | 500 |
|
||||
| libero_10 | 94.0% | 500 |
|
||||
| **average** | **96.4%** | 2000 |
|
||||
|
||||
Reproduce: `lerobot-eval --policy.path=ZibinDong/fastwam_libero_uncond_2cam224 --policy.device=cuda --policy.torch_dtype=float32 --policy.n_action_steps=10 --env.type=libero --env.task=libero_spatial --env.observation_height=256 --env.observation_width=256 --eval.batch_size=1 --eval.n_episodes=50 --seed=0 --env.episode_length=300`.
|
||||
|
||||
For LIBERO-10, use `--env.task=libero_10 --env.episode_length=600`:
|
||||
|
||||
```bash
|
||||
lerobot-eval \
|
||||
--policy.path=ZibinDong/fastwam_libero_uncond_2cam224 \
|
||||
--policy.device=cuda \
|
||||
--policy.torch_dtype=float32 \
|
||||
--policy.n_action_steps=10 \
|
||||
--env.type=libero \
|
||||
--env.task=libero_10 --env.observation_height=256 --env.observation_width=256 \
|
||||
--eval.batch_size=1 \
|
||||
--eval.n_episodes=50 \
|
||||
--seed=0 --env.episode_length=600
|
||||
```
|
||||
@@ -161,7 +161,7 @@ lerobot-record \
|
||||
--dataset.private=true \
|
||||
--dataset.streaming_encoding=true \
|
||||
--dataset.encoder_threads=2 \
|
||||
# --dataset.rgb_encoder.vcodec=auto \
|
||||
# --dataset.camera_encoder.vcodec=auto \
|
||||
--display_data=true
|
||||
```
|
||||
|
||||
@@ -203,7 +203,7 @@ lerobot-record \
|
||||
--dataset.private=true \
|
||||
--dataset.streaming_encoding=true \
|
||||
--dataset.encoder_threads=2 \
|
||||
# --dataset.rgb_encoder.vcodec=auto \
|
||||
# --dataset.camera_encoder.vcodec=auto \
|
||||
--display_data=true
|
||||
```
|
||||
|
||||
|
||||
@@ -0,0 +1,250 @@
|
||||
# Remote Inference (lerobot-policy-server)
|
||||
|
||||
Remote inference decouples GPU policy inference from robot control. A `lerobot-policy-server` process runs the policy on a GPU machine; the robot runs `lerobot-rollout --inference.type=remote` as a **weightless edge client** — no policy weights, no GPU, no policy processors on the robot. One GPU server can serve several robots at once, and the remote backend works with every rollout strategy (`base`, `sentry`, `highlight`, `dagger`, `episodic`).
|
||||
|
||||
Use remote inference when:
|
||||
|
||||
- The policy is too large or too slow for the machine attached to the robot (e.g. Pi0/Pi0.5 on a Raspberry Pi or laptop edge).
|
||||
- You want one GPU to serve a fleet of robots running the same policy.
|
||||
- You want to update or restart the inference side without touching the robots.
|
||||
|
||||
<Tip>
|
||||
|
||||
Remote inference requires the `async` extra on **both** sides: `pip install 'lerobot[async]'` (installs `eclipse-zenoh` and `msgpack`). The server additionally needs the extras of the policy it serves (e.g. `lerobot[pi]`, `lerobot[smolvla]`).
|
||||
|
||||
</Tip>
|
||||
|
||||
## Architecture
|
||||
|
||||
```
|
||||
robot (edge, weightless) GPU machine
|
||||
┌───────────────────────────┐ ┌────────────────────────────┐
|
||||
│ lerobot-rollout │ │ lerobot-policy-server │
|
||||
│ --inference.type=remote │ zenoh │ one process = one │
|
||||
│ │ router │ (model, revision, GPU) │
|
||||
│ control loop @ fps │ ┌────────┐ │ │
|
||||
│ └─ pops local action ◄──┼───┤ zenohd ├─────┼─► inference worker thread │
|
||||
│ buffer (chunks) │ └────────┘ │ (round-robin over │
|
||||
│ │ observations ► │ client sessions) │
|
||||
│ network worker thread ───┼──► ◄ action │ │
|
||||
│ (publishes obs, merges │ chunks │ stateless per request │
|
||||
│ chunks into buffer) │ │ │
|
||||
└───────────────────────────┘ └────────────────────────────┘
|
||||
```
|
||||
|
||||
The client keeps a local **action buffer** filled with chunks of future actions, so the control loop never blocks on the network: short network blips are absorbed by the buffer and the robot keeps moving. The client self-clocks — it requests a new chunk whenever the buffer holds less than `--inference.buffer_time_s` seconds of playback.
|
||||
|
||||
The server is **stateless per request**: clients ship their RTC prefixes and a delay hint with every observation, so a server crash or restart loses zero control state and reconnects are trivial. In production both robots and servers _dial out_ to a `zenohd` router (NAT-friendly: nothing on the robot network needs an open inbound port).
|
||||
|
||||
## Quickstart on a LAN (peer mode, no router)
|
||||
|
||||
For a quick test on one network you can skip the router: the server listens directly and the robot connects to it.
|
||||
|
||||
On the GPU machine:
|
||||
|
||||
```bash
|
||||
lerobot-policy-server \
|
||||
--model.repo_or_path=${HF_USER}/my_pi0_policy \
|
||||
--default_task="pick up the cube" \
|
||||
--zenoh.mode=peer \
|
||||
--zenoh.listen_endpoints='["tcp/0.0.0.0:7447"]'
|
||||
```
|
||||
|
||||
Wait for `Policy server up: ...` (the model is downloaded, loaded, and warmed up first).
|
||||
|
||||
On the robot machine (replace `192.168.1.42` with the GPU machine's IP):
|
||||
|
||||
```bash
|
||||
lerobot-rollout \
|
||||
--strategy.type=base \
|
||||
--policy.path=${HF_USER}/my_pi0_policy \
|
||||
--inference.type=remote \
|
||||
--inference.zenoh_mode=peer \
|
||||
--inference.connect_endpoint=tcp/192.168.1.42:7447 \
|
||||
--robot.type=so100_follower \
|
||||
--robot.port=/dev/ttyACM0 \
|
||||
--robot.cameras="{ front: {type: opencv, index_or_path: 0, width: 640, height: 480, fps: 30}}" \
|
||||
--task="pick up the cube" \
|
||||
--duration=60
|
||||
```
|
||||
|
||||
`--policy.path` on the client resolves to a config-only download (no weights): it is used for pre-flight validation and action ordering, and doubles as the default service address. The client's `--policy.path` and `--task` must match the server's `--model.repo_or_path` and `--default_task` — that pair is the namespace the service is published under (see [Troubleshooting](#troubleshooting)).
|
||||
|
||||
## Production deployment (router)
|
||||
|
||||
In production, run a [zenoh router](https://zenoh.io/docs/getting-started/installation/) (`zenohd`) somewhere both sides can reach, and have robots and servers dial out to it:
|
||||
|
||||
```bash
|
||||
zenohd # listens on tcp/0.0.0.0:7447 by default
|
||||
```
|
||||
|
||||
Configure the server with a YAML manifest:
|
||||
|
||||
```yaml
|
||||
# server.yaml
|
||||
model:
|
||||
repo_or_path: lerobot/pi0_towels
|
||||
revision: main
|
||||
dtype: bfloat16 # optional cast after load
|
||||
device: cuda
|
||||
default_task: "fold the towel"
|
||||
serving_mode: auto # shared for verified chunk-stateless policies, exclusive otherwise
|
||||
max_sessions: 5
|
||||
warmup_inferences: 2
|
||||
trained_fps: 30.0
|
||||
rtc:
|
||||
enabled: true
|
||||
execution_horizon: 10
|
||||
max_guidance_weight: 10.0
|
||||
health_port: 9100 # /healthz + /metrics; 0 disables
|
||||
zenoh:
|
||||
mode: client
|
||||
connect_endpoints: ["tcp/router.gpu-cluster.internal:7447"]
|
||||
```
|
||||
|
||||
```bash
|
||||
lerobot-policy-server --manifest server.yaml
|
||||
```
|
||||
|
||||
Everything in the manifest can also be set directly on the CLI (`--model.repo_or_path=...`, `--max_sessions=...`, etc.). One process serves exactly one `(model, revision, dtype, device)` — to serve two models, or one model on two GPUs, run two processes. Dynamic model loading is deliberately unsupported: pre-warmed processes keep capacity planning honest.
|
||||
|
||||
On the robot, only the endpoint changes (the default `--inference.zenoh_mode=client` is already router mode):
|
||||
|
||||
```bash
|
||||
lerobot-rollout \
|
||||
--strategy.type=base \
|
||||
--policy.path=lerobot/pi0_towels \
|
||||
--inference.type=remote \
|
||||
--inference.connect_endpoint=tcp/router.gpu-cluster.internal:7447 \
|
||||
--robot.type=so100_follower \
|
||||
--robot.port=/dev/ttyACM0 \
|
||||
--robot.cameras="{ front: {type: opencv, index_or_path: 0, width: 640, height: 480, fps: 30}}" \
|
||||
--task="fold the towel" \
|
||||
--duration=600
|
||||
```
|
||||
|
||||
### TLS / mTLS
|
||||
|
||||
For traffic that leaves a trusted network, terminate TLS at the router and give both sides client certificates (all three PEM paths are required together):
|
||||
|
||||
```yaml
|
||||
# server.yaml (zenoh section)
|
||||
zenoh:
|
||||
mode: client
|
||||
connect_endpoints: ["tls/router.gpu-cluster.internal:7447"]
|
||||
tls_root_ca_certificate: /etc/lerobot/ca.pem
|
||||
tls_connect_certificate: /etc/lerobot/server.pem
|
||||
tls_connect_private_key: /etc/lerobot/server.key
|
||||
```
|
||||
|
||||
On the robot the equivalent flags are `--inference.tls_ca`, `--inference.tls_cert`, and `--inference.tls_key`, with `--inference.connect_endpoint=tls/...`.
|
||||
|
||||
<Tip>
|
||||
|
||||
Multicast scouting is always disabled: discovery is configuration, not protocol magic. If nothing connects, check the endpoints — there is no fallback discovery mechanism.
|
||||
|
||||
</Tip>
|
||||
|
||||
## RTC over the network
|
||||
|
||||
The remote engine reuses the [Real-Time Chunking](./rtc) machinery: the client keeps the chunk leftover and latency tracking locally and ships an action prefix plus a delay hint with every observation; the server runs prefix-conditioned chunk generation. This gives the same smooth chunk-to-chunk transitions as local RTC, with network latency folded into the delay computation.
|
||||
|
||||
RTC is enabled by default on both sides (`rtc.enabled: true`). Tune it from the client:
|
||||
|
||||
```bash
|
||||
lerobot-rollout \
|
||||
... \
|
||||
--inference.type=remote \
|
||||
--inference.rtc.execution_horizon=10 \
|
||||
--inference.rtc.max_guidance_weight=10.0
|
||||
```
|
||||
|
||||
If the server or its policy does not support RTC (only `pi0`, `pi05`, and `smolvla` are RTC-capable, and the server manifest must have `rtc.enabled: true`), the session is **downgraded to plain chunk-append** and the client logs:
|
||||
|
||||
```
|
||||
RTC downgraded to chunk-append (server does not support RTC)
|
||||
```
|
||||
|
||||
The robot still runs — chunks are simply appended to the buffer without prefix blending, which can produce visible seams between chunks on slow policies.
|
||||
|
||||
## Fail-safe behavior
|
||||
|
||||
The client runs a fail-safe state machine (`CONNECTING → STREAMING → DEGRADED → STALLED → RECONNECTING → DEAD`). A bad initial deployment fails fast: `lerobot-rollout` aborts before the robot moves if the handshake or validation fails. Once streaming, faults degrade in stages:
|
||||
|
||||
| Condition | Behavior |
|
||||
| -------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| Short network blip / late chunk | The robot rides its action buffer; state goes `DEGRADED` after `--inference.degraded_after_s` (default 1.0 s) without a fresh chunk |
|
||||
| Buffered actions older than `max_action_age_s` | Stale actions are dropped (never executed); default `--inference.max_action_age_s=3.0` |
|
||||
| Buffer runs dry (`STALLED`) | Fallback per `--inference.fallback`: `hold` (default — robot holds its last commanded position), `repeat_last`, or `zero` |
|
||||
| Server liveliness lost / repeated request timeouts | `RECONNECTING`: re-handshake with exponential backoff (`reconnect_initial_backoff_s=0.5` doubling up to `reconnect_max_backoff_s=10.0`) |
|
||||
| Reconnected server runs a different model/revision | Hard refusal (`DEAD`) — the client never executes wrong-model chunks |
|
||||
| Offline longer than `max_offline_s` (default 60 s) | `DEAD`: the engine signals the rollout's shutdown event for a clean stop |
|
||||
|
||||
<Tip warning={true}>
|
||||
|
||||
`--inference.fallback=zero` is required for velocity-controlled robots: for them "send nothing" means "keep the last velocity", so an explicit zero command is the only safe stop. For position-controlled arms the default `hold` is safe.
|
||||
|
||||
</Tip>
|
||||
|
||||
Server restarts are equally graceful: on SIGTERM the server drops its liveliness token first (clients ride their buffers through the drain), finishes the in-flight inference, and exits. Clients reconnect when the replacement comes up.
|
||||
|
||||
## Serving multiple robots
|
||||
|
||||
`max_sessions` caps concurrent clients per server process. A single inference worker thread serializes GPU access and round-robins over sessions with a pending observation; per-client newest-wins mailboxes mean overload degrades into longer cycle times (larger but correct client-side delays), never into queue buildup.
|
||||
|
||||
A rough capacity estimate, keeping ~20% headroom:
|
||||
|
||||
```
|
||||
N_robots ≈ 0.8 / (rate × inference_time)
|
||||
```
|
||||
|
||||
where `rate` is each robot's chunk-request rate in Hz (how often the client's buffer dips below `buffer_time_s`) and `inference_time` is the server's seconds per chunk. For example, at 100 ms per chunk and ~2 chunk requests per second per robot: `N ≈ 0.8 / (2 × 0.1) = 4` robots.
|
||||
|
||||
The actual serving mode is classified per policy family, never inferred:
|
||||
|
||||
- **shared** — verified chunk-stateless policies (`act`, `pi0`, `pi05`, and `smolvla` with `n_obs_steps=1`) serve up to `max_sessions` clients from one policy instance.
|
||||
- **exclusive** — stateful families (diffusion-family policies, `smolvla` with observation history, and any unverified policy) are forced to `max_sessions=1`. Run one server process per robot for these.
|
||||
|
||||
`serving_mode: auto` (the default) resolves this automatically; you may force `exclusive`, but `shared` can never override a stateful classification.
|
||||
|
||||
## Observability
|
||||
|
||||
With `health_port` set (default 9100), the server exposes:
|
||||
|
||||
- `GET /healthz` — `200 ok` while the inference worker is alive, `503` otherwise. Wire this to your orchestrator's liveness probe.
|
||||
- `GET /metrics` — Prometheus text format: `lerobot_policy_server_requests_total`, `errors_total`, `superseded_total`, `dropped_unknown_client_total`, `sessions_opened_total`, `sessions_closed_total`, `active_sessions`, `server_load`.
|
||||
|
||||
Every inference request also emits one structured audit line on the `lerobot.policy_server.audit` logger:
|
||||
|
||||
```json
|
||||
{
|
||||
"session_id": "9f2c...",
|
||||
"client_uuid": "robot-07",
|
||||
"seq_id": 412,
|
||||
"episode_id": 3,
|
||||
"queue_wait_ms": 1.8,
|
||||
"inference_ms": 93.2,
|
||||
"superseded": 0,
|
||||
"outcome": "ok"
|
||||
}
|
||||
```
|
||||
|
||||
`(session_id, seq_id)` correlates a server-side audit line with the client's request. Set a stable `--inference.client_uuid` per robot (instead of the default fresh UUID per run) for fleet-wide log correlation, and use `--inference.tags` to forward free-form labels in the handshake.
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
**`No policy server answered status query at '@lerobot/...'`**
|
||||
|
||||
The client found no server under the key it dialed. Either the endpoint is wrong (check `--inference.connect_endpoint`, the router, and firewalls), or the **service namespace** does not match. The namespace is the `(model_id, revision, task)` triple: on the client it comes from `--inference.service_model_id` (default: `--policy.path`), `--inference.service_revision` (default: `main`), and `--inference.service_task` (default: the rollout `--task`); on the server from `model.repo_or_path`, `model.revision`, and `service_name` (default: a slug of `default_task`). A robot task string that differs from the server's `default_task` is the most common cause — fix the task, or pin the namespace explicitly with `--inference.service_task` on the client / `service_name` in the manifest.
|
||||
|
||||
**`Action name/order mismatch between server policy and this robot`**
|
||||
|
||||
The hard sync-safety contract: chunk columns map to motors **by order**, so the robot's ordered action keys must exactly equal the policy's `action_feature_names`. This fires when the robot type, motor naming, or rename map differs from the training setup. Use the same robot type (and rename map) the policy was trained with.
|
||||
|
||||
**`RTC requested but this server/policy does not support it — downgrading to chunk-append`**
|
||||
|
||||
Informational, not fatal. Enable RTC in the server manifest (`rtc.enabled: true`) and make sure the policy family is RTC-capable (`pi0`, `pi05`, `smolvla`). Otherwise, expect chunk-append behavior (see [RTC over the network](#rtc-over-the-network)).
|
||||
|
||||
**`server full: N/N sessions active`**
|
||||
|
||||
The session-open was rejected at capacity. Raise `max_sessions` (shared mode only), or point the robot at another server replica — the rejection includes the current load so orchestration can retry elsewhere.
|
||||
@@ -166,7 +166,7 @@ lerobot-train \
|
||||
--output_dir=./outputs/smolvla_robocasa_CloseFridge \
|
||||
--steps=100000 \
|
||||
--batch_size=4 \
|
||||
--env_eval_freq=5000 \
|
||||
--eval_freq=5000 \
|
||||
--eval.batch_size=1 \
|
||||
--eval.n_episodes=5 \
|
||||
--save_freq=10000
|
||||
|
||||
+9
-9
@@ -151,18 +151,18 @@ lerobot-rollout \
|
||||
--device=cuda
|
||||
```
|
||||
|
||||
## How It Differs from the Async Inference in LeRobot
|
||||
## How It Relates to Remote Inference
|
||||
|
||||
Both RTC and [async inference](./async) improve real-time robot control, but they solve different problems.
|
||||
Both RTC and [remote inference](./remote_inference) improve real-time robot control, but they solve different problems.
|
||||
|
||||
| Aspect | Async Inference | RTC |
|
||||
| ------------- | -------------------------------------------------------------------------- | --------------------------------------------------- |
|
||||
| **Problem** | Idle frames while waiting for inference | Discontinuities between action chunks |
|
||||
| **Solution** | Decouple prediction from execution | Guide new chunks to continue smoothly from previous |
|
||||
| **Benefit** | No waiting, continuous action | Smooth transitions, natural motion |
|
||||
| **Best Used** | Async inference is best used with large models with high inference latency | Flow-matching based policies |
|
||||
| Aspect | Remote Inference | RTC |
|
||||
| ------------- | ------------------------------------------------------------------------ | --------------------------------------------------- |
|
||||
| **Problem** | The policy is too large (or too slow) for the edge machine | Discontinuities between action chunks |
|
||||
| **Solution** | Run inference on a GPU server; the robot executes buffered action chunks | Guide new chunks to continue smoothly from previous |
|
||||
| **Benefit** | Weightless edge clients, one GPU serves many robots | Smooth transitions, natural motion |
|
||||
| **Best Used** | Large models with high inference latency, robot fleets | Flow-matching based policies |
|
||||
|
||||
**Use both together** for maximum smoothness and reactivity!
|
||||
**Use both together** (`--inference.type=remote` with `--inference.rtc.execution_horizon=...`) for maximum smoothness and reactivity: the remote engine reuses RTC's chunk-merging machinery client-side while the server runs prefix-conditioned chunk generation.
|
||||
|
||||
## Advanced: Debug Tracking
|
||||
|
||||
|
||||
@@ -122,7 +122,7 @@ The video below shows the sequence of steps for setting the motor ids.
|
||||
|
||||
#### Follower
|
||||
|
||||
Connect the usb cable from your computer and the power supply to the follower arm's controller board. Then, run the following command or run the API example with the port you got from the previous step. You'll also need to give your follower arm a name with the `id` parameter.
|
||||
Connect the usb cable from your computer and the power supply to the follower arm's controller board. Then, run the following command or run the API example with the port you got from the previous step. You'll also need to give your leader arm a name with the `id` parameter.
|
||||
|
||||
<hfoptions id="setup_motors">
|
||||
<hfoption id="Command">
|
||||
|
||||
@@ -17,7 +17,7 @@ This makes `save_episode()` near-instant (the video is already encoded by the ti
|
||||
| Parameter | CLI Flag | Type | Default | Description |
|
||||
| ----------------------- | --------------------------------- | ------------- | ------------- | ----------------------------------------------------------------- |
|
||||
| `streaming_encoding` | `--dataset.streaming_encoding` | `bool` | `True` | Enable real-time encoding during capture |
|
||||
| `vcodec` | `--dataset.rgb_encoder.vcodec` | `str` | `"libsvtav1"` | Video codec. `"auto"` detects best HW encoder |
|
||||
| `vcodec` | `--dataset.camera_encoder.vcodec` | `str` | `"libsvtav1"` | Video codec. `"auto"` detects best HW encoder |
|
||||
| `encoder_threads` | `--dataset.encoder_threads` | `int \| None` | `None` (auto) | Threads per encoder instance. `None` will leave the vcoded decide |
|
||||
| `encoder_queue_maxsize` | `--dataset.encoder_queue_maxsize` | `int` | `30` | Max buffered frames per camera (~1s at 30fps). Consumes RAM |
|
||||
|
||||
@@ -82,15 +82,15 @@ Use HW encoding when:
|
||||
|
||||
### Available HW Encoders
|
||||
|
||||
| Encoder | Platform | Hardware | CLI Value |
|
||||
| ------------------- | ------------- | ------------------------------------------------------------------------------------------------ | ------------------------------------------------ |
|
||||
| `h264_videotoolbox` | macOS | Apple Silicon / Intel | `--dataset.rgb_encoder.vcodec=h264_videotoolbox` |
|
||||
| `hevc_videotoolbox` | macOS | Apple Silicon / Intel | `--dataset.rgb_encoder.vcodec=hevc_videotoolbox` |
|
||||
| `h264_nvenc` | Linux/Windows | NVIDIA GPU | `--dataset.rgb_encoder.vcodec=h264_nvenc` |
|
||||
| `hevc_nvenc` | Linux/Windows | NVIDIA GPU | `--dataset.rgb_encoder.vcodec=hevc_nvenc` |
|
||||
| `h264_vaapi` | Linux | Intel/AMD GPU | `--dataset.rgb_encoder.vcodec=h264_vaapi` |
|
||||
| `h264_qsv` | Linux/Windows | Intel Quick Sync | `--dataset.rgb_encoder.vcodec=h264_qsv` |
|
||||
| `auto` | Any | Probes the system for available HW encoders. Falls back to `libsvtav1` if no HW encoder is found | `--dataset.rgb_encoder.vcodec=auto` |
|
||||
| Encoder | Platform | Hardware | CLI Value |
|
||||
| ------------------- | ------------- | ------------------------------------------------------------------------------------------------ | --------------------------------------------------- |
|
||||
| `h264_videotoolbox` | macOS | Apple Silicon / Intel | `--dataset.camera_encoder.vcodec=h264_videotoolbox` |
|
||||
| `hevc_videotoolbox` | macOS | Apple Silicon / Intel | `--dataset.camera_encoder.vcodec=hevc_videotoolbox` |
|
||||
| `h264_nvenc` | Linux/Windows | NVIDIA GPU | `--dataset.camera_encoder.vcodec=h264_nvenc` |
|
||||
| `hevc_nvenc` | Linux/Windows | NVIDIA GPU | `--dataset.camera_encoder.vcodec=hevc_nvenc` |
|
||||
| `h264_vaapi` | Linux | Intel/AMD GPU | `--dataset.camera_encoder.vcodec=h264_vaapi` |
|
||||
| `h264_qsv` | Linux/Windows | Intel Quick Sync | `--dataset.camera_encoder.vcodec=h264_qsv` |
|
||||
| `auto` | Any | Probes the system for available HW encoders. Falls back to `libsvtav1` if no HW encoder is found | `--dataset.camera_encoder.vcodec=auto` |
|
||||
|
||||
> [!NOTE]
|
||||
> In order to use the HW accelerated encoders you might need to upgrade your GPU drivers.
|
||||
@@ -100,15 +100,15 @@ Use HW encoding when:
|
||||
|
||||
## 5. Troubleshooting
|
||||
|
||||
| Symptom | Likely Cause | Fix |
|
||||
| ------------------------------------------------------------------ | -------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
|
||||
| System freezes or choppy robot movement or Rerun visualization lag | CPU starved (100% load usage) | Close other apps, reduce encoding throughput, lower `encoder_threads`, use `h264`, use `display_data=False`. If the CPU continues to be at 100% then it might be insufficient for your setup, consider `--dataset.streaming_encoding=false` or HW encoding (`--dataset.rgb_encoder.vcodec=auto`) |
|
||||
| "Encoder queue full" warnings or dropped frames in dataset | Encoder can't keep up (Queue overflow) | If CPU is not at 100%: Increase `encoder_threads`, increase `encoder_queue_maxsize` or use HW encoding (`--dataset.rgb_encoder.vcodec=auto`). |
|
||||
| High RAM usage | Queue filling faster than encoding | `encoder_threads` too low or CPU insufficient. Reduce `encoder_queue_maxsize` or use HW encoding |
|
||||
| Large video files | Using HW encoder or H.264 | Expected trade-off. Switch to `libsvtav1` if CPU allows |
|
||||
| `save_episode()` still slow | `streaming_encoding` is `False` | Set `--dataset.streaming_encoding=true` |
|
||||
| Encoder thread crash | Codec not available or invalid settings | Check `vcodec` is installed, try `--dataset.rgb_encoder.vcodec=auto` |
|
||||
| Recorded dataset is missing frames | CPU/GPU starvation or occasional load spikes | If ~5% of frames are missing, your system is likely overloaded — follow the recommendations above. If fewer frames are missing (~2%), they are probably due to occasional transient load spikes (often at startup) and can be considered expected. |
|
||||
| Symptom | Likely Cause | Fix |
|
||||
| ------------------------------------------------------------------ | -------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| System freezes or choppy robot movement or Rerun visualization lag | CPU starved (100% load usage) | Close other apps, reduce encoding throughput, lower `encoder_threads`, use `h264`, use `display_data=False`. If the CPU continues to be at 100% then it might be insufficient for your setup, consider `--dataset.streaming_encoding=false` or HW encoding (`--dataset.camera_encoder.vcodec=auto`) |
|
||||
| "Encoder queue full" warnings or dropped frames in dataset | Encoder can't keep up (Queue overflow) | If CPU is not at 100%: Increase `encoder_threads`, increase `encoder_queue_maxsize` or use HW encoding (`--dataset.camera_encoder.vcodec=auto`). |
|
||||
| High RAM usage | Queue filling faster than encoding | `encoder_threads` too low or CPU insufficient. Reduce `encoder_queue_maxsize` or use HW encoding |
|
||||
| Large video files | Using HW encoder or H.264 | Expected trade-off. Switch to `libsvtav1` if CPU allows |
|
||||
| `save_episode()` still slow | `streaming_encoding` is `False` | Set `--dataset.streaming_encoding=true` |
|
||||
| Encoder thread crash | Codec not available or invalid settings | Check `vcodec` is installed, try `--dataset.camera_encoder.vcodec=auto` |
|
||||
| Recorded dataset is missing frames | CPU/GPU starvation or occasional load spikes | If ~5% of frames are missing, your system is likely overloaded — follow the recommendations above. If fewer frames are missing (~2%), they are probably due to occasional transient load spikes (often at startup) and can be considered expected. |
|
||||
|
||||
## 6. Recommended Configurations
|
||||
|
||||
@@ -146,7 +146,7 @@ On very constrained systems, streaming encoding may compete too heavily with the
|
||||
# 2camsx 640x480x3 @30fps: Requires some tuning.
|
||||
|
||||
# Use H.264, disable streaming, consider batching encoding
|
||||
lerobot-record --dataset.rgb_encoder.vcodec=h264 --dataset.streaming_encoding=false ...
|
||||
lerobot-record --dataset.camera_encoder.vcodec=h264 --dataset.streaming_encoding=false ...
|
||||
```
|
||||
|
||||
## 7. Closing note
|
||||
|
||||
@@ -11,9 +11,8 @@ LeRobot provides several utilities for manipulating datasets:
|
||||
3. **Merge Datasets** - Combine multiple datasets into one. The datasets must have identical features, and episodes are concatenated in the order specified in `repo_ids`
|
||||
4. **Add Features** - Add new features to a dataset
|
||||
5. **Remove Features** - Remove features from a dataset
|
||||
6. **Convert to Video** - Convert image-based datasets to video format for efficient storage (RGB and depth cameras are encoded with separate encoders)
|
||||
7. **Re-encode Videos** - Re-encode an existing video dataset's RGB and/or depth streams with new encoder settings
|
||||
8. **Show the Info of Datasets** - Show the summary of datasets information such as number of episode etc.
|
||||
6. **Convert to Video** - Convert image-based datasets to video format for efficient storage
|
||||
7. **Show the Info of Datasets** - Show the summary of datasets information such as number of episode etc.
|
||||
|
||||
The core implementation is in `lerobot.datasets.dataset_tools`.
|
||||
An example script detailing how to use the tools API is available in `examples/dataset/use_dataset_tools.py`.
|
||||
@@ -118,19 +117,10 @@ lerobot-edit-dataset \
|
||||
--repo_id lerobot/pusht_image \
|
||||
--operation.type convert_image_to_video \
|
||||
--operation.output_dir outputs/pusht_video \
|
||||
--operation.rgb_encoder.vcodec libsvtav1 \
|
||||
--operation.rgb_encoder.pix_fmt yuv420p \
|
||||
--operation.rgb_encoder.g 2 \
|
||||
--operation.rgb_encoder.crf 30
|
||||
|
||||
# Convert a dataset that includes depth maps, customizing the depth encoder
|
||||
lerobot-edit-dataset \
|
||||
--repo_id lerobot/pusht_image \
|
||||
--operation.type convert_image_to_video \
|
||||
--operation.output_dir outputs/pusht_video \
|
||||
--operation.depth_encoder.depth_min 0.01 \
|
||||
--operation.depth_encoder.depth_max 10.0 \
|
||||
--operation.depth_encoder.use_log true
|
||||
--operation.camera_encoder.vcodec libsvtav1 \
|
||||
--operation.camera_encoder.pix_fmt yuv420p \
|
||||
--operation.camera_encoder.g 2 \
|
||||
--operation.camera_encoder.crf 30
|
||||
|
||||
# Convert only specific episodes
|
||||
lerobot-edit-dataset \
|
||||
@@ -157,42 +147,11 @@ lerobot-edit-dataset \
|
||||
**Parameters:**
|
||||
|
||||
- `output_dir`: Custom output directory (optional - by default uses `new_repo_id` or `{repo_id}_video`)
|
||||
- `rgb_encoder`: Video encoder settings applied to RGB cameras — all sub-fields accessible via `--operation.rgb_encoder.<field>`. See [Video Encoding Parameters](./video_encoding_parameters) for more details.
|
||||
- `depth_encoder`: Video encoder settings applied to depth-map cameras (e.g. from an Intel RealSense). In addition to the standard encoder fields it exposes the depth quantization knobs (`depth_min`, `depth_max`, `shift`, `use_log`), accessible via `--operation.depth_encoder.<field>`. These quantization settings are persisted to the dataset metadata so depth can be dequantized back to physical units on load. See the [Depth streams](./video_encoding_parameters#depth-streams) section for details.
|
||||
- `camera_encoder`: Video encoder settings — all sub-fields accessible via `--operation.camera_encoder.<field>. See [Video Encoding Parameters](./video_encoding_parameters) for more details.
|
||||
- `episode_indices`: List of specific episodes to convert (default: all episodes)
|
||||
- `num_workers`: Number of parallel workers for processing (default: 4)
|
||||
|
||||
**Note:** The resulting dataset will be a proper LeRobotDataset with all cameras encoded as videos in the `videos/` directory, with parquet files containing only metadata (no raw image data). Depth-map cameras are detected automatically and routed to the `depth_encoder`, while RGB cameras use the `rgb_encoder`. All episodes, stats, and tasks are preserved.
|
||||
|
||||
#### Re-encode Videos
|
||||
|
||||
Re-encode the videos of an existing video dataset with different encoder settings, without going back to raw frames. RGB videos use the `rgb_encoder` and depth videos use the `depth_encoder`. Provide only the encoder(s) you want to re-encode; the other stream type is left untouched.
|
||||
|
||||
```bash
|
||||
# Re-encode all RGB videos with new settings (saves to lerobot/pusht_reencoded by default)
|
||||
lerobot-edit-dataset \
|
||||
--repo_id lerobot/pusht \
|
||||
--operation.type reencode_videos \
|
||||
--operation.rgb_encoder.vcodec h264 \
|
||||
--operation.rgb_encoder.pix_fmt yuv420p \
|
||||
--operation.rgb_encoder.crf 23
|
||||
|
||||
# Re-encode both RGB and depth videos in a dataset with depth maps
|
||||
lerobot-edit-dataset \
|
||||
--repo_id lerobot/pusht_depth \
|
||||
--operation.type reencode_videos \
|
||||
--operation.rgb_encoder.vcodec h264 \
|
||||
--operation.depth_encoder.crf 50
|
||||
```
|
||||
|
||||
**Parameters:**
|
||||
|
||||
- `rgb_encoder`: Encoder settings applied to every RGB video. Omit to skip re-encoding RGB videos.
|
||||
- `depth_encoder`: Encoder settings applied to every depth video. Omit to skip re-encoding depth videos.
|
||||
- `num_workers`: Number of parallel workers for processing.
|
||||
|
||||
> [!NOTE]
|
||||
> When re-encoding depth videos, the existing depth quantization parameters (`depth_min`, `depth_max`, `shift`, `use_log`) and the `is_depth_map` flag are **preserved** — re-encoding only changes the codec/quality of the stored stream, not how depth is dequantized on load.
|
||||
**Note:** The resulting dataset will be a proper LeRobotDataset with all cameras encoded as videos in the `videos/` directory, with parquet files containing only metadata (no raw image data). All episodes, stats, and tasks are preserved.
|
||||
|
||||
### Show the information of datasets
|
||||
|
||||
|
||||
@@ -2,15 +2,15 @@
|
||||
|
||||
When video storage is enabled, LeRobot stores each camera stream as an **MP4** file instead of saving one image file per timestep. Video encoding compresses across time, which usually cuts dataset size and I/O compared to a pile of PNG, while keeping MP4 — a format every player and loader understands.
|
||||
|
||||
Encoding frames into an MP4 is a full FFmpeg pipeline: choice of encoder, pixel format, GOP/keyframes, quality vs. speed, and optional extra encoder flags. Most of these knobs are user-tunable through `rgb_encoder`, a nested `RGBEncoderConfig` (`lerobot.configs.video.RGBEncoderConfig`) passed through PyAV.
|
||||
Encoding frames into an MP4 is a full FFmpeg pipeline: choice of encoder, pixel format, GOP/keyframes, quality vs. speed, and optional extra encoder flags. Most of these knobs are user-tunable through `camera_encoder`, a nested `VideoEncoderConfig` (`lerobot.configs.video.VideoEncoderConfig`) passed through PyAV.
|
||||
|
||||
You can set these parameters from the CLI with `--dataset.rgb_encoder.<field>` (e.g. with `lerobot-record` or `lerobot-rollout`). The same block applies to every camera video stream in that run.
|
||||
You can set these parameters from the CLI with `--dataset.camera_encoder.<field>` (e.g. with `lerobot-record` or `lerobot-rollout`). The same block applies to every camera video stream in that run.
|
||||
|
||||
<Tip>
|
||||
Video storage must be on for `rgb_encoder` to have any effect —
|
||||
Video storage must be on for `camera_encoder` to have any effect —
|
||||
`use_videos=True` in Python APIs, or `--dataset.video=true` on the CLI (the
|
||||
recording default). With video off, inputs stay as images and `rgb_encoder` is
|
||||
ignored.
|
||||
recording default). With video off, inputs stay as images and `camera_encoder`
|
||||
is ignored.
|
||||
</Tip>
|
||||
|
||||
For details on **when** frames are written vs. encoded (streaming vs. post-episode), queues, and other top-level `--dataset.*` switches, see [Streaming Video Encoding](./streaming_video_encoding). For an encoding-parameter comparison and experiments, see the [video-benchmark Space](https://huggingface.co/spaces/lerobot/video-benchmark).
|
||||
@@ -33,9 +33,9 @@ lerobot-record \
|
||||
--dataset.single_task="Grab the cube" \
|
||||
--dataset.streaming_encoding=true \
|
||||
--dataset.encoder_threads=2 \
|
||||
--dataset.rgb_encoder.vcodec=h264 \
|
||||
--dataset.rgb_encoder.preset=fast \
|
||||
--dataset.rgb_encoder.extra_options={"tune": "film", "profile:v": "high", "bf": 2} \
|
||||
--dataset.camera_encoder.vcodec=h264 \
|
||||
--dataset.camera_encoder.preset=fast \
|
||||
--dataset.camera_encoder.extra_options={"tune": "film", "profile:v": "high", "bf": 2} \
|
||||
--display_data=true
|
||||
```
|
||||
|
||||
@@ -50,7 +50,7 @@ Only override these parameters if you have a specific reason to, and measure the
|
||||
|
||||
</Tip>
|
||||
|
||||
All flags below are prefixed with `--dataset.rgb_encoder.` on the CLI.
|
||||
All flags below are prefixed with `--dataset.camera_encoder.` on the CLI.
|
||||
|
||||
| Parameter | Type | Default | Description |
|
||||
| --------------- | ---------------- | ------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
@@ -65,77 +65,6 @@ All flags below are prefixed with `--dataset.rgb_encoder.` on the CLI.
|
||||
|
||||
---
|
||||
|
||||
## Depth streams
|
||||
|
||||
Depth maps (Intel RealSense, Reachy 2) are stored as their **own video streams** alongside the RGB streams. Raw depth (`uint16` millimetres or `float32` metres) can't survive an 8-bit codec, so LeRobot **quantizes** each map to a 12-bit code (`[0, 4095]`) — logarithmically by default, to match the `1/depth` error profile of depth sensors — then packs it into a high-bit-depth pixel format (`gray12le`) and encodes it with a 12-bit codec.
|
||||
|
||||
```mermaid
|
||||
flowchart LR
|
||||
A["Raw depth (uint16 mm / float32 m)"] --> B["Clip to depth_min, depth_max"]
|
||||
B --> C["Quantize to 12-bit code 0–4095 (log or linear)"]
|
||||
C --> D["Pack into gray12le"]
|
||||
D --> E["Encode video (hevc Main 12)"]
|
||||
E --> F[("MP4 + metadata: depth_min/max, shift, use_log")]
|
||||
F -. "load time (depth_output_unit)" .-> G["Dequantize to mm or m"]
|
||||
|
||||
classDef input fill:#e3f2fd,stroke:#1565c0,color:#0d47a1;
|
||||
classDef encode fill:#ede7f6,stroke:#5e35b1,color:#311b92;
|
||||
classDef store fill:#fff8e1,stroke:#f9a825,color:#e65100;
|
||||
classDef load fill:#e8f5e9,stroke:#2e7d32,color:#1b5e20;
|
||||
|
||||
class A input;
|
||||
class B,C,D,E encode;
|
||||
class F store;
|
||||
class G load;
|
||||
```
|
||||
|
||||
Configure the depth pipeline through a parallel **`depth_encoder`** block (`DepthEncoderConfig`). It shares every `RGBEncoderConfig` field (`vcodec`, `pix_fmt`, `crf`, …) and adds four quantizer knobs, set via `--dataset.depth_encoder.<field>`:
|
||||
|
||||
```bash
|
||||
lerobot-record \
|
||||
... \
|
||||
--dataset.depth_encoder.vcodec=hevc \
|
||||
--dataset.depth_encoder.depth_min=0.05 \
|
||||
--dataset.depth_encoder.depth_max=5.0 \
|
||||
--dataset.depth_encoder.use_log=true
|
||||
```
|
||||
|
||||
| Parameter | Type | Default | Description |
|
||||
| --------------- | ------- | ------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| `vcodec` | `str` | `"hevc"` | HEVC Main 12 (a 12-bit-capable codec, MP4-compatible). |
|
||||
| `extra_options` | `dict` | `{"x265-params": "lossless=1"}` | **Depth defaults to lossless** (exact round-trip); `crf` is ignored. Pass `extra_options={}` and set `crf` for a smaller lossy stream. |
|
||||
| `pix_fmt` | `str` | `"gray12le"` | Single-channel 12-bit pixel format used to carry the quantized codes. |
|
||||
| `depth_min` | `float` | `0.01` | Depth in metres mapped to quantum `0`. Values below are clipped on decode. |
|
||||
| `depth_max` | `float` | `10.0` | Depth in metres mapped to quantum `4095`. Values above are clipped on decode. |
|
||||
| `shift` | `float` | `3.5` | Pre-log offset (metres) used in logarithmic quantization for numerical stability near zero. Must satisfy `depth_min + shift > 0`. |
|
||||
| `use_log` | `bool` | `True` | If `true`, quantize in log-space (recommended for typical depth sensors). Set to `false` for uniform/linear quantization. |
|
||||
|
||||
> [!TIP]
|
||||
> `depth_min`, `depth_max`, and `shift` are always interpreted in **metres**, regardless of the input depth's unit. Inputs are auto-detected: integer arrays (e.g. `uint16` millimetres straight from a RealSense) are treated as millimetres, floating arrays as metres.
|
||||
> Pick `depth_min` / `depth_max` to bracket the actual working range of your sensor — quanta outside that range saturate, which can crush detail at the boundaries.
|
||||
|
||||
Depth features are flagged with `"is_depth_map": true` in `meta/info.json`, and their quantizer settings (`video.depth_min`, `video.depth_max`, `video.shift`, `video.use_log`) are persisted — which is what lets depth be **dequantized back to physical units** on load.
|
||||
|
||||
### Output unit at load time
|
||||
|
||||
`depth_encoder` is a **record-time** concern. The unit that depth maps are dequantized to on _load_ (e.g. during training) is set separately by the read-time flag `--dataset.depth_output_unit`:
|
||||
|
||||
```bash
|
||||
lerobot-train \
|
||||
--dataset.repo_id=<my_username>/<my_dataset_name> \
|
||||
--dataset.depth_output_unit=m \
|
||||
--policy.type=act
|
||||
```
|
||||
|
||||
| Parameter | Type | Default | Description |
|
||||
| ------------------- | ----- | ------- | -------------------------------------------------------------------------------------------- |
|
||||
| `depth_output_unit` | `str` | `"mm"` | Physical unit depth maps are dequantized to on load: `"mm"` (millimetres) or `"m"` (metres). |
|
||||
|
||||
> [!TIP]
|
||||
> This is purely a decode-time presentation choice — it does **not** alter the stored video or its metadata, so the same dataset can be read as `mm` or `m` without re-encoding. It has no effect on datasets without depth cameras.
|
||||
|
||||
---
|
||||
|
||||
## Persistence in dataset metadata
|
||||
|
||||
After the first episode of a video stream is encoded, the encoder configuration is **persisted into the dataset metadata** (`meta/info.json`) under each video feature, alongside the values probed from the file itself. For a video feature `observation.images.<camera>`, the layout in `info.json` is:
|
||||
@@ -153,7 +82,7 @@ After the first episode of a video stream is encoded, the encoder configuration
|
||||
"video.pix_fmt": "yuv420p",
|
||||
"video.fps": 30,
|
||||
"video.channels": 3,
|
||||
"is_depth_map": false,
|
||||
"video.is_depth_map": false,
|
||||
"video.g": 2,
|
||||
"video.crf": 30,
|
||||
"video.preset": "fast",
|
||||
@@ -168,12 +97,12 @@ After the first episode of a video stream is encoded, the encoder configuration
|
||||
|
||||
Two sources contribute to the `info` block:
|
||||
|
||||
- **Stream-derived** (read back from the encoded MP4 with PyAV): `video.height`, `video.width`, `video.codec`, `video.pix_fmt`, `video.fps`, `video.channels`, `is_depth_map`, plus `audio.*` if an audio stream is present.
|
||||
- **Encoder-derived** (taken from `RGBEncoderConfig` or `DepthEncoderConfig`): `video.g`, `video.crf`, `video.preset`, `video.fast_decode`, `video.video_backend`, `video.extra_options`.
|
||||
- **Stream-derived** (read back from the encoded MP4 with PyAV): `video.height`, `video.width`, `video.codec`, `video.pix_fmt`, `video.fps`, `video.channels`, `video.is_depth_map`, plus `audio.*` if an audio stream is present.
|
||||
- **Encoder-derived** (taken from `VideoEncoderConfig`): `video.g`, `video.crf`, `video.preset`, `video.fast_decode`, `video.video_backend`, `video.extra_options`.
|
||||
|
||||
<Tip>
|
||||
This block is populated **once**, from the **first** episode. It assumes every
|
||||
episode in the dataset was encoded with the same `rgb_encoder`. Changing
|
||||
episode in the dataset was encoded with the same `camera_encoder`. Changing
|
||||
encoder settings partway through a recording is not supported — the
|
||||
`info.json` will only reflect the parameters used for the first episode.
|
||||
</Tip>
|
||||
|
||||
@@ -165,7 +165,7 @@ lerobot-train \
|
||||
--output_dir=./outputs/smolvla_vlabench_primitive \
|
||||
--steps=100000 \
|
||||
--batch_size=4 \
|
||||
--env_eval_freq=5000 \
|
||||
--eval_freq=5000 \
|
||||
--eval.batch_size=1 \
|
||||
--eval.n_episodes=1 \
|
||||
--save_freq=10000
|
||||
|
||||
@@ -1,77 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
|
||||
# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
"""Launch ``lerobot-annotate`` on a Hugging Face job (vllm + Qwen3.6-27B VLM).
|
||||
|
||||
Spawns one single-GPU ``h200`` job that:
|
||||
|
||||
1. installs ``lerobot`` from ``main`` plus the annotation extras,
|
||||
2. boots one vllm server with Qwen3.6-27B (dense VLM),
|
||||
3. runs the plan / interjections / vqa modules across the dataset
|
||||
in free-form mode (each episode generates its own subtasks +
|
||||
memory),
|
||||
4. uploads the annotated dataset to ``--new_repo_id`` (when set)
|
||||
or back to ``--repo_id``.
|
||||
|
||||
Usage:
|
||||
|
||||
HF_TOKEN=hf_... uv run python examples/annotations/run_hf_job.py
|
||||
|
||||
Adjust ``CMD`` (dataset, model, hub repo) and ``flavor`` below for your
|
||||
run. For larger datasets, scale to ``h200x4`` and raise
|
||||
``--vlm.parallel_servers`` / ``--vlm.num_gpus`` to match.
|
||||
"""
|
||||
|
||||
import os
|
||||
|
||||
from huggingface_hub import get_token, run_job
|
||||
|
||||
token = os.environ.get("HF_TOKEN") or get_token()
|
||||
if not token:
|
||||
raise RuntimeError("No HF token. Run `huggingface-cli login` or `export HF_TOKEN=hf_...`")
|
||||
|
||||
CMD = (
|
||||
"apt-get update -qq && apt-get install -y -qq git ffmpeg && "
|
||||
"pip install --no-deps "
|
||||
"'lerobot @ git+https://github.com/huggingface/lerobot.git@main' && "
|
||||
"pip install --upgrade-strategy only-if-needed "
|
||||
"datasets pyarrow av jsonlines draccus gymnasium torchcodec mergedeep pyyaml-include toml typing-inspect "
|
||||
"openai && "
|
||||
"export VLLM_MEMORY_PROFILER_ESTIMATE_CUDAGRAPHS=0 && "
|
||||
"export VLLM_VIDEO_BACKEND=pyav && "
|
||||
"lerobot-annotate "
|
||||
"--repo_id=pepijn223/robocasa_pretrain_human300_v4 "
|
||||
"--new_repo_id=pepijn223/robocasa_pretrain_human300_v4_annotated "
|
||||
"--push_to_hub=true "
|
||||
"--vlm.backend=openai "
|
||||
"--vlm.model_id=Qwen/Qwen3.6-27B "
|
||||
"--vlm.num_gpus=1 "
|
||||
'--vlm.serve_command="vllm serve Qwen/Qwen3.6-27B '
|
||||
"--tensor-parallel-size 1 --max-model-len 32768 "
|
||||
'--gpu-memory-utilization 0.8 --uvicorn-log-level warning --port {port}" '
|
||||
"--vlm.serve_ready_timeout_s=1800 "
|
||||
# Qwen3.6 ships with thinking on; annotation wants plain JSON answers.
|
||||
"--vlm.chat_template_kwargs='{\"enable_thinking\": false}'"
|
||||
)
|
||||
|
||||
job = run_job(
|
||||
image="vllm/vllm-openai:latest",
|
||||
command=["bash", "-c", CMD],
|
||||
flavor="h200",
|
||||
secrets={"HF_TOKEN": token},
|
||||
timeout="2h",
|
||||
)
|
||||
print(f"Job URL: {job.url}")
|
||||
print(f"Job ID: {job.id}")
|
||||
@@ -17,7 +17,7 @@
|
||||
import logging
|
||||
import time
|
||||
|
||||
from lerobot.common.control_utils import predict_action
|
||||
from lerobot.common.control_utils import init_keyboard_listener, predict_action
|
||||
from lerobot.datasets import LeRobotDataset
|
||||
from lerobot.policies import make_pre_post_processors
|
||||
from lerobot.policies.act import ACTPolicy
|
||||
@@ -26,7 +26,6 @@ from lerobot.processor import make_default_processors
|
||||
from lerobot.robots.lekiwi import LeKiwiClient, LeKiwiClientConfig
|
||||
from lerobot.utils.constants import ACTION, OBS_STR
|
||||
from lerobot.utils.feature_utils import build_dataset_frame, hw_to_dataset_features
|
||||
from lerobot.utils.keyboard_input import init_keyboard_listener
|
||||
from lerobot.utils.robot_utils import precise_sleep
|
||||
from lerobot.utils.utils import log_say
|
||||
from lerobot.utils.visualization_utils import init_rerun, log_rerun_data
|
||||
|
||||
@@ -14,6 +14,7 @@
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
from lerobot.common.control_utils import init_keyboard_listener
|
||||
from lerobot.datasets import LeRobotDataset
|
||||
from lerobot.processor import make_default_processors
|
||||
from lerobot.robots.lekiwi import LeKiwiClient, LeKiwiClientConfig
|
||||
@@ -22,7 +23,6 @@ from lerobot.teleoperators.keyboard import KeyboardTeleop, KeyboardTeleopConfig
|
||||
from lerobot.teleoperators.so_leader import SO100Leader, SO100LeaderConfig
|
||||
from lerobot.utils.constants import ACTION, OBS_STR
|
||||
from lerobot.utils.feature_utils import hw_to_dataset_features
|
||||
from lerobot.utils.keyboard_input import init_keyboard_listener
|
||||
from lerobot.utils.utils import log_say
|
||||
from lerobot.utils.visualization_utils import init_rerun
|
||||
|
||||
|
||||
@@ -18,7 +18,7 @@ import logging
|
||||
import time
|
||||
|
||||
from lerobot.cameras.opencv import OpenCVCameraConfig
|
||||
from lerobot.common.control_utils import predict_action
|
||||
from lerobot.common.control_utils import init_keyboard_listener, predict_action
|
||||
from lerobot.configs import FeatureType, PolicyFeature
|
||||
from lerobot.datasets import LeRobotDataset, aggregate_pipeline_dataset_features, create_initial_features
|
||||
from lerobot.model.kinematics import RobotKinematics
|
||||
@@ -41,7 +41,6 @@ from lerobot.robots.so_follower.robot_kinematic_processor import (
|
||||
from lerobot.types import RobotAction, RobotObservation
|
||||
from lerobot.utils.constants import ACTION, OBS_STR
|
||||
from lerobot.utils.feature_utils import build_dataset_frame, combine_feature_dicts
|
||||
from lerobot.utils.keyboard_input import init_keyboard_listener
|
||||
from lerobot.utils.robot_utils import precise_sleep
|
||||
from lerobot.utils.utils import log_say
|
||||
from lerobot.utils.visualization_utils import init_rerun, log_rerun_data
|
||||
|
||||
@@ -15,6 +15,7 @@
|
||||
# limitations under the License.
|
||||
|
||||
from lerobot.cameras.opencv import OpenCVCameraConfig
|
||||
from lerobot.common.control_utils import init_keyboard_listener
|
||||
from lerobot.datasets import LeRobotDataset, aggregate_pipeline_dataset_features, create_initial_features
|
||||
from lerobot.model.kinematics import RobotKinematics
|
||||
from lerobot.processor import (
|
||||
@@ -38,7 +39,6 @@ from lerobot.teleoperators.phone.config_phone import PhoneOS
|
||||
from lerobot.teleoperators.phone.phone_processor import MapPhoneActionToRobotAction
|
||||
from lerobot.types import RobotAction, RobotObservation
|
||||
from lerobot.utils.feature_utils import combine_feature_dicts
|
||||
from lerobot.utils.keyboard_input import init_keyboard_listener
|
||||
from lerobot.utils.utils import log_say
|
||||
from lerobot.utils.visualization_utils import init_rerun
|
||||
|
||||
|
||||
@@ -0,0 +1,115 @@
|
||||
# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
# Example manifest for `lerobot-policy-server --manifest server.yaml`.
|
||||
#
|
||||
# One process = one (model, revision, dtype, device) on one GPU. Dynamic
|
||||
# model loading is deliberately unsupported: pre-warmed processes keep
|
||||
# capacity planning honest. Every field below can also be overridden on
|
||||
# the command line via draccus, e.g. --model.repo_or_path=... or
|
||||
# --zenoh.connect_endpoints='["tcp/other-router:7447"]'.
|
||||
#
|
||||
# Field names mirror the dataclasses in src/lerobot/policy_server/manifest.py.
|
||||
|
||||
# --- Which policy this process serves, and where it runs ------------------
|
||||
model:
|
||||
# Hub repo id (org/name) or a local checkpoint directory. Required.
|
||||
repo_or_path: lerobot/pi0_towels
|
||||
# Hub revision: branch, tag, or commit sha.
|
||||
revision: main
|
||||
# Optional torch dtype cast applied after load (e.g. "bfloat16",
|
||||
# "float16"). null keeps the checkpoint's native dtype.
|
||||
dtype: bfloat16
|
||||
# Inference device, e.g. "cuda", "cuda:1", "cpu".
|
||||
device: cuda
|
||||
|
||||
# --- Task namespace --------------------------------------------------------
|
||||
# The task this service is published under. VLA clients may override the
|
||||
# task per session unless `pin_task` is true, in which case session opens
|
||||
# with a different task string are rejected.
|
||||
default_task: "fold the towel"
|
||||
pin_task: false
|
||||
# Optional override for the <task_slug> key segment of the Zenoh prefix
|
||||
# (defaults to a slug of `default_task`).
|
||||
service_name: ""
|
||||
|
||||
# --- Serving mode & capacity ------------------------------------------------
|
||||
# "auto" resolves from the policy classification: shared for verified
|
||||
# chunk-stateless policies (act/pi0/pi05, smolvla with n_obs_steps=1),
|
||||
# exclusive otherwise. Chunk-stateful policies — e.g. diffusion, whose
|
||||
# predict_action_chunk reads select_action-fed queues — are always forced
|
||||
# to "exclusive" (max_sessions=1); "shared" cannot override that.
|
||||
serving_mode: auto
|
||||
|
||||
# Capacity rule-of-thumb: with t = server seconds per inference, r = each
|
||||
# client's request rate (self-clocked to ~1-4 Hz, not the control rate),
|
||||
# H = RTC execution horizon, and dt = control period:
|
||||
# max_sessions ~= min( 0.8 / (r*t), (H*dt/2 - network RTT) / t )
|
||||
# e.g. ACT @ 20 ms, 1 Hz refresh -> ~40 clients/GPU; Pi0 @ 150 ms -> ~5.
|
||||
# Session opens beyond this are rejected with the current load in the
|
||||
# reply, so clients retry another replica.
|
||||
max_sessions: 5
|
||||
|
||||
# Dummy inferences run at startup so the first real request does not pay
|
||||
# for CUDA graph/kernel warmup.
|
||||
warmup_inferences: 2
|
||||
|
||||
# --- FPS contract -----------------------------------------------------------
|
||||
# Control rate the policy was trained at. Clients reporting a different
|
||||
# fps get a warning — or a hard reject when `strict_fps` is true.
|
||||
trained_fps: 30.0
|
||||
strict_fps: false
|
||||
|
||||
# --- Real Time Chunking (RTC) -----------------------------------------------
|
||||
# Global to this process: init_rtc_processor mutates the policy instance,
|
||||
# so RTC is a per-process decision, not per-session. Only rtc-capable
|
||||
# families (pi0/pi05/smolvla) honor it; others are downgraded to plain
|
||||
# chunk-append at session open.
|
||||
rtc:
|
||||
enabled: true
|
||||
# Number of actions executed from each chunk before the next chunk is
|
||||
# blended in (the H in the capacity formula above).
|
||||
execution_horizon: 10
|
||||
|
||||
# --- Housekeeping ------------------------------------------------------------
|
||||
# Sessions with no liveliness token and no traffic for this long are
|
||||
# garbage-collected (belt-and-braces behind liveliness GC).
|
||||
session_idle_timeout_s: 300.0
|
||||
|
||||
# --- Transport ----------------------------------------------------------------
|
||||
# Robots and servers both *dial out* to a zenohd router in production
|
||||
# (mode: client). mode: peer + listen_endpoints supports router-less LAN
|
||||
# and loopback test deployments. Multicast scouting is always disabled:
|
||||
# fleet discovery is configuration, not protocol magic.
|
||||
zenoh:
|
||||
mode: client
|
||||
connect_endpoints:
|
||||
- tcp/router.gpu-cluster.internal:7447
|
||||
listen_endpoints: []
|
||||
# mTLS material (PEM paths). All three are required for tls/ endpoints;
|
||||
# leave them null for plain tcp/ inside a trusted network.
|
||||
# tls_root_ca_certificate: /etc/lerobot/tls/ca.pem
|
||||
# tls_connect_certificate: /etc/lerobot/tls/server.pem
|
||||
# tls_connect_private_key: /etc/lerobot/tls/server.key
|
||||
# Escape hatch: raw JSON5 merged into the zenoh config last.
|
||||
# extra_config_json5: '{transport: {link: {tx: {queue: {size: {data: 4}}}}}}'
|
||||
|
||||
# --- Observability -------------------------------------------------------------
|
||||
# HTTP health + Prometheus metrics port; 0 disables the endpoint.
|
||||
health_port: 9100
|
||||
|
||||
# Optional bounded request/response capture for offline replay.
|
||||
debug:
|
||||
capture_dir: null
|
||||
capture_max: 256
|
||||
@@ -18,7 +18,7 @@ import logging
|
||||
import time
|
||||
|
||||
from lerobot.cameras.opencv import OpenCVCameraConfig
|
||||
from lerobot.common.control_utils import predict_action
|
||||
from lerobot.common.control_utils import init_keyboard_listener, predict_action
|
||||
from lerobot.configs import FeatureType, PolicyFeature
|
||||
from lerobot.datasets import LeRobotDataset, aggregate_pipeline_dataset_features, create_initial_features
|
||||
from lerobot.model.kinematics import RobotKinematics
|
||||
@@ -41,7 +41,6 @@ from lerobot.robots.so_follower.robot_kinematic_processor import (
|
||||
from lerobot.types import RobotAction, RobotObservation
|
||||
from lerobot.utils.constants import ACTION, OBS_STR
|
||||
from lerobot.utils.feature_utils import build_dataset_frame, combine_feature_dicts
|
||||
from lerobot.utils.keyboard_input import init_keyboard_listener
|
||||
from lerobot.utils.robot_utils import precise_sleep
|
||||
from lerobot.utils.utils import log_say
|
||||
from lerobot.utils.visualization_utils import init_rerun, log_rerun_data
|
||||
|
||||
@@ -16,6 +16,7 @@
|
||||
|
||||
|
||||
from lerobot.cameras.opencv import OpenCVCameraConfig
|
||||
from lerobot.common.control_utils import init_keyboard_listener
|
||||
from lerobot.datasets import LeRobotDataset, aggregate_pipeline_dataset_features, create_initial_features
|
||||
from lerobot.model.kinematics import RobotKinematics
|
||||
from lerobot.processor import (
|
||||
@@ -35,7 +36,6 @@ from lerobot.scripts.lerobot_record import record_loop
|
||||
from lerobot.teleoperators.so_leader import SO100Leader, SO100LeaderConfig
|
||||
from lerobot.types import RobotAction, RobotObservation
|
||||
from lerobot.utils.feature_utils import combine_feature_dicts
|
||||
from lerobot.utils.keyboard_input import init_keyboard_listener
|
||||
from lerobot.utils.utils import log_say
|
||||
from lerobot.utils.visualization_utils import init_rerun
|
||||
|
||||
|
||||
@@ -1,17 +0,0 @@
|
||||
from lerobot.async_inference.configs import PolicyServerConfig
|
||||
from lerobot.async_inference.policy_server import serve
|
||||
|
||||
|
||||
def main():
|
||||
host = ... # something like "127.0.0.1" if you're exposing to localhost
|
||||
port = ... # something like 8080
|
||||
|
||||
config = PolicyServerConfig(
|
||||
host=host,
|
||||
port=port,
|
||||
)
|
||||
serve(config)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -1,62 +0,0 @@
|
||||
import threading
|
||||
|
||||
from lerobot.async_inference.configs import RobotClientConfig
|
||||
from lerobot.async_inference.helpers import visualize_action_queue_size
|
||||
from lerobot.async_inference.robot_client import RobotClient
|
||||
from lerobot.cameras.opencv import OpenCVCameraConfig
|
||||
from lerobot.robots.so_follower import SO100FollowerConfig
|
||||
|
||||
|
||||
def main():
|
||||
# these cameras must match the ones expected by the policy - find your cameras with lerobot-find-cameras
|
||||
# check the config.json on the Hub for the policy you are using to see the expected camera specs
|
||||
camera_cfg = {
|
||||
"up": OpenCVCameraConfig(index_or_path=0, width=640, height=480, fps=30),
|
||||
"side": OpenCVCameraConfig(index_or_path=1, width=640, height=480, fps=30),
|
||||
}
|
||||
|
||||
# # find ports using lerobot-find-port
|
||||
follower_port = ... # something like "/dev/tty.usbmodem58760431631"
|
||||
|
||||
# # the robot ids are used the load the right calibration files
|
||||
follower_id = ... # something like "follower_so100"
|
||||
|
||||
robot_cfg = SO100FollowerConfig(port=follower_port, id=follower_id, cameras=camera_cfg)
|
||||
|
||||
server_address = ... # something like "127.0.0.1:8080" if using localhost
|
||||
|
||||
# 3. Create client configuration
|
||||
client_cfg = RobotClientConfig(
|
||||
robot=robot_cfg,
|
||||
server_address=server_address,
|
||||
policy_device="mps",
|
||||
client_device="cpu",
|
||||
policy_type="act",
|
||||
pretrained_name_or_path="<user>/robot_learning_tutorial_act",
|
||||
chunk_size_threshold=0.5, # g
|
||||
actions_per_chunk=50, # make sure this is less than the max actions of the policy
|
||||
)
|
||||
|
||||
# 4. Create and start client
|
||||
client = RobotClient(client_cfg)
|
||||
|
||||
# 5. Provide a textual description of the task
|
||||
task = ...
|
||||
|
||||
if client.start():
|
||||
# Start action receiver thread
|
||||
action_receiver_thread = threading.Thread(target=client.receive_actions, daemon=True)
|
||||
action_receiver_thread.start()
|
||||
|
||||
try:
|
||||
# Run the control loop
|
||||
client.control_loop(task)
|
||||
except KeyboardInterrupt:
|
||||
client.stop()
|
||||
action_receiver_thread.join()
|
||||
# (Optionally) plot the action queue size
|
||||
visualize_action_queue_size(client.action_queue_size)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
+8
-39
@@ -124,7 +124,7 @@ hardware = [
|
||||
"lerobot[deepdiff-dep]",
|
||||
]
|
||||
viz = [
|
||||
"rerun-sdk>=0.24.0,<0.34.0",
|
||||
"rerun-sdk>=0.24.0,<0.27.0",
|
||||
]
|
||||
# ── User-facing composite extras (map to CLI scripts) ─────
|
||||
# lerobot-record, lerobot-replay, lerobot-calibrate, lerobot-teleoperate, etc.
|
||||
@@ -140,14 +140,7 @@ av-dep = ["av>=15.0.0,<16.0.0"]
|
||||
pygame-dep = ["pygame>=2.5.1,<2.7.0"]
|
||||
# NOTE: 0.9.16 links against liburdfdom_sensor.so.4, which is unavailable on Ubuntu 24.04
|
||||
# (noble ships urdfdom 3.x). Cap below 0.9.16 until system urdfdom 4.x is broadly available.
|
||||
#
|
||||
# NOTE: placo pulls in pin (Pinocchio), whose binary wheels dlopen specific cmeel sonames
|
||||
# (liburdfdom_sensor.so.4.0, libtinyxml2.so.10) but declare only `>=` floors on their cmeel
|
||||
# packages. The 2026-05-21 major bumps (cmeel-urdfdom 6.0.0 -> .so.6, cmeel-tinyxml2 11.0.0
|
||||
# -> .so.11) ship newer sonames, so left unpinned the resolver grabs them and `import placo`
|
||||
# fails at load with "liburdfdom_sensor.so.4.0: cannot open shared object file" (see #3755).
|
||||
# There is no cmeel-urdfdom 5.x; <5 selects the 4.x ABI the placo/pin wheels are built against.
|
||||
placo-dep = ["placo>=0.9.6,<0.9.16", "cmeel-urdfdom>=4,<5", "cmeel-tinyxml2<11"]
|
||||
placo-dep = ["placo>=0.9.6,<0.9.16"]
|
||||
transformers-dep = ["transformers>=5.4.0,<5.6.0"]
|
||||
grpcio-dep = ["grpcio>=1.73.1,<2.0.0", "protobuf>=6.31.1,<8.0.0"]
|
||||
accelerate-dep = ["accelerate>=1.14.0,<2.0.0"]
|
||||
@@ -229,32 +222,15 @@ robometer = ["lerobot[transformers-dep]", "lerobot[qwen-vl-utils-dep]", "lerobot
|
||||
topreward = ["lerobot[transformers-dep]"]
|
||||
xvla = ["lerobot[transformers-dep]"]
|
||||
eo1 = ["lerobot[transformers-dep]", "lerobot[qwen-vl-utils-dep]"]
|
||||
fastwam = [
|
||||
"lerobot[transformers-dep]",
|
||||
"lerobot[diffusers-dep]",
|
||||
]
|
||||
hilserl = ["lerobot[transformers-dep]", "lerobot[dataset]", "gym-hil>=0.1.14,<0.2.0", "lerobot[grpcio-dep]", "lerobot[placo-dep]"]
|
||||
vla_jepa = ["lerobot[transformers-dep]", "lerobot[diffusers-dep]", "lerobot[qwen-vl-utils-dep]"]
|
||||
|
||||
# Features
|
||||
async = ["lerobot[grpcio-dep]", "lerobot[matplotlib-dep]"]
|
||||
# Remote inference over Zenoh: lerobot-policy-server + lerobot-rollout --inference.type=remote.
|
||||
# Keep zenohd routers on the same minor version as the Python binding.
|
||||
async = ["eclipse-zenoh>=1.9,<2.0", "msgpack>=1.0.0,<2.0.0"]
|
||||
peft = ["lerobot[transformers-dep]", "lerobot[peft-dep]"]
|
||||
|
||||
# Annotation pipeline (lerobot-annotate). The only backend is ``openai``,
|
||||
# which talks to any OpenAI-compatible server (``vllm serve`` /
|
||||
# ``transformers serve`` / hosted). Distributed runs use Hugging Face Jobs
|
||||
# (see examples/annotations/run_hf_job.py).
|
||||
annotations = [
|
||||
"lerobot[dataset]",
|
||||
"lerobot[transformers-dep]",
|
||||
"openai>=1.40,<2.0",
|
||||
# ``vllm`` is intentionally NOT a hard dep: it pins an older torch, and
|
||||
# uv's single unified lock would then cap ``torch`` for every extra
|
||||
# (e.g. forcing 2.8 while ``torchcodec`` in [dataset] needs 2.11 -> ABI
|
||||
# break in CI). The HF Jobs image (``vllm/vllm-openai``) provides vLLM;
|
||||
# install it locally only if you run your own ``vllm serve``.
|
||||
]
|
||||
|
||||
# Development
|
||||
dev = ["pre-commit>=3.7.0,<5.0.0", "debugpy>=1.8.1,<1.9.0", "lerobot[grpcio-dep]", "grpcio-tools>=1.73.1,<2.0.0", "mypy>=1.19.1", "ruff>=0.14.1", "lerobot[notebook]"]
|
||||
notebook = ["jupyter>=1.0.0,<2.0.0", "ipykernel>=6.0.0,<7.0.0"]
|
||||
@@ -312,7 +288,6 @@ all = [
|
||||
"lerobot[pi]",
|
||||
"lerobot[molmoact2]",
|
||||
"lerobot[smolvla]",
|
||||
"lerobot[fastwam]",
|
||||
# "lerobot[groot]", TODO(Steven): Gr00t requires specific installation instructions for flash-attn
|
||||
"lerobot[xvla]",
|
||||
"lerobot[hilserl]",
|
||||
@@ -350,8 +325,8 @@ lerobot-find-joint-limits="lerobot.scripts.lerobot_find_joint_limits:main"
|
||||
lerobot-imgtransform-viz="lerobot.scripts.lerobot_imgtransform_viz:main"
|
||||
lerobot-edit-dataset="lerobot.scripts.lerobot_edit_dataset:main"
|
||||
lerobot-setup-can="lerobot.scripts.lerobot_setup_can:main"
|
||||
lerobot-annotate="lerobot.scripts.lerobot_annotate:main"
|
||||
lerobot-rollout="lerobot.scripts.lerobot_rollout:main"
|
||||
lerobot-policy-server="lerobot.scripts.lerobot_policy_server:main"
|
||||
|
||||
# ---------------- Tool Configurations ----------------
|
||||
|
||||
@@ -369,7 +344,7 @@ torch = [{ index = "pytorch-cu128", marker = "sys_platform == 'linux'" }]
|
||||
torchvision = [{ index = "pytorch-cu128", marker = "sys_platform == 'linux'" }]
|
||||
|
||||
[tool.setuptools.package-data]
|
||||
lerobot = ["envs/*.json", "annotations/steerable_pipeline/prompts/*.txt"]
|
||||
lerobot = ["envs/*.json"]
|
||||
|
||||
[tool.setuptools.packages.find]
|
||||
where = ["src"]
|
||||
@@ -449,8 +424,7 @@ default.extend-ignore-identifiers-re = [
|
||||
"is_compileable",
|
||||
"ROBOTIS",
|
||||
"OT_VALUE",
|
||||
"VanderBilt",
|
||||
"seperated_timestep",
|
||||
"VanderBilt"
|
||||
]
|
||||
|
||||
# TODO: Uncomment when ready to use
|
||||
@@ -545,11 +519,6 @@ ignore_errors = false
|
||||
# module = "lerobot.rl.*"
|
||||
# ignore_errors = false
|
||||
|
||||
|
||||
# [[tool.mypy.overrides]]
|
||||
# module = "lerobot.async_inference.*"
|
||||
# ignore_errors = false
|
||||
|
||||
[[tool.mypy.overrides]]
|
||||
module = "lerobot.transport.*"
|
||||
ignore_errors = false
|
||||
|
||||
@@ -1,15 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
|
||||
# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
@@ -1,36 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
|
||||
# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
"""Steerable annotation pipeline producing ``language_persistent`` and
|
||||
``language_events`` columns for LeRobot datasets.
|
||||
|
||||
The pipeline is decomposed into three independently runnable modules whose
|
||||
outputs are staged per-episode before a final parquet rewrite:
|
||||
|
||||
- :mod:`.modules.plan_subtasks_memory` (the ``plan`` module) — persistent styles
|
||||
- :mod:`.modules.interjections_and_speech` (the ``interjections`` module) — event styles + speech
|
||||
- :mod:`.modules.general_vqa` (the ``vqa`` module) — event-style VQA pairs
|
||||
"""
|
||||
|
||||
from .config import AnnotationPipelineConfig
|
||||
from .validator import StagingValidator, ValidationReport
|
||||
from .writer import LanguageColumnsWriter
|
||||
|
||||
__all__ = [
|
||||
"AnnotationPipelineConfig",
|
||||
"LanguageColumnsWriter",
|
||||
"StagingValidator",
|
||||
"ValidationReport",
|
||||
]
|
||||
@@ -1,211 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
|
||||
# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from dataclasses import dataclass, field
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
|
||||
|
||||
@dataclass
|
||||
class PlanConfig:
|
||||
"""``plan`` module: subtasks + plan + memory + task augmentation."""
|
||||
|
||||
enabled: bool = True
|
||||
|
||||
# ``task_aug`` rephrasings at t=0 (renderer rotates ${task} among them); 0 disables.
|
||||
n_task_rephrasings: int = 10
|
||||
|
||||
# Derive the task from video instead of episode_task: off / if_short / always.
|
||||
# Affects prompts only; ``meta/tasks.parquet`` is untouched.
|
||||
derive_task_from_video: str = "if_short"
|
||||
derive_task_min_words: int = 3
|
||||
|
||||
# --- Frame input: timestamped contact sheets (always on) ---------------
|
||||
# The subtask describe/segment passes ALWAYS render the episode as
|
||||
# macrodata/refiner-style contact sheets: sampled frames packed into JPEG
|
||||
# grids with each frame's timestamp burned into its corner, so the VLM
|
||||
# cites the exact source time of a boundary directly. This is far cheaper
|
||||
# in vision tokens than one image per frame (≈2× faster subtask generation
|
||||
# in practice), which is why the sampling is dense by default.
|
||||
#
|
||||
# ``frames_per_second`` is the sampling rate: 2.0 = one frame every 0.5s.
|
||||
frames_per_second: float = 2.0
|
||||
# Frame budget per VLM call (= columns × rows × sheets). When a whole
|
||||
# episode sampled at ``frames_per_second`` exceeds this, the episode is
|
||||
# AUTOMATICALLY split into consecutive windows of
|
||||
# ``max_frames_per_prompt`` frames each (one describe→segment call per
|
||||
# window, still at the full ``frames_per_second`` density), and the
|
||||
# per-window spans are merged + stitched into one contiguous cover. So an
|
||||
# episode of any length is always covered at the full sampling density.
|
||||
max_frames_per_prompt: int = 60
|
||||
contact_sheet_columns: int = 5
|
||||
contact_sheet_frames_per_sheet: int = 20
|
||||
contact_sheet_frame_width: int = 224
|
||||
contact_sheet_quality: int = 84
|
||||
|
||||
min_subtask_seconds: float = 1.5
|
||||
plan_max_steps: int = 8
|
||||
|
||||
# Narrate-only grounding pass before segmenting — best defense against subtasks
|
||||
# invented from the task text (+1 VLM call/episode).
|
||||
subtask_describe_first: bool = True
|
||||
|
||||
# Emit ``style="plan"`` rows at each boundary; False = subtasks + memory only.
|
||||
emit_plan: bool = True
|
||||
|
||||
# Emit ``style="memory"`` rows at each boundary; False = subtasks (+ plan) only.
|
||||
# Symmetric counterpart of ``emit_plan``.
|
||||
emit_memory: bool = True
|
||||
|
||||
# (subtask spans are always stitched to a contiguous full-episode cover; not configurable.)
|
||||
|
||||
# Optional EgoMimic-style 5-axis task augmentation; replaces n_task_rephrasings.
|
||||
task_aug_axes: TaskAugAxesConfig = field(default_factory=lambda: TaskAugAxesConfig())
|
||||
|
||||
|
||||
@dataclass
|
||||
class TaskAugAxesConfig:
|
||||
"""5-axis t=0 task augmentation (EgoMimic-style): synonym / omit_arm /
|
||||
omit_orientation / omit_grasp_method / combined. Replaces n_task_rephrasings
|
||||
when enabled; each variant becomes a ``task_aug`` row. Axes with nothing to
|
||||
omit emit fewer entries. Defaults (3+3+2+2+2) match EgoMimic."""
|
||||
|
||||
enabled: bool = False
|
||||
|
||||
synonym_paraphrase: int = 3
|
||||
omit_arm: int = 3
|
||||
omit_orientation: int = 2
|
||||
omit_grasp_method: int = 2
|
||||
combined_omissions: int = 2
|
||||
|
||||
|
||||
@dataclass
|
||||
class InterjectionsConfig:
|
||||
"""``interjections`` module: interjections + paired speech."""
|
||||
|
||||
enabled: bool = True
|
||||
|
||||
# Each emits a paired (interjection, speech) row + a plan refresh at that ts.
|
||||
max_interjections_per_episode: int = 3
|
||||
interjection_min_t: float = 2.0
|
||||
|
||||
# Frame window centered on the timestamp so the VLM sees motion, not one frame.
|
||||
interjection_window_seconds: float = 2.0
|
||||
interjection_window_frames: int = 4
|
||||
|
||||
|
||||
@dataclass
|
||||
class VqaConfig:
|
||||
"""``vqa`` module: general VQA."""
|
||||
|
||||
enabled: bool = True
|
||||
vqa_emission_hz: float = 1.0
|
||||
K: int = 1
|
||||
"""Consecutive frames per emission tick. The VLM grounds on the FIRST frame,
|
||||
so K>1 smears stale labels onto moved frames. Default 1 (no smear)."""
|
||||
question_types: tuple[str, ...] = ("bbox", "keypoint", "count", "attribute", "spatial")
|
||||
|
||||
# True: ground VQA only on --vlm.camera_key (default: every camera).
|
||||
restrict_to_default_camera: bool = False
|
||||
|
||||
|
||||
@dataclass
|
||||
class VlmConfig:
|
||||
"""Shared Qwen-VL client configuration."""
|
||||
|
||||
# Only ``openai`` (OpenAI-compatible vLLM server, auto-spawned when
|
||||
# auto_serve=True); ``stub`` is for tests.
|
||||
backend: str = "openai"
|
||||
model_id: str = "Qwen/Qwen3.6-27B"
|
||||
|
||||
# OpenAI-compatible endpoint; ``EMPTY`` key works for local servers.
|
||||
api_base: str = "http://localhost:8000/v1"
|
||||
api_key: str = "EMPTY"
|
||||
|
||||
# Spawn a server if none answers api_base; False = fail fast on a remote.
|
||||
auto_serve: bool = True
|
||||
serve_port: int = 8000
|
||||
# Override the auto-serve command; ``{port}`` substituted per replica.
|
||||
serve_command: str | None = None
|
||||
|
||||
# Independent servers for round-robin routing (one per GPU). num_gpus=0 = one each.
|
||||
parallel_servers: int = 1
|
||||
num_gpus: int = 0
|
||||
client_concurrency: int = 16
|
||||
serve_ready_timeout_s: float = 600.0
|
||||
|
||||
max_new_tokens: int = 512
|
||||
temperature: float = 0.2
|
||||
|
||||
# Auto-serve context length (None → 32768); other vLLM flags go in serve_command.
|
||||
max_model_len: int | None = None
|
||||
|
||||
# Camera for keyframes; None → first ``observation.images.*`` key.
|
||||
camera_key: str | None = None
|
||||
# Forwarded as extra_body.chat_template_kwargs (e.g. {"enable_thinking": false}).
|
||||
chat_template_kwargs: dict[str, Any] | None = None
|
||||
|
||||
|
||||
@dataclass
|
||||
class ExecutorConfig:
|
||||
"""Executor settings (intra-process episode concurrency; distribution via HF Jobs)."""
|
||||
|
||||
# Episodes processed concurrently per phase; main knob for saturating the servers.
|
||||
episode_parallelism: int = 16
|
||||
|
||||
|
||||
@dataclass
|
||||
class AnnotationPipelineConfig:
|
||||
"""Top-level config for ``lerobot-annotate`` (rewrites data shards in place)."""
|
||||
|
||||
# Hub dataset: download source when ``root`` unset; push target when push_to_hub
|
||||
# is on and ``new_repo_id`` unset.
|
||||
repo_id: str | None = None
|
||||
|
||||
# Separate push target (matches the LeRobot edit tools). Unset → push in place.
|
||||
new_repo_id: str | None = None
|
||||
|
||||
root: Path | None = None
|
||||
|
||||
# Defaults to ``<root>/.annotate_staging/``.
|
||||
staging_dir: Path | None = None
|
||||
|
||||
seed: int = 1729
|
||||
|
||||
plan: PlanConfig = field(default_factory=PlanConfig)
|
||||
interjections: InterjectionsConfig = field(default_factory=InterjectionsConfig)
|
||||
vqa: VqaConfig = field(default_factory=VqaConfig)
|
||||
|
||||
vlm: VlmConfig = field(default_factory=VlmConfig)
|
||||
executor: ExecutorConfig = field(default_factory=ExecutorConfig)
|
||||
|
||||
skip_validation: bool = False
|
||||
only_episodes: tuple[int, ...] | None = None
|
||||
|
||||
# Keyframe decode backend forwarded to ``decode_video_frames``. None →
|
||||
# library default (torchcodec when available, else PyAV). Or pin
|
||||
# ``"torchcodec"`` / ``"pyav"`` explicitly.
|
||||
video_backend: str | None = None
|
||||
|
||||
# Upload to the Hub (new_repo_id if set, else repo_id; one must be set).
|
||||
push_to_hub: bool = False
|
||||
push_private: bool = False
|
||||
push_commit_message: str | None = None
|
||||
|
||||
def resolved_staging_dir(self, root: Path) -> Path:
|
||||
return self.staging_dir if self.staging_dir is not None else root / ".annotate_staging"
|
||||
@@ -1,253 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
|
||||
# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
"""In-process executor that runs the annotation phases.
|
||||
|
||||
The executor runs **six phases** in dependency order:
|
||||
|
||||
phase 1: ``plan`` module (plan + subtasks + memory)
|
||||
phase 2: ``interjections`` module (interjections + speech)
|
||||
phase 3: ``plan`` plan-update pass — re-runs plan emission at every
|
||||
interjection timestamp produced by phase 2
|
||||
phase 4: ``vqa`` module (VQA)
|
||||
phase 5: validator
|
||||
phase 6: writer
|
||||
|
||||
Phase 3 is why the ``plan`` module must be re-entered after the
|
||||
``interjections`` module — to refresh ``plan`` rows at interjection
|
||||
timestamps.
|
||||
|
||||
Distributed execution is provided by Hugging Face Jobs (see
|
||||
``examples/annotations/run_hf_job.py``); the runner inside the job
|
||||
invokes ``lerobot-annotate`` which uses this in-process executor.
|
||||
Episode-level concurrency is controlled by
|
||||
``ExecutorConfig.episode_parallelism``.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
import time
|
||||
from concurrent.futures import ThreadPoolExecutor, as_completed
|
||||
from dataclasses import dataclass
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
|
||||
from .config import AnnotationPipelineConfig
|
||||
from .reader import EpisodeRecord, iter_episodes
|
||||
from .staging import EpisodeStaging
|
||||
from .validator import StagingValidator
|
||||
from .writer import LanguageColumnsWriter
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
@dataclass
|
||||
class PhaseResult:
|
||||
"""Summary of one pipeline phase across all episodes."""
|
||||
|
||||
name: str
|
||||
episodes_processed: int
|
||||
episodes_skipped: int
|
||||
|
||||
|
||||
@dataclass
|
||||
class PipelineRunSummary:
|
||||
"""Aggregated result returned by :meth:`Executor.run`."""
|
||||
|
||||
phases: list[PhaseResult]
|
||||
written_paths: list[Path]
|
||||
validation_report: Any # ValidationReport, kept Any to avoid import cycle
|
||||
|
||||
|
||||
@dataclass
|
||||
class Executor:
|
||||
"""Run all six phases over a dataset root in-process.
|
||||
|
||||
Episode-level concurrency comes from ``ExecutorConfig.episode_parallelism``
|
||||
(a thread pool); cluster-level concurrency comes from running this
|
||||
executor inside a Hugging Face Job. Tests construct the executor
|
||||
directly with stub modules.
|
||||
"""
|
||||
|
||||
config: AnnotationPipelineConfig
|
||||
plan: Any # PlanSubtasksMemoryModule
|
||||
interjections: Any # InterjectionsAndSpeechModule
|
||||
vqa: Any # GeneralVqaModule
|
||||
writer: LanguageColumnsWriter
|
||||
validator: StagingValidator
|
||||
|
||||
def run(self, root: Path) -> PipelineRunSummary:
|
||||
records = list(iter_episodes(root, only_episodes=self.config.only_episodes))
|
||||
n = len(records)
|
||||
if n == 0:
|
||||
raise ValueError(f"No episodes found under {root}/data/")
|
||||
|
||||
print(f"[annotate] {n} episodes total", flush=True)
|
||||
|
||||
staging_dir = self.config.resolved_staging_dir(root)
|
||||
staging_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
phases: list[PhaseResult] = []
|
||||
|
||||
# Phase 1: ``plan`` module (plan + subtasks + memory)
|
||||
phases.append(self._run_module_phase("plan", records, staging_dir, self.plan))
|
||||
# Phase 2: ``interjections`` module (interjections + speech). It
|
||||
# reads the ``plan`` module's subtask rows from the same staging
|
||||
# tree to ground the interjection prompt in the correct local subtask.
|
||||
phases.append(self._run_module_phase("interjections", records, staging_dir, self.interjections))
|
||||
# Phase 3: ``plan`` plan-update pass at interjection timestamps.
|
||||
phases.append(self._run_plan_update_phase(records, staging_dir))
|
||||
# Phase 4: ``vqa`` module (VQA)
|
||||
phases.append(self._run_module_phase("vqa", records, staging_dir, self.vqa))
|
||||
|
||||
print("[annotate] running validator...", flush=True)
|
||||
report = self.validator.validate(records, staging_dir)
|
||||
if not report.ok and not self.config.skip_validation:
|
||||
raise RuntimeError(f"Staging validation failed: {report.summary()}")
|
||||
print(f"[annotate] validator: {report.summary()}", flush=True)
|
||||
|
||||
print(f"[annotate] writing parquet shards into {root}/data/...", flush=True)
|
||||
written = self.writer.write_all(records, staging_dir, root)
|
||||
print(f"[annotate] wrote {len(written)} shard(s); pipeline complete", flush=True)
|
||||
|
||||
# Keep meta/info.json aligned with the parquet schema we just wrote.
|
||||
# Idempotent and additive: existing user metadata is preserved.
|
||||
self._ensure_annotation_metadata_in_info(root)
|
||||
|
||||
return PipelineRunSummary(phases=phases, written_paths=written, validation_report=report)
|
||||
|
||||
@staticmethod
|
||||
def _ensure_annotation_metadata_in_info(root: Path) -> None:
|
||||
"""Write language features and canonical tools to ``meta/info.json``.
|
||||
|
||||
``LanguageColumnsWriter`` adds ``language_persistent`` and
|
||||
``language_events`` to parquet shards. The metadata must advertise
|
||||
those columns too, otherwise non-streaming ``LeRobotDataset`` loads
|
||||
cast against the old schema and fail on the extra parquet columns.
|
||||
"""
|
||||
from lerobot.datasets.io_utils import load_info, write_info # noqa: PLC0415
|
||||
from lerobot.datasets.language import SAY_TOOL_SCHEMA, language_feature_info # noqa: PLC0415
|
||||
|
||||
info_path = root / "meta" / "info.json"
|
||||
if not info_path.exists():
|
||||
return
|
||||
try:
|
||||
info = load_info(root)
|
||||
except Exception as exc: # noqa: BLE001
|
||||
print(f"[annotate] could not read {info_path}: {exc}", flush=True)
|
||||
return
|
||||
|
||||
changed = False
|
||||
|
||||
merged_features = {**info.features, **language_feature_info()}
|
||||
if merged_features != info.features:
|
||||
info.features = merged_features
|
||||
changed = True
|
||||
|
||||
existing = info.tools or []
|
||||
names = {(t.get("function") or {}).get("name") for t in existing if isinstance(t, dict)}
|
||||
if SAY_TOOL_SCHEMA["function"]["name"] not in names:
|
||||
info.tools = [*existing, SAY_TOOL_SCHEMA]
|
||||
changed = True
|
||||
|
||||
if changed:
|
||||
write_info(info, root)
|
||||
print(
|
||||
"[annotate] meta/info.json: "
|
||||
f"language_features={list(language_feature_info())}, "
|
||||
f"tools={[t['function']['name'] for t in (info.tools or [])]}",
|
||||
flush=True,
|
||||
)
|
||||
|
||||
def _run_module_phase(
|
||||
self,
|
||||
name: str,
|
||||
records: list[EpisodeRecord],
|
||||
staging_dir: Path,
|
||||
module: Any,
|
||||
) -> PhaseResult:
|
||||
if not module.enabled:
|
||||
print(f"[annotate] phase={name} skipped (module disabled)", flush=True)
|
||||
return PhaseResult(name=name, episodes_processed=0, episodes_skipped=len(records))
|
||||
n = len(records)
|
||||
parallelism = max(1, min(self.config.executor.episode_parallelism, n))
|
||||
print(
|
||||
f"[annotate] phase={name} starting on {n} episode(s) (parallelism={parallelism})",
|
||||
flush=True,
|
||||
)
|
||||
t0 = time.time()
|
||||
|
||||
def _do(idx_record: tuple[int, EpisodeRecord]) -> tuple[int, int, float]:
|
||||
i, record = idx_record
|
||||
ep_start = time.time()
|
||||
staging = EpisodeStaging(staging_dir, record.episode_index)
|
||||
module.run_episode(record, staging)
|
||||
return i, record.episode_index, time.time() - ep_start
|
||||
|
||||
processed = 0
|
||||
if parallelism == 1:
|
||||
for i, record in enumerate(records, 1):
|
||||
_, ep_idx, elapsed = _do((i, record))
|
||||
processed += 1
|
||||
print(
|
||||
f"[annotate] {name} episode {i}/{n} (idx={ep_idx}) done in {elapsed:.1f}s",
|
||||
flush=True,
|
||||
)
|
||||
else:
|
||||
with ThreadPoolExecutor(max_workers=parallelism) as pool:
|
||||
futures = [pool.submit(_do, (i, r)) for i, r in enumerate(records, 1)]
|
||||
for fut in as_completed(futures):
|
||||
i, ep_idx, elapsed = fut.result()
|
||||
processed += 1
|
||||
print(
|
||||
f"[annotate] {name} episode {processed}/{n} "
|
||||
f"(idx={ep_idx}, submit_order={i}) done in {elapsed:.1f}s",
|
||||
flush=True,
|
||||
)
|
||||
total = time.time() - t0
|
||||
print(f"[annotate] phase={name} complete: {processed}/{n} in {total:.1f}s", flush=True)
|
||||
return PhaseResult(name=name, episodes_processed=processed, episodes_skipped=0)
|
||||
|
||||
def _run_plan_update_phase( # noqa: PLR0915
|
||||
self, records: list[EpisodeRecord], staging_dir: Path
|
||||
) -> PhaseResult:
|
||||
"""Re-emit ``plan`` rows at each timestamp the ``interjections`` module produced.
|
||||
|
||||
The ``plan`` module owns the prompt; the ``interjections`` module
|
||||
produced the timestamps. This phase therefore calls back into the
|
||||
``plan`` module with the interjection timestamps so its existing
|
||||
prompt path is reused.
|
||||
"""
|
||||
if not self.plan.enabled or not self.interjections.enabled:
|
||||
return PhaseResult(name="plan_update", episodes_processed=0, episodes_skipped=len(records))
|
||||
processed = 0
|
||||
for record in records:
|
||||
staging = EpisodeStaging(staging_dir, record.episode_index)
|
||||
interjection_rows = [
|
||||
row for row in staging.read("interjections") if row.get("style") == "interjection"
|
||||
]
|
||||
interjection_times = [float(row["timestamp"]) for row in interjection_rows]
|
||||
interjection_texts = [str(row.get("content") or "") for row in interjection_rows]
|
||||
if interjection_times:
|
||||
self.plan.run_plan_updates(record, staging, interjection_times, interjection_texts)
|
||||
processed += 1
|
||||
# Episodes without any interjections are skipped (no plan refresh
|
||||
# needed); count them so the summary's processed+skipped == total.
|
||||
return PhaseResult(
|
||||
name="plan_update",
|
||||
episodes_processed=processed,
|
||||
episodes_skipped=len(records) - processed,
|
||||
)
|
||||
@@ -1,483 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
|
||||
# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
"""Keyframe extraction for the annotation pipeline.
|
||||
|
||||
Modules attach decoded camera frames to their VLM prompts so the model can
|
||||
ground subtask decomposition, interjection scenarios, and VQA in actual
|
||||
visual content. The pipeline shares one provider across modules and one
|
||||
episode at a time, with a small per-episode cache so multiple modules
|
||||
querying the same timestamp pay decode cost once.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import io
|
||||
import logging
|
||||
import math
|
||||
import threading
|
||||
from collections.abc import Sequence
|
||||
from dataclasses import dataclass, field
|
||||
from pathlib import Path
|
||||
from typing import Any, Protocol
|
||||
|
||||
import PIL.Image
|
||||
import torch
|
||||
|
||||
from lerobot.configs import RGBEncoderConfig
|
||||
from lerobot.datasets.video_utils import decode_video_frames, reencode_video
|
||||
|
||||
from .reader import EpisodeRecord, snap_to_frame
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class FrameProvider(Protocol):
|
||||
"""Decodes camera frames at episode-relative timestamps."""
|
||||
|
||||
@property
|
||||
def camera_keys(self) -> list[str]:
|
||||
"""All ``observation.images.*`` feature keys this provider can decode."""
|
||||
|
||||
def frames_at(
|
||||
self,
|
||||
record: EpisodeRecord,
|
||||
timestamps: list[float],
|
||||
camera_key: str | None = None,
|
||||
) -> list[Any]:
|
||||
"""Return one decoded frame per timestamp from ``camera_key`` (or default).
|
||||
|
||||
Frames are ``torch.Tensor`` (``C, H, W`` uint8) — the shape
|
||||
:func:`lerobot.datasets.video_utils.decode_video_frames` returns.
|
||||
:func:`to_image_blocks` converts them to PIL only at the VLM-message
|
||||
boundary.
|
||||
|
||||
Empty list if the camera is unavailable. ``camera_key=None`` falls back
|
||||
to the provider's default camera so existing single-camera callers
|
||||
(the ``plan`` and ``interjections`` modules) keep working unchanged.
|
||||
"""
|
||||
|
||||
def video_for_episode(
|
||||
self,
|
||||
record: EpisodeRecord,
|
||||
max_frames: int,
|
||||
camera_key: str | None = None,
|
||||
) -> list[Any]:
|
||||
"""Return up to ``max_frames`` decoded frames covering the whole episode.
|
||||
|
||||
Sampling is uniform across the episode duration. Frames are
|
||||
``torch.Tensor`` (``C, H, W`` uint8); :func:`to_video_block` wraps
|
||||
them into one ``{"type":"video", "video":<list>}`` block for a
|
||||
Qwen-VL-compatible model that pools temporally itself. Empty list if
|
||||
no camera available.
|
||||
"""
|
||||
|
||||
|
||||
@dataclass
|
||||
class _NullProvider:
|
||||
"""No-op provider used when the dataset has no video keys or in tests."""
|
||||
|
||||
@property
|
||||
def camera_keys(self) -> list[str]:
|
||||
return []
|
||||
|
||||
def frames_at(
|
||||
self,
|
||||
record: EpisodeRecord,
|
||||
timestamps: list[float],
|
||||
camera_key: str | None = None,
|
||||
) -> list[Any]:
|
||||
return []
|
||||
|
||||
def video_for_episode(
|
||||
self,
|
||||
record: EpisodeRecord,
|
||||
max_frames: int,
|
||||
camera_key: str | None = None,
|
||||
) -> list[Any]:
|
||||
return []
|
||||
|
||||
|
||||
def null_provider() -> FrameProvider:
|
||||
return _NullProvider()
|
||||
|
||||
|
||||
@dataclass
|
||||
class VideoFrameProvider:
|
||||
"""Decodes frames from the dataset's ``observation.images.*`` streams.
|
||||
|
||||
By default the *first* camera key is used for the ``plan`` module
|
||||
(subtask decomposition) and the ``interjections`` module (interjection
|
||||
scenarios) — those prompts care about *what is happening*, not which
|
||||
angle. The ``vqa`` module instead iterates over every camera in
|
||||
:attr:`camera_keys` so each frame's
|
||||
grounded answer (bbox/keypoint/...) is tagged with the camera it was
|
||||
grounded against.
|
||||
|
||||
``camera_key`` overrides the default-camera choice but does not restrict
|
||||
:attr:`camera_keys`. Pass ``camera_key`` explicitly to ``frames_at`` /
|
||||
``video_for_episode`` to read a non-default stream.
|
||||
|
||||
Caches up to ``cache_size`` decoded frames per process to keep
|
||||
co-timestamped ``interjections`` + ``plan`` plan-update calls cheap.
|
||||
"""
|
||||
|
||||
root: Path
|
||||
camera_key: str | None = None
|
||||
tolerance_s: float = 1e-2
|
||||
cache_size: int = 256
|
||||
# Keyframe decode backend forwarded to
|
||||
# :func:`lerobot.datasets.video_utils.decode_video_frames`. ``None``
|
||||
# uses the library default (torchcodec when available, else PyAV).
|
||||
video_backend: str | None = None
|
||||
_meta: Any = field(default=None, init=False, repr=False)
|
||||
_cache: dict = field(default_factory=dict, init=False, repr=False)
|
||||
_camera_keys: list[str] = field(default_factory=list, init=False, repr=False)
|
||||
# Pipeline runs the three module phases under a ThreadPoolExecutor (see
|
||||
# ``ExecutorConfig.episode_parallelism``); guard the dict cache and the
|
||||
# one-shot warn flag against concurrent updates from worker threads.
|
||||
_lock: threading.Lock = field(default_factory=threading.Lock, init=False, repr=False)
|
||||
# Serializes decode_video_frames calls: torchcodec hands out one
|
||||
# ``VideoDecoder`` per file from a process-wide cache, and the decoder
|
||||
# is not safe to drive from multiple threads at once.
|
||||
_decode_lock: threading.Lock = field(default_factory=threading.Lock, init=False, repr=False)
|
||||
_warned_decode_fail: bool = field(default=False, init=False, repr=False)
|
||||
|
||||
def __post_init__(self) -> None:
|
||||
from lerobot.datasets.dataset_metadata import LeRobotDatasetMetadata # noqa: PLC0415
|
||||
|
||||
self._meta = LeRobotDatasetMetadata(repo_id="local", root=self.root)
|
||||
# Only ``video_keys`` are decodable here: the clip/decode paths read
|
||||
# ``videos/<key>/from_timestamp`` from episode metadata, which exists
|
||||
# only for video-stored cameras. Image-stored cameras (also in
|
||||
# ``camera_keys``) would KeyError, so restrict the list — and the
|
||||
# default — to video keys.
|
||||
# Depth cameras are excluded from the annotation pipeline for now.
|
||||
depth_keys = set(self._meta.depth_keys)
|
||||
keys = [key for key in self._meta.video_keys if key not in depth_keys]
|
||||
# Last-resort fallback: if metadata didn't surface any video keys but
|
||||
# the caller explicitly named a camera (``--vlm.camera_key=...``),
|
||||
# trust them — the key is by definition known to exist on the dataset.
|
||||
if not keys and self.camera_key:
|
||||
keys = [self.camera_key]
|
||||
self._camera_keys = keys
|
||||
if self.camera_key is None:
|
||||
self.camera_key = keys[0] if keys else None
|
||||
|
||||
@property
|
||||
def camera_keys(self) -> list[str]:
|
||||
"""All ``observation.images.*`` keys available on this dataset."""
|
||||
return list(self._camera_keys)
|
||||
|
||||
def frames_at(
|
||||
self,
|
||||
record: EpisodeRecord,
|
||||
timestamps: list[float],
|
||||
camera_key: str | None = None,
|
||||
) -> list[Any]:
|
||||
target = camera_key if camera_key is not None else self.camera_key
|
||||
if not timestamps or target is None:
|
||||
return []
|
||||
# Snap each request to the nearest real frame timestamp: callers
|
||||
# sample uniform grids whose points land mid-frame, and
|
||||
# ``decode_video_frames`` rejects queries farther than
|
||||
# ``tolerance_s`` from a decodable frame. Snapping also dedupes
|
||||
# repeat queries through the cache.
|
||||
if record.frame_timestamps:
|
||||
timestamps = [snap_to_frame(float(ts), record.frame_timestamps) for ts in timestamps]
|
||||
|
||||
out: list[Any] = []
|
||||
misses: list[float] = []
|
||||
miss_indices: list[int] = []
|
||||
with self._lock:
|
||||
for i, ts in enumerate(timestamps):
|
||||
key = (record.episode_index, target, round(float(ts), 6))
|
||||
cached = self._cache.get(key)
|
||||
if cached is not None:
|
||||
out.append(cached)
|
||||
else:
|
||||
out.append(None)
|
||||
misses.append(float(ts))
|
||||
miss_indices.append(i)
|
||||
|
||||
if misses:
|
||||
decoded = self._decode(record.episode_index, misses, target)
|
||||
# ``_decode`` returns exactly one frame per requested timestamp,
|
||||
# or an empty list if decoding failed wholesale. A partial list
|
||||
# would mean a frame/timestamp misalignment, so only pair them up
|
||||
# when the counts match (``strict=True`` then guards regressions).
|
||||
if len(decoded) == len(miss_indices):
|
||||
with self._lock:
|
||||
for i, frame in zip(miss_indices, decoded, strict=True):
|
||||
out[i] = frame
|
||||
key = (record.episode_index, target, round(float(timestamps[i]), 6))
|
||||
if len(self._cache) >= self.cache_size:
|
||||
self._cache.pop(next(iter(self._cache)))
|
||||
self._cache[key] = frame
|
||||
# filter out any None left over from decode failures
|
||||
return [frame for frame in out if frame is not None]
|
||||
|
||||
def video_for_episode(
|
||||
self,
|
||||
record: EpisodeRecord,
|
||||
max_frames: int,
|
||||
camera_key: str | None = None,
|
||||
) -> list[Any]:
|
||||
"""Return up to ``max_frames`` frames uniformly sampled across the episode.
|
||||
|
||||
The whole episode duration is covered; the model picks subtask
|
||||
boundaries from the temporal pooling it does internally. Frames are
|
||||
``torch.Tensor`` (see :meth:`frames_at`).
|
||||
"""
|
||||
target = camera_key if camera_key is not None else self.camera_key
|
||||
if max_frames <= 0 or target is None or not record.frame_timestamps:
|
||||
return []
|
||||
n_frames = min(max_frames, len(record.frame_timestamps))
|
||||
if n_frames == len(record.frame_timestamps):
|
||||
timestamps = list(record.frame_timestamps)
|
||||
else:
|
||||
t0 = record.frame_timestamps[0]
|
||||
t_last = record.frame_timestamps[-1]
|
||||
if t_last <= t0:
|
||||
timestamps = [float(t0)] * n_frames
|
||||
else:
|
||||
step = (t_last - t0) / (n_frames - 1) if n_frames > 1 else 0.0
|
||||
timestamps = [float(t0 + i * step) for i in range(n_frames)]
|
||||
return self.frames_at(record, timestamps, camera_key=target)
|
||||
|
||||
def episode_clip_path(self, record: EpisodeRecord, cache_dir: Path) -> Path | None:
|
||||
"""Extract the episode's subclip to ``cache_dir/ep_{idx:06d}.mp4``.
|
||||
|
||||
Returns ``None`` if the dataset has no video tracks or extraction
|
||||
failed. Skips re-extract when the cached clip already exists.
|
||||
Re-encodes to H.264 via
|
||||
:func:`lerobot.datasets.video_utils.reencode_video` so the resulting
|
||||
mp4 is decodable by every downstream video processor — stream-copy
|
||||
would inherit the source codec (often AV1 in modern LeRobot
|
||||
datasets), which vllm's libav build cannot decode.
|
||||
"""
|
||||
if self.camera_key is None:
|
||||
return None
|
||||
cache_dir.mkdir(parents=True, exist_ok=True)
|
||||
out_path = cache_dir / f"ep_{record.episode_index:06d}.mp4"
|
||||
if out_path.exists() and out_path.stat().st_size > 0:
|
||||
return out_path
|
||||
ep = self._meta.episodes[record.episode_index]
|
||||
from_timestamp = float(ep[f"videos/{self.camera_key}/from_timestamp"])
|
||||
to_timestamp = float(ep[f"videos/{self.camera_key}/to_timestamp"])
|
||||
src = self.root / self._meta.get_video_file_path(record.episode_index, self.camera_key)
|
||||
encoder = RGBEncoderConfig(vcodec="h264", pix_fmt="yuv420p", g=None, crf=23, preset="ultrafast")
|
||||
try:
|
||||
reencode_video(
|
||||
src,
|
||||
out_path,
|
||||
video_encoder=encoder,
|
||||
overwrite=True,
|
||||
start_time_s=from_timestamp,
|
||||
end_time_s=to_timestamp,
|
||||
)
|
||||
except Exception:
|
||||
logger.warning(
|
||||
"clip extraction failed for episode %s (%s)", record.episode_index, src, exc_info=True
|
||||
)
|
||||
return None
|
||||
return out_path if out_path.exists() and out_path.stat().st_size > 0 else None
|
||||
|
||||
def _decode(self, episode_index: int, timestamps: list[float], camera_key: str) -> list[Any]:
|
||||
"""Decode ``timestamps`` from the episode's video as ``(C, H, W)`` tensors.
|
||||
|
||||
Delegates to :func:`lerobot.datasets.video_utils.decode_video_frames`
|
||||
(torchcodec when available, PyAV otherwise; ``video_backend`` pins
|
||||
one explicitly). Returns one frame per requested timestamp, or ``[]``
|
||||
if decoding failed — callers treat ``[]`` as "no frames available".
|
||||
"""
|
||||
ep = self._meta.episodes[episode_index]
|
||||
from_timestamp = ep[f"videos/{camera_key}/from_timestamp"]
|
||||
shifted = [from_timestamp + ts for ts in timestamps]
|
||||
video_path = self.root / self._meta.get_video_file_path(episode_index, camera_key)
|
||||
|
||||
try:
|
||||
# The module phases decode under a ThreadPoolExecutor (see
|
||||
# ``ExecutorConfig.episode_parallelism``) but torchcodec's cached
|
||||
# per-file decoder is single-threaded, so serialize decodes on a
|
||||
# dedicated lock. Frame extraction is a small fraction of episode
|
||||
# wall time (VLM calls dominate), so the contention is cheap.
|
||||
with self._decode_lock:
|
||||
# Stacked ``(N, C, H, W)`` uint8 tensor; one row per timestamp.
|
||||
decoded = decode_video_frames(
|
||||
video_path, shifted, self.tolerance_s, backend=self.video_backend, return_uint8=True
|
||||
)
|
||||
return list(decoded)
|
||||
except Exception as exc:
|
||||
# Log loudly the first time so a silent vqa-module no-op (every
|
||||
# prompt skipped because frames_at returned []) is debuggable from
|
||||
# the job log instead of post-hoc parquet inspection. Subsequent
|
||||
# failures stay quiet.
|
||||
with self._lock:
|
||||
already_warned = self._warned_decode_fail
|
||||
if not already_warned:
|
||||
self._warned_decode_fail = True
|
||||
if not already_warned:
|
||||
logger.warning(
|
||||
"VideoFrameProvider._decode failed for episode=%s camera=%s video_path=%s backend=%s: %s",
|
||||
episode_index,
|
||||
camera_key,
|
||||
video_path,
|
||||
self.video_backend,
|
||||
exc,
|
||||
exc_info=exc,
|
||||
)
|
||||
return []
|
||||
|
||||
|
||||
def make_frame_provider(
|
||||
root: Path, camera_key: str | None = None, video_backend: str | None = None
|
||||
) -> FrameProvider:
|
||||
"""Build a :class:`VideoFrameProvider` if videos are present, else null."""
|
||||
try:
|
||||
provider = VideoFrameProvider(root=root, camera_key=camera_key, video_backend=video_backend)
|
||||
except Exception:
|
||||
return null_provider()
|
||||
if provider.camera_key is None:
|
||||
return null_provider()
|
||||
return provider
|
||||
|
||||
|
||||
def _frame_to_pil(frame: Any) -> Any:
|
||||
"""Materialise a decoded frame as a ``PIL.Image`` for the VLM message.
|
||||
|
||||
Frames flow through the provider as ``torch.Tensor`` (``C, H, W`` uint8,
|
||||
straight from :func:`decode_video_frames`); PIL is only created here, at
|
||||
the VLM-message boundary, because the chat backends expect PIL images /
|
||||
data URLs. Non-tensor inputs (e.g. test stubs) pass through untouched.
|
||||
"""
|
||||
if not isinstance(frame, torch.Tensor):
|
||||
return frame
|
||||
array = frame.detach().cpu()
|
||||
if array.ndim == 3 and array.shape[0] in (1, 3):
|
||||
array = array.permute(1, 2, 0) # (C, H, W) -> (H, W, C)
|
||||
if array.shape[-1] == 1:
|
||||
array = array.squeeze(-1)
|
||||
return PIL.Image.fromarray(array.to(torch.uint8).numpy())
|
||||
|
||||
|
||||
def to_image_blocks(frames: list[Any]) -> list[dict[str, Any]]:
|
||||
"""Convert decoded frames to Qwen-VL-compatible image content blocks."""
|
||||
return [{"type": "image", "image": _frame_to_pil(frame)} for frame in frames]
|
||||
|
||||
|
||||
def to_video_block(frames: list[Any]) -> list[dict[str, Any]]:
|
||||
"""Wrap a list of decoded frames as one Qwen-VL video block.
|
||||
|
||||
Returns ``[]`` when the list is empty, so the caller can splat the result
|
||||
into a content array without a separate emptiness check.
|
||||
"""
|
||||
if not frames:
|
||||
return []
|
||||
return [{"type": "video", "video": [_frame_to_pil(frame) for frame in frames]}]
|
||||
|
||||
|
||||
def to_video_url_block(url: str | None, fps: float = 2.0) -> list[dict[str, Any]]:
|
||||
"""Wrap a video file URL as one ``video_url`` block.
|
||||
|
||||
Used by the ``openai`` backend (transformers serve / vllm serve /
|
||||
ktransformers serve), where the server handles frame sampling.
|
||||
Returns ``[]`` when ``url`` is ``None`` so the caller can splat.
|
||||
"""
|
||||
if not url:
|
||||
return []
|
||||
return [{"type": "video_url", "video_url": {"url": url}, "fps": fps}]
|
||||
|
||||
|
||||
def _draw_timestamp_badge(image: PIL.Image.Image, timestamp: float) -> PIL.Image.Image:
|
||||
"""Burn ``timestamp`` (seconds) into the top-left corner of ``image``.
|
||||
|
||||
A solid black badge with white text, so a VLM reading a contact sheet can
|
||||
cite the exact source time of each tile (e.g. ``012.50s``) directly,
|
||||
instead of the caller having to map tile position back to time. Mirrors
|
||||
the macrodata/refiner contact-sheet convention.
|
||||
"""
|
||||
from PIL import ImageDraw, ImageFont
|
||||
|
||||
result = image.copy()
|
||||
draw = ImageDraw.Draw(result)
|
||||
font = ImageFont.load_default()
|
||||
label = f"{timestamp:06.2f}s"
|
||||
left, top, right, bottom = draw.textbbox((0, 0), label, font=font)
|
||||
text_w, text_h = right - left, bottom - top
|
||||
pad = max(3, round(min(image.width, image.height) * 0.018))
|
||||
draw.rectangle((0, 0, text_w + pad * 2, text_h + pad * 2), fill=(0, 0, 0))
|
||||
draw.text((pad - left, pad - top), label, fill=(255, 255, 255), font=font)
|
||||
return result
|
||||
|
||||
|
||||
def to_contact_sheet_blocks(
|
||||
frames: Sequence[Any],
|
||||
timestamps: Sequence[float],
|
||||
*,
|
||||
columns: int = 5,
|
||||
frames_per_sheet: int = 20,
|
||||
frame_width: int = 224,
|
||||
quality: int = 84,
|
||||
) -> list[dict[str, Any]]:
|
||||
"""Pack decoded frames into timestamped JPEG contact-sheet image blocks.
|
||||
|
||||
Each frame is resized to ``frame_width`` wide, stamped with its
|
||||
episode-relative timestamp, and tiled row-major into grids of
|
||||
``frames_per_sheet`` (``columns`` wide). One ``{"type":"image", ...}``
|
||||
block is returned per grid; many frames collapse into a few images, so a
|
||||
long episode's temporal coverage stays dense at a fraction of the vision
|
||||
tokens N separate frames would cost. ``frames`` and ``timestamps`` must be
|
||||
aligned and equal length. Returns ``[]`` for empty input.
|
||||
"""
|
||||
from PIL import Image
|
||||
|
||||
if not frames:
|
||||
return []
|
||||
columns = max(1, columns)
|
||||
frames_per_sheet = max(1, frames_per_sheet)
|
||||
rows_per_sheet = math.ceil(frames_per_sheet / columns)
|
||||
|
||||
tiles: list[PIL.Image.Image] = []
|
||||
for ts, frame in zip(timestamps, frames, strict=False):
|
||||
img = _frame_to_pil(frame)
|
||||
if not isinstance(img, PIL.Image.Image):
|
||||
continue
|
||||
img = img.convert("RGB")
|
||||
if img.width != frame_width:
|
||||
height = max(1, round(img.height * frame_width / img.width))
|
||||
img = img.resize((frame_width, height), resample=Image.Resampling.BILINEAR)
|
||||
tiles.append(_draw_timestamp_badge(img, float(ts)))
|
||||
if not tiles:
|
||||
return []
|
||||
|
||||
blocks: list[dict[str, Any]] = []
|
||||
for start in range(0, len(tiles), frames_per_sheet):
|
||||
chunk = tiles[start : start + frames_per_sheet]
|
||||
cell_w = max(tile.width for tile in chunk)
|
||||
cell_h = max(tile.height for tile in chunk)
|
||||
sheet = Image.new("RGB", (cell_w * columns, cell_h * rows_per_sheet), color=(0, 0, 0))
|
||||
for i, tile in enumerate(chunk):
|
||||
x = (i % columns) * cell_w
|
||||
y = (i // columns) * cell_h
|
||||
sheet.paste(tile, (x, y))
|
||||
# JPEG round-trip at ``quality`` to match the refiner convention and
|
||||
# shrink the wire payload; vision-token count is set by resolution, so
|
||||
# the real saving is the grid packing, not the codec.
|
||||
buf = io.BytesIO()
|
||||
sheet.save(buf, format="JPEG", quality=quality)
|
||||
buf.seek(0)
|
||||
blocks.append({"type": "image", "image": Image.open(buf).convert("RGB")})
|
||||
return blocks
|
||||
@@ -1,25 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
|
||||
# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
from .general_vqa import GeneralVqaModule
|
||||
from .interjections_and_speech import InterjectionsAndSpeechModule
|
||||
from .plan_subtasks_memory import PlanSubtasksMemoryModule
|
||||
|
||||
__all__ = [
|
||||
"GeneralVqaModule",
|
||||
"InterjectionsAndSpeechModule",
|
||||
"PlanSubtasksMemoryModule",
|
||||
]
|
||||
@@ -1,248 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
|
||||
# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
"""``vqa`` module: general VQA at a timed cadence.
|
||||
|
||||
Every ``1/hz`` seconds an emission tick fires; each tick anchors ``K``
|
||||
consecutive frames, and every anchored frame gets its own VQA pair. Each
|
||||
pair is grounded on that single anchor frame — there is no per-pair frame
|
||||
window. For datasets with multiple cameras, every anchored frame produces
|
||||
one ``(vqa, user)`` + ``(vqa, assistant)`` pair *per camera*: each pair is
|
||||
generated against that camera's frame and stamped with the matching
|
||||
``camera`` field on the emitted rows. The resolver disambiguates via
|
||||
``camera=...``; recipes that consume VQA do so through one sub-recipe
|
||||
per camera (see ``recipes/pi05_hirobot.yaml``).
|
||||
|
||||
Within a single (frame, camera) we still emit at most one ``(vqa, user)``
|
||||
and one ``(vqa, assistant)`` row, so the resolver contract stays scalar.
|
||||
|
||||
Question types covered (per the plan's ``vqa`` table): bbox, keypoint,
|
||||
count, attribute, spatial. The assistant's ``content`` is a JSON string
|
||||
whose schema depends on the question type. Malformed JSON triggers one
|
||||
retry inside :meth:`VlmClient.generate_json`.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import logging
|
||||
import random
|
||||
from collections.abc import Sequence
|
||||
from dataclasses import dataclass, field
|
||||
from typing import Any
|
||||
|
||||
from ..config import VqaConfig
|
||||
from ..frames import FrameProvider, null_provider, to_image_blocks
|
||||
from ..prompts import load as load_prompt
|
||||
from ..reader import EpisodeRecord
|
||||
from ..staging import EpisodeStaging
|
||||
from ..validator import classify_vqa_answer
|
||||
from ..vlm_client import VlmClient
|
||||
|
||||
|
||||
def _emission_anchor_indices(frame_timestamps: Sequence[float], hz: float, k: int) -> list[int]:
|
||||
"""Return the relative frame indices to anchor VQA emissions to.
|
||||
|
||||
For each emission tick (every ``1/hz`` seconds), we anchor ``k``
|
||||
consecutive frames starting at the tick. Ticks fall on the nearest
|
||||
available source frame timestamp.
|
||||
"""
|
||||
if hz <= 0 or k <= 0 or not frame_timestamps:
|
||||
return []
|
||||
t0 = frame_timestamps[0]
|
||||
t_last = frame_timestamps[-1]
|
||||
period = 1.0 / hz
|
||||
indices: list[int] = []
|
||||
t = t0
|
||||
while t <= t_last + 1e-9:
|
||||
# find the index of the nearest frame to t
|
||||
nearest_i = min(range(len(frame_timestamps)), key=lambda i: abs(frame_timestamps[i] - t))
|
||||
for offset in range(k):
|
||||
j = nearest_i + offset
|
||||
if j >= len(frame_timestamps):
|
||||
break
|
||||
if not indices or indices[-1] != j:
|
||||
indices.append(j)
|
||||
t += period
|
||||
# dedupe while preserving order
|
||||
seen: set[int] = set()
|
||||
deduped: list[int] = []
|
||||
for i in indices:
|
||||
if i in seen:
|
||||
continue
|
||||
seen.add(i)
|
||||
deduped.append(i)
|
||||
return deduped
|
||||
|
||||
|
||||
@dataclass
|
||||
class GeneralVqaModule:
|
||||
"""Emit grounded VQA pairs at a timed cadence."""
|
||||
|
||||
vlm: VlmClient
|
||||
config: VqaConfig
|
||||
seed: int = 1729
|
||||
frame_provider: FrameProvider = field(default_factory=null_provider)
|
||||
_warned_no_camera: bool = field(default=False, init=False, repr=False)
|
||||
|
||||
@property
|
||||
def enabled(self) -> bool:
|
||||
return self.config.enabled
|
||||
|
||||
def run_episode(self, record: EpisodeRecord, staging: EpisodeStaging) -> None:
|
||||
if not record.frame_timestamps:
|
||||
staging.write("vqa", [])
|
||||
return
|
||||
rng = random.Random(f"{self.seed}:{record.episode_index}:vqa")
|
||||
anchor_idx = _emission_anchor_indices(
|
||||
record.frame_timestamps, self.config.vqa_emission_hz, self.config.K
|
||||
)
|
||||
cameras = self._target_cameras()
|
||||
if not cameras:
|
||||
# No camera available — emit nothing rather than producing
|
||||
# untagged rows that would fail validation. Surface a loud one-
|
||||
# time warning so this is never silently a no-op.
|
||||
if not self._warned_no_camera:
|
||||
logging.getLogger(__name__).warning(
|
||||
"vqa module found no cameras on the frame provider — "
|
||||
"every episode will emit zero VQA rows. Check that the "
|
||||
"dataset declares observation.images.* features in "
|
||||
"meta/info.json; passing --vlm.camera_key=<key> at the "
|
||||
"CLI now also seeds the cameras list as a fallback."
|
||||
)
|
||||
self._warned_no_camera = True
|
||||
staging.write("vqa", [])
|
||||
return
|
||||
|
||||
# Build all messages first (one per (frame, camera)), then issue them
|
||||
# as a single batched generate_json call so the client can fan them
|
||||
# out concurrently.
|
||||
per_call: list[tuple[float, str, str, list[dict[str, Any]]]] = []
|
||||
for idx in anchor_idx:
|
||||
ts = float(record.frame_timestamps[idx])
|
||||
qtype = rng.choice(self.config.question_types)
|
||||
for camera in cameras:
|
||||
messages = self._build_messages(record, qtype, ts, camera)
|
||||
# Skip cameras that decoded to zero frames at this ts: no point
|
||||
# asking the VLM to ground a bbox without an image.
|
||||
if not _has_image_block(messages):
|
||||
continue
|
||||
per_call.append((ts, camera, qtype, messages))
|
||||
|
||||
if not per_call:
|
||||
staging.write("vqa", [])
|
||||
return
|
||||
|
||||
results = self.vlm.generate_json([m for _, _, _, m in per_call])
|
||||
|
||||
rows: list[dict[str, Any]] = []
|
||||
for (ts, camera, _qtype, _messages), result in zip(per_call, results, strict=True):
|
||||
qa = self._postprocess(result)
|
||||
if qa is None:
|
||||
continue
|
||||
question, answer = qa
|
||||
rows.append(
|
||||
{
|
||||
"role": "user",
|
||||
"content": question,
|
||||
"style": "vqa",
|
||||
"timestamp": ts,
|
||||
"camera": camera,
|
||||
"tool_calls": None,
|
||||
}
|
||||
)
|
||||
rows.append(
|
||||
{
|
||||
"role": "assistant",
|
||||
"content": json.dumps(answer, sort_keys=True),
|
||||
"style": "vqa",
|
||||
"timestamp": ts,
|
||||
"camera": camera,
|
||||
"tool_calls": None,
|
||||
}
|
||||
)
|
||||
staging.write("vqa", rows)
|
||||
|
||||
def _target_cameras(self) -> list[str]:
|
||||
"""Return the cameras the ``vqa`` module should iterate per anchored frame.
|
||||
|
||||
Defaults to every camera the provider exposes. Datasets with no
|
||||
cameras (or test/null providers) yield an empty list, which makes
|
||||
``run_episode`` a no-op.
|
||||
|
||||
When ``config.restrict_to_default_camera`` is set, VQA grounds on
|
||||
only the provider's default camera (the single ``--vlm.camera_key``
|
||||
stream), matching the plan / interjection modules so the whole
|
||||
pipeline focuses on one view.
|
||||
"""
|
||||
all_cameras = list(getattr(self.frame_provider, "camera_keys", []) or [])
|
||||
if getattr(self.config, "restrict_to_default_camera", False):
|
||||
default = getattr(self.frame_provider, "camera_key", None)
|
||||
if default and default in all_cameras:
|
||||
return [default]
|
||||
# ``restrict_to_default_camera`` is set but the configured default
|
||||
# isn't one the provider exposes. Returning it anyway would make
|
||||
# ``_decode`` raise a KeyError deep in frame extraction, so warn and
|
||||
# fall through to every available camera instead.
|
||||
if default:
|
||||
logging.getLogger(__name__).warning(
|
||||
"restrict_to_default_camera is set but camera_key=%r is not in the "
|
||||
"provider's cameras %s; grounding VQA on all available cameras instead.",
|
||||
default,
|
||||
all_cameras,
|
||||
)
|
||||
return all_cameras
|
||||
|
||||
def _build_messages(
|
||||
self,
|
||||
record: EpisodeRecord,
|
||||
question_type: str,
|
||||
frame_timestamp: float,
|
||||
camera_key: str,
|
||||
) -> list[dict[str, Any]]:
|
||||
prompt = load_prompt("vqa").format(
|
||||
episode_task=record.episode_task,
|
||||
question_type=question_type,
|
||||
)
|
||||
images = self.frame_provider.frames_at(record, [frame_timestamp], camera_key=camera_key)
|
||||
content = [*to_image_blocks(images), {"type": "text", "text": prompt}]
|
||||
return [{"role": "user", "content": content}]
|
||||
|
||||
def _postprocess(self, result: Any) -> tuple[str, dict[str, Any]] | None:
|
||||
if not isinstance(result, dict):
|
||||
return None
|
||||
question = result.get("question")
|
||||
answer = result.get("answer")
|
||||
if not isinstance(question, str) or not question.strip():
|
||||
return None
|
||||
if not isinstance(answer, dict):
|
||||
return None
|
||||
# The validator will enforce shape; here we just sanity-check that the
|
||||
# answer matches *some* known shape so we can drop garbage early.
|
||||
if classify_vqa_answer(answer) is None:
|
||||
return None
|
||||
return question.strip(), answer
|
||||
|
||||
|
||||
def _has_image_block(messages: list[dict[str, Any]]) -> bool:
|
||||
"""Return True if any user content block is a populated image block."""
|
||||
for msg in messages:
|
||||
content = msg.get("content")
|
||||
if not isinstance(content, list):
|
||||
continue
|
||||
for block in content:
|
||||
if isinstance(block, dict) and block.get("type") == "image":
|
||||
return True
|
||||
return False
|
||||
@@ -1,211 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
|
||||
# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
"""``interjections`` module: interjections + paired speech (EVENT styles + speech atoms).
|
||||
|
||||
Two sub-passes:
|
||||
|
||||
1. At ``t=0``, emit ONLY a speech tool-call atom (acknowledgement of the
|
||||
canonical task). No interjection row — the canonical task is already the
|
||||
user utterance from ``meta/tasks.parquet``.
|
||||
|
||||
2. For mid-episode interruptions, emit a co-timestamped pair:
|
||||
{role:user, style:interjection, content:<text>}
|
||||
speech atom (role:assistant, style:None, tool_calls=[say(...)])
|
||||
Both rows go in ``language_events`` at the same timestamp.
|
||||
|
||||
The ``plan`` module's :meth:`run_plan_updates` reuses this module's
|
||||
interjection timestamps to refresh the ``plan`` row at the same instant.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import random
|
||||
from collections.abc import Sequence
|
||||
from dataclasses import dataclass, field
|
||||
from typing import Any
|
||||
|
||||
from ..config import InterjectionsConfig
|
||||
from ..frames import FrameProvider, null_provider, to_image_blocks
|
||||
from ..prompts import load as load_prompt
|
||||
from ..reader import EpisodeRecord, reconstruct_subtask_spans, snap_to_frame
|
||||
from ..staging import EpisodeStaging
|
||||
from ..vlm_client import VlmClient
|
||||
from ..writer import speech_atom
|
||||
|
||||
|
||||
@dataclass
|
||||
class InterjectionsAndSpeechModule:
|
||||
"""Generate task-start speech and mid-episode interjection/speech pairs."""
|
||||
|
||||
vlm: VlmClient
|
||||
config: InterjectionsConfig
|
||||
seed: int = 1729
|
||||
frame_provider: FrameProvider = field(default_factory=null_provider)
|
||||
|
||||
@property
|
||||
def enabled(self) -> bool:
|
||||
return self.config.enabled
|
||||
|
||||
def run_episode(self, record: EpisodeRecord, staging: EpisodeStaging) -> None:
|
||||
rows: list[dict[str, Any]] = []
|
||||
if record.frame_timestamps:
|
||||
t0 = float(record.frame_timestamps[0])
|
||||
initial = self._initial_speech(record)
|
||||
if initial:
|
||||
rows.append(speech_atom(t0, initial))
|
||||
# Pull the ``plan`` module's subtask spans for this episode so the
|
||||
# interjection prompt can ground itself in the actual current
|
||||
# subtask at each chosen timestamp. The ``plan`` module ran first.
|
||||
episode_end_t = float(record.frame_timestamps[-1]) if record.frame_timestamps else None
|
||||
subtask_spans = reconstruct_subtask_spans(staging.read("plan"), episode_end_t=episode_end_t)
|
||||
rows.extend(self._mid_episode_interjections(record, subtask_spans))
|
||||
staging.write("interjections", rows)
|
||||
|
||||
@staticmethod
|
||||
def _subtask_at(spans: Sequence[dict[str, Any]], t: float) -> str | None:
|
||||
current: str | None = None
|
||||
for span in spans:
|
||||
if float(span["start"]) <= t:
|
||||
current = span.get("text")
|
||||
else:
|
||||
break
|
||||
return current
|
||||
|
||||
def _initial_speech(self, record: EpisodeRecord) -> str | None:
|
||||
prompt = load_prompt("interjections_initial_speech").format(
|
||||
episode_task=record.episode_task,
|
||||
)
|
||||
messages = [{"role": "user", "content": [{"type": "text", "text": prompt}]}]
|
||||
result = self.vlm.generate_json([messages])[0]
|
||||
if isinstance(result, dict) and isinstance(result.get("text"), str):
|
||||
text = result["text"].strip()
|
||||
if text:
|
||||
return text
|
||||
return None
|
||||
|
||||
def _mid_episode_interjections(
|
||||
self,
|
||||
record: EpisodeRecord,
|
||||
subtask_spans: Sequence[dict[str, Any]],
|
||||
) -> list[dict[str, Any]]:
|
||||
"""Generate interjections aligned with the actual demo trajectory.
|
||||
|
||||
Teleop data is frozen — the robot already executed every step in
|
||||
the video. A *counterfactual* interjection like "actually skip
|
||||
the wipe" contradicts what then happens in the video, which is
|
||||
what qwen36moe-10/11 surfaced as low-quality interjections.
|
||||
|
||||
Instead, anchor every interjection at a subtask boundary and
|
||||
write it as a natural user request for the *upcoming* subtask.
|
||||
The robot's visible next behavior IS the interjection's effect,
|
||||
so the training signal stays consistent: interjection text →
|
||||
plan refresh → action stream all line up.
|
||||
"""
|
||||
if self.config.max_interjections_per_episode <= 0:
|
||||
return []
|
||||
if len(subtask_spans) < 2:
|
||||
# Need at least one transition (subtask 0 → subtask 1).
|
||||
return []
|
||||
# Deterministic per-episode RNG so reruns are stable across SLURM jobs.
|
||||
rng = random.Random(f"{self.seed}:{record.episode_index}:interjection")
|
||||
|
||||
# Boundaries: the start time of every subtask except the first
|
||||
# (which is just t0 and is covered by the initial-task speech atom).
|
||||
boundaries: list[tuple[float, str, str]] = []
|
||||
for i in range(1, len(subtask_spans)):
|
||||
ts = float(subtask_spans[i]["start"])
|
||||
if ts < self.config.interjection_min_t:
|
||||
continue
|
||||
prev_text = (subtask_spans[i - 1].get("text") or "").strip()
|
||||
next_text = (subtask_spans[i].get("text") or "").strip()
|
||||
if not next_text:
|
||||
continue
|
||||
boundaries.append((ts, prev_text, next_text))
|
||||
if not boundaries:
|
||||
return []
|
||||
|
||||
n = min(self.config.max_interjections_per_episode, len(boundaries))
|
||||
chosen = sorted(rng.sample(boundaries, n), key=lambda b: b[0])
|
||||
|
||||
out: list[dict[str, Any]] = []
|
||||
for t, prev_subtask, next_subtask in chosen:
|
||||
t_snap = snap_to_frame(t, record.frame_timestamps)
|
||||
# Window straddles the boundary so the VLM sees the end of the
|
||||
# previous subtask and the start of the next one — same
|
||||
# conditioning the policy will see at training time.
|
||||
window_ts = self._window_timestamps(t_snap, record.frame_timestamps)
|
||||
prompt = load_prompt("interjections_interjection").format(
|
||||
episode_task=record.episode_task,
|
||||
prev_subtask=prev_subtask or "(starting from initial state)",
|
||||
next_subtask=next_subtask,
|
||||
timestamp=t_snap,
|
||||
window_seconds=self.config.interjection_window_seconds,
|
||||
)
|
||||
images = self.frame_provider.frames_at(record, window_ts)
|
||||
content = [*to_image_blocks(images), {"type": "text", "text": prompt}]
|
||||
messages = [{"role": "user", "content": content}]
|
||||
result = self.vlm.generate_json([messages])[0]
|
||||
if not isinstance(result, dict):
|
||||
continue
|
||||
interjection_text = result.get("interjection")
|
||||
speech_text = result.get("speech")
|
||||
if not isinstance(interjection_text, str) or not interjection_text.strip():
|
||||
continue
|
||||
if not isinstance(speech_text, str) or not speech_text.strip():
|
||||
continue
|
||||
out.append(
|
||||
{
|
||||
"role": "user",
|
||||
"content": interjection_text.strip(),
|
||||
"style": "interjection",
|
||||
"timestamp": t_snap,
|
||||
"tool_calls": None,
|
||||
}
|
||||
)
|
||||
out.append(speech_atom(t_snap, speech_text.strip()))
|
||||
return out
|
||||
|
||||
def _window_timestamps(self, t_anchor: float, frame_timestamps: Sequence[float]) -> list[float]:
|
||||
"""Return a small set of frame timestamps centered on ``t_anchor``.
|
||||
|
||||
The window straddles the subtask boundary the interjection sits
|
||||
on: roughly half the frames cover the end of the previous
|
||||
subtask, half cover the start of the next one. The VLM therefore
|
||||
sees BOTH what just finished AND what's about to start, which is
|
||||
the conditioning we need to write a natural "now please do X"
|
||||
request that matches the visible upcoming behavior.
|
||||
"""
|
||||
if not frame_timestamps:
|
||||
return [t_anchor]
|
||||
n = max(1, int(self.config.interjection_window_frames))
|
||||
if n == 1:
|
||||
return [t_anchor]
|
||||
window = float(self.config.interjection_window_seconds)
|
||||
step = window / max(1, n - 1)
|
||||
# Center the window on the anchor so half lands before, half after.
|
||||
start_offset = -window / 2.0
|
||||
targets = [t_anchor + start_offset + step * i for i in range(n)]
|
||||
first_ts = float(frame_timestamps[0])
|
||||
last_ts = float(frame_timestamps[-1])
|
||||
snapped: list[float] = []
|
||||
seen: set[float] = set()
|
||||
for tgt in targets:
|
||||
clamped = min(last_ts, max(first_ts, tgt))
|
||||
t = snap_to_frame(clamped, frame_timestamps)
|
||||
if t not in seen:
|
||||
seen.add(t)
|
||||
snapped.append(t)
|
||||
return snapped or [t_anchor]
|
||||
@@ -1,780 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
|
||||
# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
"""``plan`` module: subtask decomposition + plan + memory (PERSISTENT styles)."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
from collections.abc import Sequence
|
||||
from dataclasses import dataclass, field
|
||||
from typing import Any
|
||||
|
||||
from ..config import PlanConfig
|
||||
from ..frames import (
|
||||
FrameProvider,
|
||||
null_provider,
|
||||
to_contact_sheet_blocks,
|
||||
)
|
||||
from ..prompts import load as load_prompt
|
||||
from ..reader import EpisodeRecord, reconstruct_subtask_spans, snap_to_frame
|
||||
from ..staging import EpisodeStaging
|
||||
from ..vlm_client import VlmClient
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
# Prepended to every describe / segment prompt so the VLM knows the images are
|
||||
# timestamped contact-sheet grids, not a single video, and reads the burned-in
|
||||
# per-tile timestamp when choosing boundaries.
|
||||
def _contact_sheet_preamble(columns: int) -> str:
|
||||
return (
|
||||
"CONTACT SHEETS — how to read the images below:\n"
|
||||
f"- Each image is a grid of sampled video frames, {columns} per row, "
|
||||
"with time running left-to-right then top-to-bottom (row-major).\n"
|
||||
"- Each frame has its timestamp burned into the top-left corner, e.g. "
|
||||
'"012.50s". Use that printed timestamp (not the tile position) when you '
|
||||
"choose start/end times; boundaries should land on or near a printed "
|
||||
"timestamp.\n"
|
||||
"- Frames continue across grids: an action may span the end of one sheet "
|
||||
"and the start of the next, so do not place a boundary just because a new "
|
||||
"image begins.\n\n"
|
||||
)
|
||||
|
||||
|
||||
# Appended to every describe (and segment) prompt. A visual, causal definition
|
||||
# of where one event ends and the next begins — adapted from macrodata/refiner —
|
||||
# to sharpen cut points while the existing prompt keeps owning the imperative
|
||||
# phrasing.
|
||||
_CAUSAL_BOUNDARY_RULES = (
|
||||
"EVENT BOUNDARIES — where one event ends and the next begins:\n"
|
||||
"- Start a new event whenever the world state changes: an object becomes "
|
||||
"held (the gripper closes on it), an object is released (the gripper opens "
|
||||
"and it stays put), an object reaches a new location, a lid/door/drawer "
|
||||
"changes open/closed state, a tool starts or stops affecting a surface, or "
|
||||
"contents visibly move (e.g. poured).\n"
|
||||
"- If a single action changes the same state gradually and continuously, "
|
||||
"keep it as ONE event — do not split it.\n"
|
||||
"- If the same action repeats on different objects or target locations, "
|
||||
"treat each repetition as a separate event.\n"
|
||||
"- Do NOT create boundaries for idle time, camera motion, hesitation, or "
|
||||
"tiny hand adjustments."
|
||||
)
|
||||
|
||||
|
||||
@dataclass
|
||||
class PlanSubtasksMemoryModule:
|
||||
"""Generate subtask spans, plan, and memory rows.
|
||||
|
||||
All output is persistent (lives in ``language_persistent``):
|
||||
|
||||
- ``subtask`` rows: one per span, stamped at the span's *start* timestamp
|
||||
(snapped to an exact frame).
|
||||
- ``plan`` rows: emitted at ``t=0``; refreshed at every interjection
|
||||
timestamp via :meth:`run_plan_updates` (called by the executor after
|
||||
the ``interjections`` module completes).
|
||||
- ``memory`` rows: emitted at each subtask boundary (= subtask start
|
||||
timestamp from the second subtask onward).
|
||||
"""
|
||||
|
||||
vlm: VlmClient
|
||||
config: PlanConfig
|
||||
frame_provider: FrameProvider = field(default_factory=null_provider)
|
||||
|
||||
@property
|
||||
def enabled(self) -> bool:
|
||||
return self.config.enabled
|
||||
|
||||
def run_episode(self, record: EpisodeRecord, staging: EpisodeStaging) -> None:
|
||||
rows: list[dict[str, Any]] = []
|
||||
# Task driving every plan-module prompt: canonical episode_task, or a
|
||||
# video-derived one when it's empty/placeholder (see derive_task_*).
|
||||
effective_task = self._resolve_effective_task(record)
|
||||
# task_aug rows at t=0: phrasings the renderer rotates ${task} through.
|
||||
# Either the structured 5-axis taxonomy (task_aug_axes.enabled) or
|
||||
# free-form n_task_rephrasings; the effective task is always emitted
|
||||
# first so the rotation covers the source-of-truth phrasing.
|
||||
t0 = float(record.frame_timestamps[0]) if record.frame_timestamps else 0.0
|
||||
variants: list[str] | None = None
|
||||
if self.config.task_aug_axes.enabled and effective_task:
|
||||
variants = self._generate_task_aug_by_axes(effective_task, self.config.task_aug_axes)
|
||||
elif self.config.n_task_rephrasings > 0 and effective_task:
|
||||
variants = self._generate_task_rephrasings(effective_task, n=self.config.n_task_rephrasings)
|
||||
if variants is not None:
|
||||
rows.extend(self._task_aug_rows([effective_task, *variants], t0))
|
||||
|
||||
subtask_spans = self._generate_subtasks(record, task=effective_task)
|
||||
|
||||
# subtask rows
|
||||
for span in subtask_spans:
|
||||
rows.append(
|
||||
{
|
||||
"role": "assistant",
|
||||
"content": span["text"],
|
||||
"style": "subtask",
|
||||
"timestamp": snap_to_frame(span["start"], record.frame_timestamps),
|
||||
"tool_calls": None,
|
||||
}
|
||||
)
|
||||
# Plan rows at every subtask boundary (incl. t=0). The plan is a
|
||||
# numbered list of still-todo subtasks, so re-emitting at each
|
||||
# boundary makes it shrink as work progresses — ${plan} at frame t is
|
||||
# exactly what's left to do.
|
||||
if self.config.emit_plan:
|
||||
for span in subtask_spans:
|
||||
boundary_t = snap_to_frame(span["start"], record.frame_timestamps)
|
||||
plan_text = self._generate_plan(
|
||||
record, subtask_spans, refresh_t=boundary_t, task=effective_task
|
||||
)
|
||||
if plan_text is not None:
|
||||
rows.append(
|
||||
{
|
||||
"role": "assistant",
|
||||
"content": plan_text,
|
||||
"style": "plan",
|
||||
"timestamp": float(boundary_t),
|
||||
"tool_calls": None,
|
||||
}
|
||||
)
|
||||
# memory rows at every subtask boundary except the very first start;
|
||||
# skipped entirely when ``emit_memory`` is False (subtasks-only / plan-only).
|
||||
prior_memory = ""
|
||||
memory_boundaries = enumerate(subtask_spans[1:], start=1) if self.config.emit_memory else []
|
||||
for i, span in memory_boundaries:
|
||||
completed = subtask_spans[i - 1]["text"]
|
||||
remaining = [s["text"] for s in subtask_spans[i:]]
|
||||
mem_text = self._generate_memory(record, prior_memory, completed, remaining, task=effective_task)
|
||||
if mem_text:
|
||||
ts = snap_to_frame(span["start"], record.frame_timestamps)
|
||||
rows.append(
|
||||
{
|
||||
"role": "assistant",
|
||||
"content": mem_text,
|
||||
"style": "memory",
|
||||
"timestamp": ts,
|
||||
"tool_calls": None,
|
||||
}
|
||||
)
|
||||
prior_memory = mem_text
|
||||
staging.write("plan", rows)
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# Task derivation + rephrasings
|
||||
# ------------------------------------------------------------------
|
||||
|
||||
_PLACEHOLDER_TASKS: frozenset[str] = frozenset(
|
||||
{
|
||||
"debug",
|
||||
"test",
|
||||
"tbd",
|
||||
"todo",
|
||||
"n/a",
|
||||
"na",
|
||||
"untitled",
|
||||
"unnamed",
|
||||
"default",
|
||||
"placeholder",
|
||||
}
|
||||
)
|
||||
|
||||
def _resolve_effective_task(self, record: EpisodeRecord) -> str:
|
||||
"""Decide which task string drives the ``plan`` module for this episode.
|
||||
|
||||
Returns the user-supplied ``record.episode_task`` unless
|
||||
``derive_task_from_video`` says otherwise (see config docstring).
|
||||
Falls back gracefully to the canonical task if video derivation
|
||||
fails.
|
||||
"""
|
||||
canonical = (record.episode_task or "").strip()
|
||||
mode = (self.config.derive_task_from_video or "off").strip().lower()
|
||||
if mode == "always":
|
||||
derived = self._derive_task_from_video(record)
|
||||
return derived or canonical
|
||||
if mode == "if_short" and self._task_seems_bad(canonical):
|
||||
derived = self._derive_task_from_video(record)
|
||||
if derived:
|
||||
return derived
|
||||
return canonical
|
||||
|
||||
def _task_seems_bad(self, task: str) -> bool:
|
||||
if not task:
|
||||
return True
|
||||
if len(task.split()) < int(self.config.derive_task_min_words):
|
||||
return True
|
||||
return task.lower() in self._PLACEHOLDER_TASKS
|
||||
|
||||
@staticmethod
|
||||
def _task_aug_rows(phrasings: Sequence[str], t0: float) -> list[dict[str, Any]]:
|
||||
"""Build deduplicated ``task_aug`` rows (role=user) at ``t0``."""
|
||||
seen: set[str] = set()
|
||||
rows: list[dict[str, Any]] = []
|
||||
for phrasing in phrasings:
|
||||
key = phrasing.strip()
|
||||
if not key or key in seen:
|
||||
continue
|
||||
seen.add(key)
|
||||
rows.append(
|
||||
{"role": "user", "content": key, "style": "task_aug", "timestamp": t0, "tool_calls": None}
|
||||
)
|
||||
return rows
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# VLM call helpers — every plan-module prompt follows the same shape:
|
||||
# build messages → single VLM call → pull a named field.
|
||||
# ------------------------------------------------------------------
|
||||
|
||||
def _vlm_field(self, messages: list[dict[str, Any]], field: str) -> Any:
|
||||
"""Run a single VLM call and return ``result[field]`` or ``None``.
|
||||
|
||||
Centralizes the ``vlm.generate_json([m])[0]`` + ``isinstance(dict)``
|
||||
dance every prompt-call site needs.
|
||||
"""
|
||||
result = self.vlm.generate_json([messages])[0]
|
||||
if isinstance(result, dict):
|
||||
return result.get(field)
|
||||
return None
|
||||
|
||||
@staticmethod
|
||||
def _text_message(text: str) -> list[dict[str, Any]]:
|
||||
"""One-shot text-only user message wrapped for ``generate_json``."""
|
||||
return [{"role": "user", "content": [{"type": "text", "text": text}]}]
|
||||
|
||||
def _video_message(
|
||||
self,
|
||||
record: EpisodeRecord,
|
||||
prompt: str,
|
||||
window: tuple[float, float] | None = None,
|
||||
) -> list[dict[str, Any]]:
|
||||
"""User message combining the (optionally windowed) contact sheets with ``prompt``.
|
||||
|
||||
The prompt is always prefixed with a short explanation of how to read
|
||||
the timestamped grids, so the model treats them as one ordered
|
||||
sequence of frames rather than unrelated images.
|
||||
"""
|
||||
prompt = _contact_sheet_preamble(self.config.contact_sheet_columns) + prompt
|
||||
content = [*self._episode_video_block(record, window=window), {"type": "text", "text": prompt}]
|
||||
return [{"role": "user", "content": content}]
|
||||
|
||||
def _derive_task_from_video(self, record: EpisodeRecord) -> str | None:
|
||||
"""Ask the VLM "what is this video about" with no task hint at all."""
|
||||
text = self._vlm_field(self._video_message(record, load_prompt("plan_video_task")), "task")
|
||||
return text.strip() if isinstance(text, str) and text.strip() else None
|
||||
|
||||
def _generate_task_rephrasings(self, base_task: str, *, n: int) -> list[str]:
|
||||
"""Generate ``n`` text-only paraphrases of ``base_task``."""
|
||||
if n <= 0 or not base_task:
|
||||
return []
|
||||
prompt = load_prompt("plan_task_rephrasings").format(base_task=base_task, n=n)
|
||||
raw = self._vlm_field(self._text_message(prompt), "rephrasings")
|
||||
if not isinstance(raw, list):
|
||||
return []
|
||||
out = [item.strip().strip('"').strip("'") for item in raw if isinstance(item, str)]
|
||||
return [s for s in out if s][:n]
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# Structured 5-axis task augmentation (EgoMimic-style taxonomy)
|
||||
# ------------------------------------------------------------------
|
||||
|
||||
def _generate_task_aug_by_axes(self, base_task: str, axes_cfg: Any) -> list[str]:
|
||||
"""One VLM call → variants along the 5-axis taxonomy.
|
||||
|
||||
Variants from all axes are flattened into a single list (the
|
||||
downstream pipeline doesn't need to know about the per-axis
|
||||
bucketing — every variant becomes a ``task_aug`` row). Order
|
||||
is preserved for reproducibility: synonym_paraphrase first,
|
||||
then omit_arm, then omit_orientation, then omit_grasp_method,
|
||||
then combined_omissions.
|
||||
"""
|
||||
if not base_task:
|
||||
return []
|
||||
prompt = load_prompt("plan_task_aug_axes").format(
|
||||
base_task=base_task,
|
||||
n_synonym=axes_cfg.synonym_paraphrase,
|
||||
n_omit_arm=axes_cfg.omit_arm,
|
||||
n_omit_orientation=axes_cfg.omit_orientation,
|
||||
n_omit_grasp_method=axes_cfg.omit_grasp_method,
|
||||
n_combined=axes_cfg.combined_omissions,
|
||||
)
|
||||
result = self.vlm.generate_json([self._text_message(prompt)])[0]
|
||||
if not isinstance(result, dict):
|
||||
return []
|
||||
ordered_axes = (
|
||||
"synonym_paraphrase",
|
||||
"omit_arm",
|
||||
"omit_orientation",
|
||||
"omit_grasp_method",
|
||||
"combined_omissions",
|
||||
)
|
||||
flat: list[str] = []
|
||||
seen: set[str] = set()
|
||||
for axis in ordered_axes:
|
||||
entries = result.get(axis)
|
||||
if not isinstance(entries, list):
|
||||
continue
|
||||
for item in entries:
|
||||
if not isinstance(item, str):
|
||||
continue
|
||||
key = item.strip().strip('"').strip("'")
|
||||
if not key or key in seen:
|
||||
continue
|
||||
seen.add(key)
|
||||
flat.append(key)
|
||||
return flat
|
||||
|
||||
def _episode_video_block(
|
||||
self, record: EpisodeRecord, window: tuple[float, float] | None = None
|
||||
) -> list[dict[str, Any]]:
|
||||
"""Timestamped contact sheets for the describe / segmentation prompts.
|
||||
|
||||
Always renders the (optionally windowed) episode as contact sheets:
|
||||
frames sampled at ``frames_per_second`` and packed into timestamped
|
||||
JPEG grids. ``max_frames_per_prompt`` caps the frame count; whole
|
||||
episodes that exceed it are windowed upstream in
|
||||
:meth:`_generate_subtasks` so each call stays within budget while the
|
||||
full episode keeps its sampling density.
|
||||
|
||||
When ``window=(w0, w1)`` is given the badges are WINDOW-RELATIVE
|
||||
(``ts - w0``) to match the window-relative time frame the
|
||||
segmentation prompt works in (spans are offset back to absolute time
|
||||
afterwards).
|
||||
"""
|
||||
if not record.frame_timestamps:
|
||||
return []
|
||||
if window is not None:
|
||||
w0, w1 = float(window[0]), float(window[1])
|
||||
dur = max(0.0, w1 - w0)
|
||||
n = max(1, int(round(dur * self.config.frames_per_second)) + 1)
|
||||
n = min(n, self.config.max_frames_per_prompt)
|
||||
if n <= 1 or dur <= 0.0:
|
||||
timestamps = [0.5 * (w0 + w1)]
|
||||
else:
|
||||
step = dur / (n - 1)
|
||||
timestamps = [w0 + i * step for i in range(n)]
|
||||
frames = self.frame_provider.frames_at(record, timestamps)
|
||||
rel = [ts - w0 for ts in timestamps[: len(frames)]]
|
||||
return self._contact_sheet_blocks(frames, rel)
|
||||
episode_duration = record.frame_timestamps[-1] - record.frame_timestamps[0]
|
||||
n = max(1, int(round(episode_duration * self.config.frames_per_second)) + 1)
|
||||
n = min(n, self.config.max_frames_per_prompt)
|
||||
timestamps = self._uniform_episode_timestamps(record, n)
|
||||
frames = self.frame_provider.frames_at(record, timestamps)
|
||||
return self._contact_sheet_blocks(frames, timestamps[: len(frames)])
|
||||
|
||||
@staticmethod
|
||||
def _uniform_episode_timestamps(record: EpisodeRecord, n: int) -> list[float]:
|
||||
"""``n`` episode-relative timestamps spanning ``[t0, t_last]`` uniformly."""
|
||||
ts = record.frame_timestamps
|
||||
if n >= len(ts):
|
||||
return [float(t) for t in ts]
|
||||
t0, t_last = float(ts[0]), float(ts[-1])
|
||||
if t_last <= t0 or n <= 1:
|
||||
return [t0] * max(1, n)
|
||||
step = (t_last - t0) / (n - 1)
|
||||
return [t0 + i * step for i in range(n)]
|
||||
|
||||
def _contact_sheet_blocks(self, frames: list[Any], timestamps: list[float]) -> list[dict[str, Any]]:
|
||||
"""Build timestamped contact-sheet image blocks from decoded frames."""
|
||||
return to_contact_sheet_blocks(
|
||||
frames,
|
||||
timestamps,
|
||||
columns=self.config.contact_sheet_columns,
|
||||
frames_per_sheet=self.config.contact_sheet_frames_per_sheet,
|
||||
frame_width=self.config.contact_sheet_frame_width,
|
||||
quality=self.config.contact_sheet_quality,
|
||||
)
|
||||
|
||||
def run_plan_updates(
|
||||
self,
|
||||
record: EpisodeRecord,
|
||||
staging: EpisodeStaging,
|
||||
interjection_times: Sequence[float],
|
||||
interjection_texts: Sequence[str] | None = None,
|
||||
) -> None:
|
||||
"""Append additional ``plan`` rows at every interjection timestamp.
|
||||
|
||||
Plans refresh ONLY on user interjections (event-driven). The
|
||||
interjection text is forwarded into the prompt so the refreshed plan
|
||||
reflects the user's correction.
|
||||
"""
|
||||
if not self.config.emit_plan:
|
||||
return
|
||||
existing = staging.read("plan")
|
||||
# Pass the last frame timestamp so the final span is closed (else its
|
||||
# end == start, zero duration, and a refresh inside it is missed).
|
||||
episode_end_t = float(record.frame_timestamps[-1]) if record.frame_timestamps else None
|
||||
spans = reconstruct_subtask_spans(existing, episode_end_t=episode_end_t)
|
||||
already_planned: set[float] = {float(r["timestamp"]) for r in existing if r.get("style") == "plan"}
|
||||
new_rows = list(existing)
|
||||
|
||||
texts: list[str | None] = (
|
||||
[None] * len(interjection_times)
|
||||
if interjection_texts is None
|
||||
else [str(t) if t else None for t in interjection_texts]
|
||||
)
|
||||
for raw_t, inter_text in zip(interjection_times, texts, strict=True):
|
||||
t = snap_to_frame(raw_t, record.frame_timestamps)
|
||||
if t in already_planned:
|
||||
continue
|
||||
already_planned.add(t)
|
||||
plan_text = self._generate_plan(record, spans, refresh_t=t, interjection=inter_text)
|
||||
if plan_text is not None:
|
||||
new_rows.append(
|
||||
{
|
||||
"role": "assistant",
|
||||
"content": plan_text,
|
||||
"style": "plan",
|
||||
"timestamp": t,
|
||||
"tool_calls": None,
|
||||
}
|
||||
)
|
||||
staging.write("plan", new_rows)
|
||||
|
||||
def _generate_subtasks(self, record: EpisodeRecord, *, task: str | None = None) -> list[dict[str, Any]]:
|
||||
"""Generate subtask spans, optionally via a multi-call quality chain.
|
||||
|
||||
Single call (default): watch video → emit subtask JSON.
|
||||
|
||||
Multi-call (opt-in, higher quality, more VLM calls):
|
||||
1. ``subtask_describe_first`` — a grounding pass that narrates
|
||||
ONLY what is visible (no JSON commitment to subtasks yet);
|
||||
its description is injected into the segmentation prompt so
|
||||
the model segments its own grounded observations instead of
|
||||
pattern-matching the task text.
|
||||
2. segmentation — emit subtask JSON (as before).
|
||||
"""
|
||||
if record.row_count == 0 or not record.frame_timestamps:
|
||||
return []
|
||||
episode_duration = record.frame_timestamps[-1] - record.frame_timestamps[0]
|
||||
effective_task = task if task is not None else record.episode_task
|
||||
|
||||
# ---- Auto-windowing (keeps the full sampling density) --------
|
||||
# Contact sheets are cheap, but a whole long episode sampled at
|
||||
# ``frames_per_second`` can still exceed ``max_frames_per_prompt``.
|
||||
# When it does, split into consecutive windows of exactly that many
|
||||
# frames (one describe→segment call each, still at the full sampling
|
||||
# density), then merge + stitch — so an episode of any length is
|
||||
# covered at full density rather than subsampled into one sparse call.
|
||||
fps = max(1e-6, float(self.config.frames_per_second))
|
||||
n_whole = int(round(episode_duration * fps)) + 1
|
||||
if n_whole > self.config.max_frames_per_prompt:
|
||||
window_s = self.config.max_frames_per_prompt / fps
|
||||
return self._generate_subtasks_windowed(record, effective_task, window_s)
|
||||
|
||||
# ---- Pass 1 (optional): grounding description ----------------
|
||||
observation_block = ""
|
||||
if getattr(self.config, "subtask_describe_first", False):
|
||||
description = self._describe_episode(record, effective_task)
|
||||
if description:
|
||||
observation_block = (
|
||||
"You watched this video and described, chronologically, "
|
||||
"ONLY what the robot actually does:\n"
|
||||
f'"""{description}"""\n\n'
|
||||
"Segment THAT grounded description (cross-checked against "
|
||||
"the video) into atomic subtasks. Do not introduce any "
|
||||
"action that is not in your description above.\n\n"
|
||||
)
|
||||
|
||||
# ---- Pass 2: segmentation ------------------------------------
|
||||
prompt = self._with_causal_rules(
|
||||
load_prompt("plan_subtasks").format(
|
||||
episode_task=effective_task,
|
||||
min_subtask_seconds=self.config.min_subtask_seconds,
|
||||
max_steps=self.config.plan_max_steps,
|
||||
episode_duration=f"{episode_duration:.3f}",
|
||||
observation_block=observation_block,
|
||||
)
|
||||
)
|
||||
spans = self._vlm_field(self._video_message(record, prompt), "subtasks")
|
||||
cleaned = self._clean_spans(spans, record)
|
||||
if not cleaned:
|
||||
return []
|
||||
|
||||
# ---- Full-episode coverage stitch ----------------------------
|
||||
# The VLM can start after t0 or leave gaps, so frames fall through
|
||||
# with no active subtask. Always stitch into a contiguous
|
||||
# [t0, t_last] cover.
|
||||
cleaned = self._stitch_full_coverage(cleaned, record)
|
||||
|
||||
return cleaned
|
||||
|
||||
def _generate_subtasks_windowed(
|
||||
self, record: EpisodeRecord, task: str, window_s: float
|
||||
) -> list[dict[str, Any]]:
|
||||
"""Subtask generation in fixed-length windows at constant fps.
|
||||
|
||||
Splits ``[t0, t_last]`` into consecutive windows of ``window_s``
|
||||
seconds, runs the describe -> segment chain on each window's own
|
||||
frames (sampled at ``frames_per_second``), offsets
|
||||
each window's spans back to absolute episode time, then merges +
|
||||
stitches into a contiguous whole-episode cover.
|
||||
"""
|
||||
t0 = float(record.frame_timestamps[0])
|
||||
t_last = float(record.frame_timestamps[-1])
|
||||
all_spans: list[dict[str, Any]] = []
|
||||
w0 = t0
|
||||
n_windows = 0
|
||||
while w0 < t_last - 1e-6:
|
||||
w1 = min(w0 + window_s, t_last)
|
||||
all_spans.extend(self._subtasks_for_window(record, task, w0, w1))
|
||||
n_windows += 1
|
||||
w0 = w1
|
||||
logger.info(
|
||||
"episode %d: windowed subtask gen over %d window(s) of %.1fs -> %d raw spans",
|
||||
record.episode_index,
|
||||
n_windows,
|
||||
window_s,
|
||||
len(all_spans),
|
||||
)
|
||||
# Merge across windows: clamp to the absolute episode, sort, and
|
||||
# frame-snap to distinct starts (handles any boundary collisions).
|
||||
cleaned = self._clean_spans(all_spans, record)
|
||||
if not cleaned:
|
||||
return []
|
||||
return self._stitch_full_coverage(cleaned, record)
|
||||
|
||||
def _subtasks_for_window(
|
||||
self, record: EpisodeRecord, task: str, w0: float, w1: float
|
||||
) -> list[dict[str, Any]]:
|
||||
"""Run describe -> segment on one ``[w0, w1]`` window.
|
||||
|
||||
The model works in window-RELATIVE time ``[0, L]`` (it perceives
|
||||
the window as a clip starting at 0); spans are offset back to
|
||||
absolute ``[w0, w1]`` before returning.
|
||||
"""
|
||||
window = (w0, w1)
|
||||
win_len = max(0.0, w1 - w0)
|
||||
|
||||
observation_block = ""
|
||||
if getattr(self.config, "subtask_describe_first", False):
|
||||
description = self._describe_episode(record, task, window=window)
|
||||
if description:
|
||||
observation_block = (
|
||||
"You watched this video clip and described, chronologically, "
|
||||
"ONLY what the robot actually does:\n"
|
||||
f'"""{description}"""\n\n'
|
||||
"Segment THAT grounded description (cross-checked against "
|
||||
"the clip) into atomic subtasks. Do not introduce any "
|
||||
"action that is not in your description above.\n\n"
|
||||
)
|
||||
|
||||
prompt = self._with_causal_rules(
|
||||
load_prompt("plan_subtasks").format(
|
||||
episode_task=task,
|
||||
min_subtask_seconds=self.config.min_subtask_seconds,
|
||||
max_steps=self.config.plan_max_steps,
|
||||
episode_duration=f"{win_len:.3f}",
|
||||
observation_block=observation_block,
|
||||
)
|
||||
)
|
||||
spans = self._vlm_field(self._video_message(record, prompt, window=window), "subtasks")
|
||||
# Window-relative clamp; no frame-snap dedupe yet (done on the
|
||||
# merged absolute set).
|
||||
cleaned = self._clean_spans(spans, record, bounds=(0.0, win_len), dedupe=False)
|
||||
if not cleaned:
|
||||
return []
|
||||
|
||||
# Offset window-relative spans back to absolute episode time.
|
||||
for s in cleaned:
|
||||
s["start"] = w0 + float(s["start"])
|
||||
s["end"] = w0 + float(s["end"])
|
||||
return cleaned
|
||||
|
||||
def _stitch_full_coverage(
|
||||
self, spans: list[dict[str, Any]], record: EpisodeRecord
|
||||
) -> list[dict[str, Any]]:
|
||||
"""Make subtask spans tile the full episode with no gaps.
|
||||
|
||||
* The first subtask starts at the episode's first frame ``t0``
|
||||
(any idle / approach before the first labelled action is folded
|
||||
into it), so every early frame has an active subtask.
|
||||
* Each subtask's ``end`` is snapped to the next subtask's
|
||||
``start`` (gaps between spans are closed), and the final
|
||||
subtask's ``end`` extends to the last frame ``t_last``.
|
||||
|
||||
Starts are otherwise left as the (already frame-snapped, distinct)
|
||||
values the VLM produced — only the FIRST start is pulled
|
||||
back to ``t0``, which can't collide with a later span because it
|
||||
was already the earliest. Purely deterministic; runs after the
|
||||
VLM passes.
|
||||
"""
|
||||
if not spans or not record.frame_timestamps:
|
||||
return spans
|
||||
t0 = float(record.frame_timestamps[0])
|
||||
t_last = float(record.frame_timestamps[-1])
|
||||
spans = sorted(spans, key=lambda s: float(s["start"]))
|
||||
spans[0]["start"] = t0
|
||||
for i in range(len(spans) - 1):
|
||||
spans[i]["end"] = float(spans[i + 1]["start"])
|
||||
spans[-1]["end"] = t_last
|
||||
for s in spans:
|
||||
if float(s["end"]) < float(s["start"]):
|
||||
s["end"] = float(s["start"])
|
||||
return spans
|
||||
|
||||
@staticmethod
|
||||
def _with_causal_rules(prompt: str) -> str:
|
||||
"""Append the causal event-boundary rules to a describe/segment prompt."""
|
||||
return f"{prompt}\n\n{_CAUSAL_BOUNDARY_RULES}"
|
||||
|
||||
def _clean_spans(
|
||||
self,
|
||||
spans: Any,
|
||||
record: EpisodeRecord,
|
||||
bounds: tuple[float, float] | None = None,
|
||||
dedupe: bool = True,
|
||||
) -> list[dict[str, Any]]:
|
||||
"""Clamp / sort / (optionally) dedupe raw VLM subtask spans into valid rows.
|
||||
|
||||
``bounds`` overrides the clamp range — pass the window's
|
||||
``(w_lo, w_hi)`` when cleaning window-relative spans, or leave
|
||||
``None`` to clamp to the whole episode ``[t0, t_last]``.
|
||||
``dedupe`` runs the frame-snap distinct-start step; skip it for
|
||||
window-relative spans (frame snapping is done once on the merged,
|
||||
absolute-time set).
|
||||
"""
|
||||
if not spans:
|
||||
return []
|
||||
if bounds is not None:
|
||||
lo, hi = float(bounds[0]), float(bounds[1])
|
||||
else:
|
||||
lo = record.frame_timestamps[0]
|
||||
hi = record.frame_timestamps[-1]
|
||||
cleaned: list[dict[str, Any]] = []
|
||||
for span in spans:
|
||||
try:
|
||||
start = float(span["start"])
|
||||
end = float(span["end"])
|
||||
text = str(span["text"]).strip()
|
||||
except (KeyError, ValueError, TypeError):
|
||||
continue
|
||||
start = max(lo, min(start, hi))
|
||||
end = max(lo, min(end, hi))
|
||||
if end < start:
|
||||
start, end = end, start
|
||||
if not text:
|
||||
continue
|
||||
cleaned.append({"text": text, "start": start, "end": end})
|
||||
cleaned.sort(key=lambda s: s["start"])
|
||||
if dedupe:
|
||||
return self._dedupe_starts_to_distinct_frames(cleaned, record)
|
||||
return cleaned
|
||||
|
||||
def _describe_episode(
|
||||
self, record: EpisodeRecord, task: str, window: tuple[float, float] | None = None
|
||||
) -> str:
|
||||
"""Grounding pass: free-form chronological description of the (windowed) video."""
|
||||
prompt = self._with_causal_rules(load_prompt("plan_subtask_describe").format(episode_task=task))
|
||||
text = self._vlm_field(self._video_message(record, prompt, window=window), "description")
|
||||
return text.strip() if isinstance(text, str) and text.strip() else ""
|
||||
|
||||
@staticmethod
|
||||
def _dedupe_starts_to_distinct_frames(
|
||||
spans: list[dict[str, Any]], record: EpisodeRecord
|
||||
) -> list[dict[str, Any]]:
|
||||
"""Bump same-frame subtask starts onto distinct frames.
|
||||
|
||||
Two consecutive VLM spans whose ``start`` rounds to the same
|
||||
source frame (after :func:`snap_to_frame`) would otherwise emit
|
||||
two ``style=subtask`` rows at the identical persistent
|
||||
timestamp. The training-time renderer's ``active_at(t,
|
||||
style=subtask)`` resolver can't disambiguate that and raises
|
||||
``Ambiguous resolver for style='subtask'``.
|
||||
|
||||
Walk the (sorted-by-start) spans, snap each to its frame, and
|
||||
if the snapped frame is already taken push the span onto the
|
||||
next unused frame so both subtasks survive on distinct
|
||||
timestamps. If the episode ends before a free frame is found,
|
||||
the trailing span is dropped with a warning — better than
|
||||
poisoning the render.
|
||||
"""
|
||||
if not spans:
|
||||
return spans
|
||||
frames = record.frame_timestamps
|
||||
if not frames:
|
||||
return spans
|
||||
used: set[float] = set()
|
||||
out: list[dict[str, Any]] = []
|
||||
for span in spans:
|
||||
ts = snap_to_frame(span["start"], frames)
|
||||
if ts in used:
|
||||
next_ts = next((f for f in frames if f > ts and f not in used), None)
|
||||
if next_ts is None:
|
||||
logger.warning(
|
||||
"episode %d: subtask %r snapped to occupied frame "
|
||||
"%.3f and no free later frame exists — dropping",
|
||||
record.episode_index,
|
||||
span.get("text"),
|
||||
ts,
|
||||
)
|
||||
continue
|
||||
ts = next_ts
|
||||
used.add(ts)
|
||||
new_span = {**span, "start": ts}
|
||||
if float(new_span.get("end", ts)) < ts:
|
||||
new_span["end"] = ts
|
||||
out.append(new_span)
|
||||
return out
|
||||
|
||||
def _generate_plan(
|
||||
self,
|
||||
record: EpisodeRecord, # noqa: ARG002 (kept for signature stability)
|
||||
subtask_spans: Sequence[dict[str, Any]],
|
||||
*,
|
||||
refresh_t: float | None = None,
|
||||
interjection: str | None = None, # noqa: ARG002
|
||||
task: str | None = None, # noqa: ARG002
|
||||
) -> str | None:
|
||||
"""Deterministic plan = numbered list of *still-todo* subtasks.
|
||||
|
||||
No VLM call: a plain numbered list keeps the plan aligned with the
|
||||
upcoming subtasks (the old VLM "compact hierarchical plan" prompt
|
||||
cost a round-trip per episode/refresh and could diverge).
|
||||
|
||||
1. <subtask 1>
|
||||
2. <subtask 2>
|
||||
|
||||
On a refresh at ``refresh_t`` (from ``run_plan_updates`` on
|
||||
interjections, and ``run_episode`` at each boundary), only subtasks
|
||||
starting at or after ``refresh_t`` are included — so it always
|
||||
describes what's left.
|
||||
"""
|
||||
if not subtask_spans:
|
||||
return None
|
||||
remaining = [
|
||||
s for s in subtask_spans if refresh_t is None or float(s.get("start", 0.0)) >= float(refresh_t)
|
||||
]
|
||||
if not remaining:
|
||||
# Past the last subtask boundary on a late refresh — nothing
|
||||
# left to plan; emit None so the caller skips the row.
|
||||
return None
|
||||
return "\n".join(f"{i}. {span.get('text', '').strip()}" for i, span in enumerate(remaining, start=1))
|
||||
|
||||
def _generate_memory(
|
||||
self,
|
||||
record: EpisodeRecord,
|
||||
prior_memory: str,
|
||||
completed: str,
|
||||
remaining: Sequence[str],
|
||||
*,
|
||||
task: str | None = None,
|
||||
) -> str:
|
||||
prompt = load_prompt("plan_memory").format(
|
||||
episode_task=(task if task is not None else record.episode_task),
|
||||
prior_memory=prior_memory or "(none)",
|
||||
completed_subtask=completed,
|
||||
remaining_subtasks=", ".join(remaining) if remaining else "(none)",
|
||||
)
|
||||
memory = self._vlm_field(self._text_message(prompt), "memory")
|
||||
return memory.strip() if isinstance(memory, str) else ""
|
||||
@@ -1,33 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
|
||||
# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
"""Prompt templates loaded as plain text.
|
||||
|
||||
One file per use site. Templates use ``str.format(**vars)`` substitution; we
|
||||
intentionally avoid jinja2 here so the templates remain inspectable in
|
||||
plain editors and roundtrip cleanly through ``ruff format``.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from pathlib import Path
|
||||
|
||||
_DIR = Path(__file__).parent
|
||||
|
||||
|
||||
def load(name: str) -> str:
|
||||
"""Read prompt template ``name.txt`` from the ``prompts/`` directory."""
|
||||
path = _DIR / f"{name}.txt"
|
||||
return path.read_text(encoding="utf-8")
|
||||
@@ -1,12 +0,0 @@
|
||||
The user just asked the robot: "{episode_task}".
|
||||
|
||||
Generate a short verbal acknowledgement the robot would speak back before
|
||||
beginning the task. Style: compact, confident, friendly.
|
||||
|
||||
Examples (Hi Robot, Shi 2025): "Sure, I won't put cheese on it.",
|
||||
"OK, starting with the sponge.", "Got it.".
|
||||
|
||||
Prefer very short replies: "Got it.", "On it.", "OK."
|
||||
|
||||
Output strictly valid JSON:
|
||||
{{ "text": "<the spoken acknowledgement>" }}
|
||||
@@ -1,46 +0,0 @@
|
||||
You are generating training data for a Hi Robot-style hierarchical
|
||||
robot policy. The robot in this demonstration has ALREADY executed
|
||||
every step shown in the video — we cannot retroactively change the
|
||||
action stream. To keep training data consistent with the video, the
|
||||
"interjection" must align with what the robot is *about to do next* in
|
||||
the demonstration, framed as a natural mid-task user request.
|
||||
|
||||
The episode's overall task: "{episode_task}".
|
||||
|
||||
The images above show roughly {window_seconds:.1f} seconds straddling a
|
||||
subtask boundary in the demonstration:
|
||||
|
||||
- Subtask the robot just finished: "{prev_subtask}"
|
||||
- Subtask the robot is about to start: "{next_subtask}"
|
||||
- Time into episode: {timestamp:.2f}s
|
||||
|
||||
Write ONE compact interjection the user would naturally say at this
|
||||
moment to prompt / confirm / encourage the robot to do "{next_subtask}".
|
||||
Keep it like a mid-task coaching cue, not a full instruction paragraph.
|
||||
Also write the robot's compact verbal acknowledgement.
|
||||
|
||||
Hard rules:
|
||||
|
||||
- The interjection MUST be consistent with the next subtask. The user
|
||||
cannot ask for something different from what the robot then does in
|
||||
the video. If you're tempted to say "actually skip X" or "do Y
|
||||
instead", DO NOT — those would contradict the demonstration.
|
||||
- The interjection must reference an object, location, or action that
|
||||
is plausible given the visible scene and the next subtask text.
|
||||
- One short phrase or sentence each. Conversational, not robotic.
|
||||
- Prefer direct cues: "{next_subtask}, please."; "Now {next_subtask}."
|
||||
- Keep robot speech very short: "OK.", "On it.", "Doing that."
|
||||
|
||||
Style examples (vary the phrasing — don't reuse these verbatim):
|
||||
- "Now go ahead and {next_subtask}."
|
||||
- "Great, can you {next_subtask} next?"
|
||||
- "{next_subtask}, please."
|
||||
- "Before you continue, please {next_subtask}."
|
||||
- "Looking good — {next_subtask} now."
|
||||
- "Okay, {next_subtask}."
|
||||
|
||||
Output strictly valid JSON:
|
||||
{{
|
||||
"interjection": "<short cue from the user, asking for the next subtask>",
|
||||
"speech": "<short robot acknowledgement>"
|
||||
}}
|
||||
@@ -1,36 +0,0 @@
|
||||
You are updating the robot's compressed semantic memory at the boundary of
|
||||
a completed subtask.
|
||||
|
||||
Reference (verbatim from MEM, Torne 2026):
|
||||
"Remove or compress information in the language memory whenever
|
||||
appropriate. Keep ONLY the minimal set of relevant information for future
|
||||
task execution. Specific object attributes (colors, precise quantities of
|
||||
each item) get discarded when their details won't affect subsequent
|
||||
actions. Functional outcomes (where items went, how many) are preserved."
|
||||
|
||||
Episode task: "{episode_task}"
|
||||
Previous memory: {prior_memory}
|
||||
Just-completed subtask: "{completed_subtask}"
|
||||
Remaining subtasks (for relevance judgement only): {remaining_subtasks}
|
||||
|
||||
Write the memory as a short FIRST-PERSON, PAST-TENSE narrative of what the
|
||||
robot has accomplished so far — the running story it would tell itself.
|
||||
|
||||
Authoring rules:
|
||||
- First person, past tense. Every sentence starts with "I": "I picked
|
||||
up...", "I opened...", "I moved to...".
|
||||
- One or two short sentences. Extend the previous memory with the
|
||||
just-completed subtask; do not rewrite it from scratch.
|
||||
- Keep WHAT happened (functional outcomes — where items went, how many),
|
||||
drop HOW (grasp details, motions).
|
||||
- Compress completed steps and drop object attributes (colors, exact
|
||||
counts) once they no longer affect the remaining subtasks.
|
||||
|
||||
Example (MEM, Torne 2026):
|
||||
Before: "I prepared the pot and got the potatoes, milk, and butter. I
|
||||
moved to the drawer."
|
||||
After: "I prepared the pot and got the ingredients. I opened the
|
||||
drawer with the masher."
|
||||
|
||||
Output strictly valid JSON:
|
||||
{{ "memory": "<one or two short first-person past-tense sentences>" }}
|
||||
@@ -1,27 +0,0 @@
|
||||
You are watching a teleoperated robot demonstration from a single
|
||||
camera. The user asked the robot to: "{episode_task}"
|
||||
|
||||
This is an OBSERVATION pass. Watch the entire clip and describe, in
|
||||
chronological order, ONLY what the robot physically does — the concrete
|
||||
motions, approaches, contacts, grasps, releases, and relocations you can
|
||||
actually SEE in the frames.
|
||||
|
||||
Hard rules:
|
||||
- Describe only motion visible in the video. Do NOT use the task
|
||||
instruction to guess steps that aren't shown. The instruction is the
|
||||
goal; the video is ground truth.
|
||||
- Do NOT segment into named subtasks yet and do NOT output JSON beyond
|
||||
the single field below. Just narrate what happens.
|
||||
- Give an approximate timestamp (in seconds) for each distinct event,
|
||||
e.g. "0.0-1.4s: the base drives forward toward the stove".
|
||||
- Do NOT invent objects, grasps, destinations, or steps. If the robot
|
||||
only does one thing (e.g. it just navigates and the clip ends), say
|
||||
exactly that and nothing more.
|
||||
- Be concrete and literal. "the gripper closes on the mug" — not "the
|
||||
robot prepares to make coffee".
|
||||
|
||||
Output strictly valid JSON:
|
||||
|
||||
{{
|
||||
"description": "<chronological, timestamped description of ONLY what is visible>"
|
||||
}}
|
||||
@@ -1,112 +0,0 @@
|
||||
You are labeling a teleoperated robot demonstration.
|
||||
|
||||
The user originally asked: "{episode_task}"
|
||||
|
||||
You are shown the entire demonstration as a single video. Watch the
|
||||
whole clip, then segment it into a list of consecutive atomic subtasks
|
||||
the robot performs.
|
||||
|
||||
{observation_block}GROUNDING — read this first, it overrides everything below:
|
||||
- Label ONLY what the robot actually does in the video. Every subtask
|
||||
you emit must correspond to motion you can SEE in specific frames.
|
||||
- Do NOT invent, anticipate, or pad. If the robot only does one thing
|
||||
(e.g. it just navigates to a location and the clip ends), emit
|
||||
EXACTLY ONE subtask. Many demonstrations are a single atomic skill.
|
||||
- ``max_steps`` below is a hard CEILING, not a target. Emitting fewer
|
||||
subtasks than the ceiling is not just allowed, it is expected for
|
||||
short / atomic demonstrations. One correct subtask is far better
|
||||
than several invented ones.
|
||||
- If the video does not clearly show the action implied by the task,
|
||||
describe what you actually see — do NOT fabricate the task's steps
|
||||
from the instruction text. The instruction tells you the goal; the
|
||||
VIDEO is the ground truth for what happened.
|
||||
|
||||
Authoring rules — Hi Robot atom granularity, pi0.7-style short prompts:
|
||||
|
||||
- Each subtask = one COMPOSITE atomic skill the low-level policy can
|
||||
execute end-to-end. A "skill" bundles its own approach motion with
|
||||
its terminal action — do NOT split the approach off as its own
|
||||
subtask. The whole-arm policy already learns to reach as part of
|
||||
every manipulation primitive.
|
||||
- Write each subtask as an IMPERATIVE COMMAND, starting with one of
|
||||
these verbs (extend only when none fits):
|
||||
pick up <obj> — approach + grasp + lift in one subtask
|
||||
put <obj> on/in <loc> — transport + release in one subtask
|
||||
place <obj> on/in <loc> — synonym of "put"; pick one and stay consistent
|
||||
push <obj> — contact + linear shove
|
||||
pull <obj> — contact + linear retract
|
||||
turn <knob/dial/handle> — rotary actuation
|
||||
press <button> — single-press contact
|
||||
open <drawer/door/lid> — full open motion
|
||||
close <drawer/door/lid> — full close motion
|
||||
pour <src> into <dst> — tilt + flow
|
||||
insert <obj> into <slot>— alignment + push-fit
|
||||
go to <loc> — ONLY when no grasp / actuation follows
|
||||
(e.g. a pure relocation between phases).
|
||||
If the next subtask grasps something at
|
||||
that location, drop "go to ..." and just
|
||||
write "pick up ..." instead.
|
||||
- Forbidden ultra-fine splits — the VLM is NOT allowed to emit these
|
||||
as standalone subtasks; fold them into the parent composite:
|
||||
"move to X" → fold into "pick up X" (or whatever follows)
|
||||
"reach for X" → fold into "pick up X"
|
||||
"grasp X" → fold into "pick up X"
|
||||
"lift X" → fold into "pick up X" (or "put X on Y" if it's
|
||||
the transport phase of a place)
|
||||
"release X" → fold into "put X on Y" (or "place X in Y")
|
||||
- Keep it SHORT — a verb phrase, not a sentence. Drop articles
|
||||
("the", "a") and adverbs ("carefully", "slowly"). Add a "how"
|
||||
detail (which hand, which grasp point) ONLY when it is needed to
|
||||
disambiguate. Every subtask must begin with one of the verbs
|
||||
above (no leading nouns, no "then", no "first").
|
||||
- NEVER use third person. Never write "the robot", "the arm", "the
|
||||
gripper moves", "it picks up" — the robot is implied. Command it,
|
||||
do not describe it.
|
||||
- Use the exact object nouns from the task above. If the task says
|
||||
"cube", every subtask says "cube" — never switch to "block". If it
|
||||
says "box", never switch to "bin"/"container". Keep vocabulary
|
||||
consistent across the whole episode.
|
||||
- Good: "pick up blue cube", "put blue cube in box", "open drawer",
|
||||
"turn red knob", "press start button", "go to sink".
|
||||
- Bad: "move to blue cube" (approach as its own subtask — forbidden,
|
||||
must be folded into "pick up blue cube"); "the robot arm moves
|
||||
towards the blue cube" (third person, too long); "carefully pick
|
||||
up the cube" (adverb, article); "release the yellow block"
|
||||
("block" when the task said "cube", and "release" must be folded
|
||||
into a "put"/"place" subtask).
|
||||
- Subtasks are non-overlapping and cover the full episode in order.
|
||||
Choose the cut points yourself based on what you see in the video
|
||||
(gripper open/close events, contact, regrasps, transitions).
|
||||
- Each subtask spans at least {min_subtask_seconds} seconds. If a
|
||||
candidate span would be shorter, merge it into its neighbour
|
||||
rather than emitting it.
|
||||
- Do not exceed {max_steps} subtasks total. Fewer, larger composites
|
||||
are preferred over many micro-steps.
|
||||
- Every subtask's [start_time, end_time] must lie within
|
||||
[0.0, {episode_duration}] seconds.
|
||||
|
||||
SPECIAL CASES — verb disambiguation (each rule is narrowly visual and
|
||||
fires ONLY on the spatial situation it names; it must not change how you
|
||||
label any other situation):
|
||||
- STACK vs PUT: if an object is placed ON TOP OF another specific object
|
||||
(not on a flat table / shelf / counter), use "stack ... on ...", not
|
||||
"put". "stack blue book on green book", NOT "put blue book on table".
|
||||
- INSERT vs PUT: if an object goes INTO a fitted slot / hole / socket /
|
||||
receptacle (push-fit), use "insert ... into ...", not "put".
|
||||
- RETRIEVE/PICK-UP vs PUT (direction): watch the gripper. If it CLOSES
|
||||
on the object and the object moves WITH the hand, it is "pick up" /
|
||||
"retrieve" (object leaves its location). If the gripper OPENS and the
|
||||
object stays where the hand left it, it is "put" / "place" (object
|
||||
arrives at a location). Decide by which way the object moves, not by
|
||||
where the hand ends up.
|
||||
- POUR vs PUT: only use "pour" when the source is tilted and contents
|
||||
flow out; moving a full container without tilting is "put"/"place".
|
||||
|
||||
Output strictly valid JSON of shape:
|
||||
|
||||
{{
|
||||
"subtasks": [
|
||||
{{"text": "<short imperative verb phrase>", "start": <float>, "end": <float>}},
|
||||
...
|
||||
]
|
||||
}}
|
||||
@@ -1,67 +0,0 @@
|
||||
You are generating structured augmentations of a robot task instruction
|
||||
for training a language-conditioned policy. Unlike free-form rephrasing,
|
||||
your variants follow a NAMED 5-axis taxonomy — each axis omits or varies
|
||||
a specific element of the task while preserving its meaning.
|
||||
|
||||
Original task: "{base_task}"
|
||||
|
||||
Produce variants along five named axes. Each axis has a target count.
|
||||
The whole batch should expose the policy to maximum linguistic diversity
|
||||
WITHOUT changing what the robot is supposed to do.
|
||||
|
||||
Axes and target counts:
|
||||
|
||||
synonym_paraphrase ({n_synonym}):
|
||||
Different wording / verbs / sentence structure. ALL information
|
||||
from the original task is preserved — same object, same arm
|
||||
specification if present, same orientation if present, same grasp
|
||||
if present.
|
||||
|
||||
omit_arm ({n_omit_arm}):
|
||||
Drop the left/right/both arm specification from the task. Skip
|
||||
entirely (emit 0 entries) if the original task does NOT mention an
|
||||
arm. Do not invent an arm specification just to omit it.
|
||||
|
||||
omit_orientation ({n_omit_orientation}):
|
||||
Drop orientation cues (upright, sideways, facing the user,
|
||||
long-edge-first, etc.). Skip entirely if no orientation cue is
|
||||
present in the original task.
|
||||
|
||||
omit_grasp_method ({n_omit_grasp_method}):
|
||||
Drop the grip / grasp method specification (pinch, wrap, hold by
|
||||
the rim, etc.). Skip entirely if no grasp method is mentioned.
|
||||
|
||||
combined_omissions ({n_combined}):
|
||||
Combine TWO of the above omissions simultaneously (e.g. drop both
|
||||
arm and orientation). Skip entirely if fewer than two of (arm,
|
||||
orientation, grasp_method) appear in the original task.
|
||||
|
||||
Hard rules:
|
||||
- Each variant MUST preserve the core action, the target object, AND
|
||||
the goal / destination. Do not change which object is involved, where
|
||||
it goes, or the high-level action. "Navigate to the stove" may become
|
||||
"go to the stove" or "head over to the stove" — it must NEVER become
|
||||
"wander around the kitchen", "explore the room", or anything that
|
||||
drops or generalises the stove destination. If you cannot vary the
|
||||
wording without changing the goal, emit fewer variants.
|
||||
- Only the FIVE listed elements (wording, arm, orientation, grasp
|
||||
method, or a combination) may be varied or omitted. The verb's
|
||||
meaning, the object, and the destination are fixed.
|
||||
- Each variant is plain prose, no markdown, no quotes, no list numbers.
|
||||
- Each variant must be DISTINCT from every other variant in the entire
|
||||
output, both within and across axes. Near-duplicates are not allowed.
|
||||
- If an axis cannot reach its target count because the original task
|
||||
lacks the omittable element, emit fewer entries — do NOT pad the
|
||||
axis with paraphrases that belong to a different axis.
|
||||
- Variants should not all start with verbs — vary sentence structure
|
||||
(some imperative, some polite request, some question).
|
||||
|
||||
Output strictly valid JSON of shape:
|
||||
|
||||
{{
|
||||
"synonym_paraphrase": ["<v1>", "<v2>", ...],
|
||||
"omit_arm": ["<v1>", "<v2>", ...],
|
||||
"omit_orientation": ["<v1>", ...],
|
||||
"omit_grasp_method": ["<v1>", ...],
|
||||
"combined_omissions": ["<v1>", ...]
|
||||
}}
|
||||
@@ -1,32 +0,0 @@
|
||||
You are generating training data for a Hi Robot-style policy. We need
|
||||
{n} alternative phrasings of the same robot task so the policy sees
|
||||
diverse user prompts during training instead of the same canonical
|
||||
string repeated every frame.
|
||||
|
||||
Original task:
|
||||
"{base_task}"
|
||||
|
||||
Generate exactly {n} alternative phrasings of the same task. Vary:
|
||||
|
||||
- formality (casual / polite / curt)
|
||||
- verbosity (mostly short imperative; occasional polite request)
|
||||
- word choice (synonyms, different verbs)
|
||||
- sentence structure (imperative / question / suggestion)
|
||||
|
||||
Hard rules:
|
||||
- Each phrasing MUST preserve the exact meaning of the original task.
|
||||
Do not change which object is involved, the destination, or the
|
||||
action. Do not add extra steps. Do not invent new objects.
|
||||
- Each phrasing must be a short phrase or sentence, plain prose, no
|
||||
markdown, no quotes, no list numbers.
|
||||
- Phrasings must be distinct — no near-duplicates.
|
||||
- Output exactly {n} entries.
|
||||
|
||||
Output strictly valid JSON:
|
||||
{{
|
||||
"rephrasings": [
|
||||
"<phrasing 1>",
|
||||
"<phrasing 2>",
|
||||
...
|
||||
]
|
||||
}}
|
||||
@@ -1,17 +0,0 @@
|
||||
The video above shows a robot manipulation episode in full. Look at
|
||||
the entire video and describe in ONE concise sentence what the robot
|
||||
is doing.
|
||||
|
||||
Rules:
|
||||
- One sentence, in natural English, like a user instruction.
|
||||
- Capture the goal of the demonstration, not low-level motions.
|
||||
Example: "place the yellow cube into the red bin" — not "move the
|
||||
end-effector down 5cm and close the gripper".
|
||||
- 4 to 15 words. Plain prose, no markdown, no bullets, no quotes.
|
||||
- Do not invent objects or actions that aren't visible.
|
||||
- Do not output anything other than the JSON object below.
|
||||
|
||||
Output strictly valid JSON:
|
||||
{{
|
||||
"task": "<single concise sentence describing what the robot does in this video>"
|
||||
}}
|
||||
@@ -1,32 +0,0 @@
|
||||
You are generating a frame-grounded visual question/answer pair for
|
||||
chain-of-thought training. Reference: ECoT (Zawalski 2024) and Steerable
|
||||
Policies — both train policies on grounded features such as bounding box
|
||||
pixel coordinates, keypoints, counts, attributes, and spatial relations.
|
||||
|
||||
The frame shows a robot working on: "{episode_task}".
|
||||
|
||||
Question types and the EXACT answer JSON shape required for each:
|
||||
|
||||
bbox => {{"detections": [{{"label": "<obj>", "bbox_format": "xyxy",
|
||||
"bbox": [x1, y1, x2, y2]}}, ...]}}
|
||||
bbox is in pixel coordinates (x_min, y_min, x_max, y_max).
|
||||
ECoT example: "a white cup [124, 25, 176, 113]".
|
||||
|
||||
keypoint => {{"label": "<point>", "point_format": "xy",
|
||||
"point": [x, y]}}
|
||||
|
||||
count => {{"label": "<obj>", "count": <int>,
|
||||
"note": "<optional short note>"}}
|
||||
|
||||
attribute => {{"label": "<obj>", "attribute": "<color|shape|state|...>",
|
||||
"value": "<observed value>"}}
|
||||
|
||||
spatial => {{"subject": "<obj>", "relation": "<left_of|right_of|on|in|"
|
||||
"above|below|near>", "object": "<obj>"}}
|
||||
|
||||
Generate a question of type "{question_type}". Output strictly valid JSON:
|
||||
|
||||
{{
|
||||
"question": "<short, frame-grounded question>",
|
||||
"answer": <object whose shape matches the schema above>
|
||||
}}
|
||||
@@ -1,216 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
|
||||
# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
"""Datatrove-shaped reader.
|
||||
|
||||
The reader walks ``data/chunk-*/file-*.parquet`` and yields one record per
|
||||
episode containing:
|
||||
|
||||
- ``episode_index``: int
|
||||
- ``frame_timestamps``: tuple[float, ...]
|
||||
- ``frame_indices``: tuple[int, ...]
|
||||
- ``episode_task``: str (canonical task from ``meta/tasks.parquet``)
|
||||
- ``data_path``: pathlib.Path of the source parquet shard
|
||||
- ``frames_df``: pandas.DataFrame slice for the episode (only loaded on demand)
|
||||
|
||||
This shape lets each module operate per-episode without loading all parquet
|
||||
rows into memory at once.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from collections.abc import Iterator, Sequence
|
||||
from dataclasses import dataclass, field
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
|
||||
import pyarrow.parquet as pq
|
||||
|
||||
from lerobot.datasets.io_utils import load_tasks
|
||||
from lerobot.datasets.utils import DEFAULT_TASKS_PATH
|
||||
|
||||
|
||||
@dataclass
|
||||
class EpisodeRecord:
|
||||
"""Per-episode record yielded by the reader."""
|
||||
|
||||
episode_index: int
|
||||
episode_task: str
|
||||
frame_timestamps: tuple[float, ...]
|
||||
frame_indices: tuple[int, ...]
|
||||
data_path: Path
|
||||
row_offset: int # row offset within the parquet file where this episode starts
|
||||
row_count: int # number of rows for this episode
|
||||
|
||||
# Memoized parquet slice — populated on first ``frames_df()`` call so
|
||||
# repeat queries from different modules don't re-read the whole shard.
|
||||
_frames_df_cache: Any = field(default=None, init=False, repr=False, compare=False)
|
||||
|
||||
def frames_df(self): # type: ignore[no-untyped-def]
|
||||
"""Lazy-load the pandas slice for this episode (memoized)."""
|
||||
if self._frames_df_cache is None:
|
||||
import pandas as pd # noqa: PLC0415 - deferred for optional dataset extra
|
||||
|
||||
table = pq.read_table(self.data_path)
|
||||
df: pd.DataFrame = table.to_pandas()
|
||||
self._frames_df_cache = df.iloc[self.row_offset : self.row_offset + self.row_count].reset_index(
|
||||
drop=True
|
||||
)
|
||||
return self._frames_df_cache
|
||||
|
||||
|
||||
def reconstruct_subtask_spans(
|
||||
rows: Sequence[dict[str, Any]],
|
||||
*,
|
||||
episode_end_t: float | None = None,
|
||||
) -> list[dict[str, Any]]:
|
||||
"""Turn ``style="subtask"`` rows into ``{text, start, end}`` spans.
|
||||
|
||||
Each span's ``end`` is the next span's ``start``. The final span's
|
||||
``end`` defaults to its own ``start`` (zero-duration) — pass
|
||||
``episode_end_t`` to extend it to the episode's last frame instead,
|
||||
which is what downstream consumers (memory, interjection boundary
|
||||
selection) expect.
|
||||
|
||||
Used by the ``plan`` module (plan-update pass) and the
|
||||
``interjections`` module (interjection anchoring), which both need the
|
||||
same span shape.
|
||||
"""
|
||||
sorted_rows = sorted(
|
||||
(r for r in rows if r.get("style") == "subtask"),
|
||||
key=lambda r: float(r["timestamp"]),
|
||||
)
|
||||
spans: list[dict[str, Any]] = []
|
||||
for r in sorted_rows:
|
||||
t = float(r["timestamp"])
|
||||
if spans:
|
||||
spans[-1]["end"] = t
|
||||
spans.append({"text": r.get("content") or "", "start": t, "end": t})
|
||||
if spans and episode_end_t is not None and float(episode_end_t) > spans[-1]["start"]:
|
||||
spans[-1]["end"] = float(episode_end_t)
|
||||
return spans
|
||||
|
||||
|
||||
def snap_to_frame(t: float, frame_timestamps: Sequence[float]) -> float:
|
||||
"""Snap an arbitrary float to the nearest exact source frame timestamp.
|
||||
|
||||
Modules use this when emitting event-style rows so the row's
|
||||
timestamp matches a real parquet frame: event rows must land on an
|
||||
exact frame, otherwise the per-frame event lookup the writer does
|
||||
would never match them.
|
||||
"""
|
||||
if not frame_timestamps:
|
||||
return float(t)
|
||||
nearest = min(frame_timestamps, key=lambda f: abs(f - t))
|
||||
return float(nearest)
|
||||
|
||||
|
||||
def _load_tasks_lookup(root: Path) -> dict[int, str]:
|
||||
"""Map ``task_index -> task`` from ``meta/tasks.parquet``.
|
||||
|
||||
Returns an empty dict when the file is absent — the task description is
|
||||
derived later from the video if needed. Reuses the library-level
|
||||
:func:`lerobot.datasets.io_utils.load_tasks`, which returns the tasks
|
||||
frame indexed by task string with a ``task_index`` column.
|
||||
"""
|
||||
if not (root / DEFAULT_TASKS_PATH).exists():
|
||||
return {}
|
||||
tasks = load_tasks(root)
|
||||
return {int(idx): str(task) for task, idx in zip(tasks.index, tasks["task_index"], strict=True)}
|
||||
|
||||
|
||||
def iter_episodes(root: Path, *, only_episodes: tuple[int, ...] | None = None) -> Iterator[EpisodeRecord]:
|
||||
"""Yield :class:`EpisodeRecord` for every episode under ``root/data/``.
|
||||
|
||||
Episodes are yielded in ascending ``episode_index`` order. The reader does
|
||||
not assume a specific chunk/file layout: it scans every ``*.parquet``
|
||||
under ``data/`` and groups by ``episode_index``.
|
||||
"""
|
||||
tasks = _load_tasks_lookup(root)
|
||||
data_dir = root / "data"
|
||||
parquet_files = sorted(data_dir.rglob("*.parquet"))
|
||||
|
||||
only_set = set(only_episodes) if only_episodes is not None else None
|
||||
|
||||
for path in parquet_files:
|
||||
yield from _iter_one_path(path, tasks, only_set)
|
||||
|
||||
|
||||
def _iter_one_path(path: Path, tasks: dict[int, str], only_set: set[int] | None) -> Iterator[EpisodeRecord]:
|
||||
table = pq.read_table(path)
|
||||
names = table.column_names
|
||||
if "episode_index" not in names:
|
||||
return
|
||||
episode_col = table.column("episode_index").to_pylist()
|
||||
timestamp_col = (
|
||||
table.column("timestamp").to_pylist() if "timestamp" in names else [0.0] * len(episode_col)
|
||||
)
|
||||
frame_col = (
|
||||
table.column("frame_index").to_pylist() if "frame_index" in names else list(range(len(episode_col)))
|
||||
)
|
||||
task_col = table.column("task_index").to_pylist() if "task_index" in names else None
|
||||
|
||||
def _build(
|
||||
ep: int,
|
||||
start: int,
|
||||
end: int,
|
||||
task_idx: int | None,
|
||||
ts_buf: list[float],
|
||||
fi_buf: list[int],
|
||||
) -> EpisodeRecord | None:
|
||||
if only_set is not None and ep not in only_set:
|
||||
return None
|
||||
task = tasks.get(task_idx, "") if task_idx is not None else ""
|
||||
return EpisodeRecord(
|
||||
episode_index=ep,
|
||||
episode_task=task,
|
||||
frame_timestamps=tuple(ts_buf),
|
||||
frame_indices=tuple(fi_buf),
|
||||
data_path=path,
|
||||
row_offset=start,
|
||||
row_count=end - start,
|
||||
)
|
||||
|
||||
cur_ep: int | None = None
|
||||
start_offset = 0
|
||||
ts_buf: list[float] = []
|
||||
fi_buf: list[int] = []
|
||||
cur_task_idx: int | None = None
|
||||
|
||||
for i, ep in enumerate(episode_col):
|
||||
if cur_ep is None:
|
||||
cur_ep = ep
|
||||
start_offset = i
|
||||
ts_buf = [timestamp_col[i]]
|
||||
fi_buf = [frame_col[i]]
|
||||
cur_task_idx = task_col[i] if task_col is not None else None
|
||||
continue
|
||||
if ep != cur_ep:
|
||||
rec = _build(cur_ep, start_offset, i, cur_task_idx, ts_buf, fi_buf)
|
||||
if rec is not None:
|
||||
yield rec
|
||||
cur_ep = ep
|
||||
start_offset = i
|
||||
ts_buf = [timestamp_col[i]]
|
||||
fi_buf = [frame_col[i]]
|
||||
cur_task_idx = task_col[i] if task_col is not None else None
|
||||
else:
|
||||
ts_buf.append(timestamp_col[i])
|
||||
fi_buf.append(frame_col[i])
|
||||
|
||||
if cur_ep is not None:
|
||||
rec = _build(cur_ep, start_offset, len(episode_col), cur_task_idx, ts_buf, fi_buf)
|
||||
if rec is not None:
|
||||
yield rec
|
||||
@@ -1,92 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
|
||||
# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
"""Per-episode staging.
|
||||
|
||||
Each module writes its raw output as a JSONL file under
|
||||
``<staging_dir>/episode_{ep:06d}/<module>.jsonl``. The writer reads back this
|
||||
staging tree and partitions rows into the two language columns.
|
||||
|
||||
JSONL is preferred over parquet here because the staging artifact is meant to
|
||||
be human-inspectable, easy to diff between prompt iterations, and trivially
|
||||
appended to. The final dataset format is parquet; staging is just an
|
||||
intermediate.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
from collections.abc import Iterable
|
||||
from dataclasses import dataclass
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
|
||||
ModuleName = str
|
||||
|
||||
_MODULES: tuple[ModuleName, ...] = (
|
||||
"plan",
|
||||
"interjections",
|
||||
"vqa",
|
||||
)
|
||||
|
||||
|
||||
@dataclass
|
||||
class EpisodeStaging:
|
||||
"""Filesystem layout for a single episode's staged module outputs."""
|
||||
|
||||
root: Path
|
||||
episode_index: int
|
||||
|
||||
@property
|
||||
def episode_dir(self) -> Path:
|
||||
return self.root / f"episode_{self.episode_index:06d}"
|
||||
|
||||
def path_for(self, module: ModuleName) -> Path:
|
||||
if module not in _MODULES:
|
||||
raise ValueError(f"Unknown module {module!r}; expected one of {_MODULES}")
|
||||
return self.episode_dir / f"{module}.jsonl"
|
||||
|
||||
def write(self, module: ModuleName, rows: Iterable[dict[str, Any]]) -> Path:
|
||||
path = self.path_for(module)
|
||||
path.parent.mkdir(parents=True, exist_ok=True)
|
||||
# Atomic replace: a crash mid-write would otherwise leave a
|
||||
# half-written JSONL file that ``read()`` would then fail to
|
||||
# parse. Write to a sibling .tmp and rename so the target path
|
||||
# only ever points at a complete file.
|
||||
tmp_path = path.with_suffix(path.suffix + ".tmp")
|
||||
with tmp_path.open("w", encoding="utf-8") as f:
|
||||
for row in rows:
|
||||
f.write(json.dumps(row, ensure_ascii=False, sort_keys=True))
|
||||
f.write("\n")
|
||||
tmp_path.replace(path)
|
||||
return path
|
||||
|
||||
def read(self, module: ModuleName) -> list[dict[str, Any]]:
|
||||
path = self.path_for(module)
|
||||
if not path.exists():
|
||||
return []
|
||||
out: list[dict[str, Any]] = []
|
||||
with path.open(encoding="utf-8") as f:
|
||||
for line in f:
|
||||
line = line.strip()
|
||||
if line:
|
||||
out.append(json.loads(line))
|
||||
return out
|
||||
|
||||
def read_all(self) -> dict[ModuleName, list[dict[str, Any]]]:
|
||||
return {m: self.read(m) for m in _MODULES}
|
||||
|
||||
def has(self, module: ModuleName) -> bool:
|
||||
return self.path_for(module).exists()
|
||||
@@ -1,332 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
|
||||
# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
"""Pre-write validation against staged outputs.
|
||||
|
||||
Runs after all three modules have written their per-episode artifacts but
|
||||
*before* the writer rewrites parquet shards. The validator never touches
|
||||
parquet; it only inspects the staging tree and the source frame timestamps
|
||||
exposed by :class:`EpisodeRecord`.
|
||||
|
||||
Checks (per the plan's "Intermediate staging and validation" section):
|
||||
|
||||
- exact timestamp alignment against source frame timestamps
|
||||
- no orphan speech / interjection pairs
|
||||
- plan / memory emission consistency (events have a paired persistent row)
|
||||
- VQA assistant ``content`` is valid JSON (one of bbox / keypoint / count /
|
||||
attribute / spatial)
|
||||
- every row maps to its correct column under :func:`column_for_style`
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import logging
|
||||
from collections.abc import Iterable, Sequence
|
||||
from dataclasses import dataclass, field
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
|
||||
from lerobot.datasets.language import (
|
||||
LANGUAGE_EVENTS,
|
||||
LANGUAGE_PERSISTENT,
|
||||
column_for_style,
|
||||
is_view_dependent_style,
|
||||
validate_camera_field,
|
||||
)
|
||||
|
||||
from .reader import EpisodeRecord
|
||||
from .staging import EpisodeStaging
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
@dataclass
|
||||
class ValidationReport:
|
||||
"""Outcome of one validation pass across all episodes."""
|
||||
|
||||
errors: list[str] = field(default_factory=list)
|
||||
warnings: list[str] = field(default_factory=list)
|
||||
episodes_checked: int = 0
|
||||
|
||||
@property
|
||||
def ok(self) -> bool:
|
||||
return not self.errors
|
||||
|
||||
def add_error(self, message: str) -> None:
|
||||
self.errors.append(message)
|
||||
|
||||
def add_warning(self, message: str) -> None:
|
||||
self.warnings.append(message)
|
||||
|
||||
def summary(self) -> str:
|
||||
return f"checked={self.episodes_checked} errors={len(self.errors)} warnings={len(self.warnings)}"
|
||||
|
||||
|
||||
VQA_ANSWER_SHAPES: dict[str, set[str]] = {
|
||||
"bbox": {"detections"},
|
||||
"keypoint": {"label", "point_format", "point"},
|
||||
"count": {"label", "count"},
|
||||
"attribute": {"label", "attribute", "value"},
|
||||
"spatial": {"subject", "relation", "object"},
|
||||
}
|
||||
|
||||
|
||||
def classify_vqa_answer(payload: Any) -> str | None:
|
||||
"""Best-effort classification of a VQA answer payload to a question type."""
|
||||
if not isinstance(payload, dict):
|
||||
return None
|
||||
keys = set(payload.keys())
|
||||
for kind, required in VQA_ANSWER_SHAPES.items():
|
||||
if required.issubset(keys):
|
||||
return kind
|
||||
return None
|
||||
|
||||
|
||||
@dataclass
|
||||
class StagingValidator:
|
||||
"""Walks the staging tree and produces a :class:`ValidationReport`."""
|
||||
|
||||
timestamp_atol: float = 0.0 # exact-match by default
|
||||
dataset_camera_keys: tuple[str, ...] | None = None
|
||||
"""Known ``observation.images.*`` keys on the dataset. When set, the
|
||||
validator additionally enforces that every view-dependent row's
|
||||
``camera`` field references one of these keys. Pass ``None`` (default)
|
||||
to skip that cross-check (e.g. in unit tests with no real dataset)."""
|
||||
|
||||
def validate(
|
||||
self,
|
||||
records: Sequence[EpisodeRecord],
|
||||
staging_dir: Path,
|
||||
) -> ValidationReport:
|
||||
report = ValidationReport()
|
||||
for record in records:
|
||||
self._validate_episode(record, staging_dir, report)
|
||||
report.episodes_checked += 1
|
||||
return report
|
||||
|
||||
def _validate_episode(
|
||||
self,
|
||||
record: EpisodeRecord,
|
||||
staging_dir: Path,
|
||||
report: ValidationReport,
|
||||
) -> None:
|
||||
staging = EpisodeStaging(staging_dir, record.episode_index)
|
||||
staged = staging.read_all()
|
||||
all_rows: list[dict[str, Any]] = []
|
||||
for module_name, rows in staged.items():
|
||||
for row in rows:
|
||||
row = {**row, "_module": module_name}
|
||||
all_rows.append(row)
|
||||
|
||||
frame_ts = set(record.frame_timestamps)
|
||||
|
||||
events: list[dict[str, Any]] = []
|
||||
persistent: list[dict[str, Any]] = []
|
||||
for row in all_rows:
|
||||
self._check_column_routing(row, report, record.episode_index)
|
||||
self._check_camera_field(row, report, record.episode_index, self.dataset_camera_keys)
|
||||
# ``_check_column_routing`` already recorded any unknown-style error;
|
||||
# don't let the same ``column_for_style`` lookup raise here uncaught.
|
||||
try:
|
||||
column = column_for_style(row.get("style"))
|
||||
except ValueError:
|
||||
continue
|
||||
if column == LANGUAGE_PERSISTENT:
|
||||
persistent.append(row)
|
||||
else:
|
||||
events.append(row)
|
||||
|
||||
for row in events:
|
||||
self._check_event_timestamp_alignment(row, frame_ts, report, record.episode_index)
|
||||
|
||||
self._check_speech_interjection_pairs(events, report, record.episode_index)
|
||||
self._check_plan_memory_consistency(persistent, events, report, record.episode_index)
|
||||
self._check_vqa_json(events, report, record.episode_index)
|
||||
self._check_vqa_uniqueness_per_frame_camera(events, report, record.episode_index)
|
||||
|
||||
def _check_camera_field(
|
||||
self,
|
||||
row: dict[str, Any],
|
||||
report: ValidationReport,
|
||||
episode_index: int,
|
||||
dataset_camera_keys: Sequence[str] | None,
|
||||
) -> None:
|
||||
"""Enforce the camera invariant + that the key matches the dataset's cameras."""
|
||||
style = row.get("style")
|
||||
camera = row.get("camera")
|
||||
try:
|
||||
validate_camera_field(style, camera)
|
||||
except ValueError as exc:
|
||||
report.add_error(f"ep={episode_index} module={row.get('_module')}: {exc}")
|
||||
return
|
||||
if is_view_dependent_style(style) and dataset_camera_keys and camera not in dataset_camera_keys:
|
||||
report.add_error(
|
||||
f"ep={episode_index} module={row.get('_module')}: camera {camera!r} on style "
|
||||
f"{style!r} is not one of the dataset's video keys {sorted(dataset_camera_keys)!r}"
|
||||
)
|
||||
|
||||
def _check_vqa_uniqueness_per_frame_camera(
|
||||
self,
|
||||
events: Iterable[dict[str, Any]],
|
||||
report: ValidationReport,
|
||||
episode_index: int,
|
||||
) -> None:
|
||||
"""Ensure at most one (vqa, user) and one (vqa, assistant) per (t, camera)."""
|
||||
counts: dict[tuple[float, str, str], int] = {}
|
||||
for row in events:
|
||||
if row.get("style") != "vqa":
|
||||
continue
|
||||
ts = row.get("timestamp")
|
||||
camera = row.get("camera")
|
||||
role = row.get("role")
|
||||
if ts is None or camera is None or role is None:
|
||||
continue # other validators flag these
|
||||
key = (float(ts), str(camera), str(role))
|
||||
counts[key] = counts.get(key, 0) + 1
|
||||
for (ts, camera, role), n in counts.items():
|
||||
if n > 1:
|
||||
report.add_error(
|
||||
f"ep={episode_index}: {n} duplicate vqa rows at t={ts} "
|
||||
f"camera={camera!r} role={role!r}; expected at most one per (t, camera, role)"
|
||||
)
|
||||
|
||||
def _check_column_routing(
|
||||
self,
|
||||
row: dict[str, Any],
|
||||
report: ValidationReport,
|
||||
episode_index: int,
|
||||
) -> None:
|
||||
style = row.get("style")
|
||||
module = row.get("_module")
|
||||
try:
|
||||
target_col = column_for_style(style)
|
||||
except ValueError:
|
||||
report.add_error(f"ep={episode_index} module={module}: unknown style {style!r}")
|
||||
return
|
||||
if module == "plan" and target_col != LANGUAGE_PERSISTENT:
|
||||
report.add_error(
|
||||
f"ep={episode_index} module=plan emitted style {style!r} that routes to {target_col} (must be persistent)"
|
||||
)
|
||||
if module in {"interjections", "vqa"} and target_col != LANGUAGE_EVENTS:
|
||||
report.add_error(
|
||||
f"ep={episode_index} module={module} emitted style {style!r} that routes to {target_col} (must be events)"
|
||||
)
|
||||
|
||||
def _check_event_timestamp_alignment(
|
||||
self,
|
||||
row: dict[str, Any],
|
||||
frame_ts: set[float],
|
||||
report: ValidationReport,
|
||||
episode_index: int,
|
||||
) -> None:
|
||||
ts = row.get("timestamp")
|
||||
if ts is None:
|
||||
report.add_error(f"ep={episode_index}: event row missing timestamp: {row!r}")
|
||||
return
|
||||
if self.timestamp_atol == 0.0:
|
||||
if float(ts) not in frame_ts:
|
||||
report.add_error(
|
||||
f"ep={episode_index}: event row timestamp {ts!r} does not match any source frame timestamp"
|
||||
)
|
||||
else:
|
||||
if not any(abs(float(ts) - f) <= self.timestamp_atol for f in frame_ts):
|
||||
report.add_error(
|
||||
f"ep={episode_index}: event row timestamp {ts!r} not within {self.timestamp_atol}s of any frame"
|
||||
)
|
||||
|
||||
def _check_speech_interjection_pairs(
|
||||
self,
|
||||
events: Iterable[dict[str, Any]],
|
||||
report: ValidationReport,
|
||||
episode_index: int,
|
||||
) -> None:
|
||||
speech_ts: dict[float, int] = {}
|
||||
interjection_ts: dict[float, int] = {}
|
||||
for row in events:
|
||||
ts = row.get("timestamp")
|
||||
if ts is None:
|
||||
continue
|
||||
ts_f = float(ts)
|
||||
if row.get("style") is None and row.get("role") == "assistant":
|
||||
speech_ts[ts_f] = speech_ts.get(ts_f, 0) + 1
|
||||
if row.get("style") == "interjection":
|
||||
interjection_ts[ts_f] = interjection_ts.get(ts_f, 0) + 1
|
||||
|
||||
for ts in interjection_ts:
|
||||
if ts not in speech_ts:
|
||||
report.add_error(f"ep={episode_index}: interjection at t={ts} has no paired speech atom")
|
||||
|
||||
def _check_plan_memory_consistency(
|
||||
self,
|
||||
persistent: Sequence[dict[str, Any]],
|
||||
events: Sequence[dict[str, Any]],
|
||||
report: ValidationReport,
|
||||
episode_index: int,
|
||||
) -> None:
|
||||
plan_ts = sorted({float(r["timestamp"]) for r in persistent if r.get("style") == "plan"})
|
||||
memory_ts = sorted({float(r["timestamp"]) for r in persistent if r.get("style") == "memory"})
|
||||
subtask_ts = sorted({float(r["timestamp"]) for r in persistent if r.get("style") == "subtask"})
|
||||
interjection_ts = sorted(
|
||||
{
|
||||
float(r["timestamp"])
|
||||
for r in events
|
||||
if r.get("style") == "interjection" and r.get("timestamp") is not None
|
||||
}
|
||||
)
|
||||
|
||||
if persistent and not plan_ts:
|
||||
report.add_warning(f"ep={episode_index}: persistent rows present but no plan emitted")
|
||||
# every interjection should have a same-timestamp plan refresh
|
||||
for ts in interjection_ts:
|
||||
if ts not in set(plan_ts):
|
||||
report.add_error(
|
||||
f"ep={episode_index}: interjection at t={ts} has no co-timestamped plan update"
|
||||
)
|
||||
# memory should be emitted at subtask boundaries (subset relation)
|
||||
if memory_ts and subtask_ts:
|
||||
mem_set = set(memory_ts)
|
||||
sub_set = set(subtask_ts)
|
||||
stray = sorted(mem_set - sub_set)
|
||||
if stray:
|
||||
report.add_warning(f"ep={episode_index}: memory rows at {stray} not at any subtask boundary")
|
||||
|
||||
def _check_vqa_json(
|
||||
self,
|
||||
events: Iterable[dict[str, Any]],
|
||||
report: ValidationReport,
|
||||
episode_index: int,
|
||||
) -> None:
|
||||
for row in events:
|
||||
if row.get("style") != "vqa" or row.get("role") != "assistant":
|
||||
continue
|
||||
content = row.get("content")
|
||||
if content is None:
|
||||
report.add_error(
|
||||
f"ep={episode_index}: VQA assistant row at t={row.get('timestamp')} has null content"
|
||||
)
|
||||
continue
|
||||
try:
|
||||
payload = json.loads(content)
|
||||
except (TypeError, ValueError) as exc:
|
||||
report.add_error(
|
||||
f"ep={episode_index}: VQA assistant content not valid JSON at t={row.get('timestamp')}: {exc}"
|
||||
)
|
||||
continue
|
||||
shape = classify_vqa_answer(payload)
|
||||
if shape is None:
|
||||
report.add_error(
|
||||
f"ep={episode_index}: VQA assistant payload at t={row.get('timestamp')} does not match any known shape: keys={list(payload) if isinstance(payload, dict) else type(payload).__name__}"
|
||||
)
|
||||
@@ -1,617 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
|
||||
# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
"""Shared Qwen-VL client.
|
||||
|
||||
The pipeline uses a single shared VLM across modules. vLLM is preferred when
|
||||
available (high throughput, JSON-guided decoding); transformers is the
|
||||
fallback. A ``stub`` backend is used for unit tests so fixtures never call
|
||||
into a real model.
|
||||
|
||||
The client speaks one method, :meth:`VlmClient.generate_json`, which:
|
||||
|
||||
- accepts a list of OpenAI/HF-style multimodal messages,
|
||||
- requests JSON output from the server,
|
||||
- batches requests transparently,
|
||||
- and reprompts once on a JSON parse failure with an inline correction
|
||||
message before raising.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import atexit
|
||||
import base64
|
||||
import io
|
||||
import json
|
||||
import os
|
||||
import shlex
|
||||
import signal
|
||||
import subprocess
|
||||
import sys
|
||||
import threading
|
||||
import time
|
||||
import urllib.request
|
||||
from collections.abc import Callable, Sequence
|
||||
from concurrent.futures import ThreadPoolExecutor
|
||||
from dataclasses import dataclass
|
||||
from typing import Any, Protocol
|
||||
|
||||
from .config import VlmConfig
|
||||
|
||||
|
||||
class VlmClient(Protocol):
|
||||
"""Protocol every backend must implement."""
|
||||
|
||||
def generate_json(
|
||||
self,
|
||||
messages_batch: Sequence[Sequence[dict[str, Any]]],
|
||||
*,
|
||||
max_new_tokens: int | None = None,
|
||||
temperature: float | None = None,
|
||||
) -> list[Any]:
|
||||
"""Generate one JSON-decoded response per messages list."""
|
||||
|
||||
|
||||
@dataclass
|
||||
class StubVlmClient:
|
||||
"""Deterministic stub used in unit tests.
|
||||
|
||||
A test passes a callable that maps the *last user message text* (or, if
|
||||
that is empty, the full message list) to a JSON-serializable response.
|
||||
"""
|
||||
|
||||
responder: Callable[[Sequence[dict[str, Any]]], Any]
|
||||
|
||||
def generate_json(
|
||||
self,
|
||||
messages_batch: Sequence[Sequence[dict[str, Any]]],
|
||||
*,
|
||||
max_new_tokens: int | None = None,
|
||||
temperature: float | None = None,
|
||||
) -> list[Any]:
|
||||
return [self.responder(list(messages)) for messages in messages_batch]
|
||||
|
||||
|
||||
def _strip_to_json(text: str) -> Any:
|
||||
text = text.strip()
|
||||
# Strip <think>...</think> blocks (Qwen3 Thinking style)
|
||||
while "<think>" in text and "</think>" in text:
|
||||
start = text.find("<think>")
|
||||
end = text.find("</think>", start) + len("</think>")
|
||||
text = (text[:start] + text[end:]).strip()
|
||||
# Strip ```json ... ``` fences from chat-tuned backbones
|
||||
if text.startswith("```"):
|
||||
first = text.find("\n")
|
||||
last = text.rfind("```")
|
||||
if first != -1 and last != -1 and last > first:
|
||||
text = text[first + 1 : last].strip()
|
||||
try:
|
||||
return json.loads(text)
|
||||
except (ValueError, json.JSONDecodeError):
|
||||
pass
|
||||
# Fall back to extracting the first balanced {...} block.
|
||||
obj_text = _extract_first_json_object(text)
|
||||
if obj_text is None:
|
||||
raise json.JSONDecodeError("No JSON object found", text, 0)
|
||||
return json.loads(obj_text)
|
||||
|
||||
|
||||
def _extract_first_json_object(text: str) -> str | None:
|
||||
"""Return the first balanced ``{...}`` substring, ignoring braces in
|
||||
string literals. Returns ``None`` if no balanced block is found."""
|
||||
start = text.find("{")
|
||||
if start < 0:
|
||||
return None
|
||||
depth = 0
|
||||
in_string = False
|
||||
escape = False
|
||||
for i in range(start, len(text)):
|
||||
ch = text[i]
|
||||
if escape:
|
||||
escape = False
|
||||
continue
|
||||
if ch == "\\":
|
||||
escape = True
|
||||
continue
|
||||
# Note: ``escape`` is always False here — the ``if escape`` branch
|
||||
# above already handled and reset it.
|
||||
if ch == '"':
|
||||
in_string = not in_string
|
||||
continue
|
||||
if in_string:
|
||||
continue
|
||||
if ch == "{":
|
||||
depth += 1
|
||||
elif ch == "}":
|
||||
depth -= 1
|
||||
if depth == 0:
|
||||
return text[start : i + 1]
|
||||
return None
|
||||
|
||||
|
||||
@dataclass
|
||||
class _GenericTextClient:
|
||||
"""Wraps any text-generation callable in JSON-mode + one-retry semantics."""
|
||||
|
||||
generate_text: Callable[[Sequence[Sequence[dict[str, Any]]], int, float], list[str]]
|
||||
config: VlmConfig
|
||||
|
||||
def generate_json(
|
||||
self,
|
||||
messages_batch: Sequence[Sequence[dict[str, Any]]],
|
||||
*,
|
||||
max_new_tokens: int | None = None,
|
||||
temperature: float | None = None,
|
||||
) -> list[Any]:
|
||||
max_tok = max_new_tokens if max_new_tokens is not None else self.config.max_new_tokens
|
||||
temp = temperature if temperature is not None else self.config.temperature
|
||||
raw = self.generate_text(messages_batch, max_tok, temp)
|
||||
out: list[Any] = []
|
||||
for messages, text in zip(messages_batch, raw, strict=True):
|
||||
try:
|
||||
out.append(_strip_to_json(text))
|
||||
continue
|
||||
except (ValueError, json.JSONDecodeError):
|
||||
pass
|
||||
retry = list(messages) + [
|
||||
{"role": "assistant", "content": text},
|
||||
{
|
||||
"role": "user",
|
||||
"content": (
|
||||
"Your previous reply was not valid JSON. "
|
||||
"Reply with strictly valid JSON, no prose, no fences."
|
||||
),
|
||||
},
|
||||
]
|
||||
retry_text = self.generate_text([retry], max_tok, temp)[0]
|
||||
try:
|
||||
out.append(_strip_to_json(retry_text))
|
||||
except (ValueError, json.JSONDecodeError):
|
||||
# After retry: log preview and return None instead of crashing
|
||||
# the whole pipeline. Modules treat None as "skip".
|
||||
preview = retry_text.strip().replace("\n", " ")[:200]
|
||||
print(
|
||||
f"[vlm] WARNING: failed to parse JSON after retry; preview: {preview!r}",
|
||||
flush=True,
|
||||
)
|
||||
out.append(None)
|
||||
return out
|
||||
|
||||
|
||||
def make_vlm_client(config: VlmConfig) -> VlmClient:
|
||||
"""Build the shared VLM client.
|
||||
|
||||
Only the ``openai`` backend is supported for now. The shipped workflow
|
||||
is Hugging Face Jobs (``examples/annotations/run_hf_job.py``): it boots
|
||||
a vLLM server inside the ``vllm/vllm-openai`` image and the pipeline
|
||||
talks to it over the OpenAI-compatible API (``--vlm.backend=openai``,
|
||||
optionally auto-spawning the server via ``auto_serve`` /
|
||||
``serve_command``). The former in-process ``vllm`` / ``transformers``
|
||||
backends were removed to keep the support surface to the HF Jobs path.
|
||||
|
||||
For ``stub``, construct :class:`StubVlmClient` directly with a responder
|
||||
callable; it is rejected here to make accidental misuse obvious.
|
||||
"""
|
||||
if config.backend == "openai":
|
||||
return _make_openai_client(config)
|
||||
if config.backend == "stub":
|
||||
raise ValueError(
|
||||
"Use StubVlmClient(...) directly for the stub backend; make_vlm_client builds real clients."
|
||||
)
|
||||
if config.backend in {"vllm", "transformers"}:
|
||||
raise ValueError(
|
||||
f"backend={config.backend!r} (in-process local model) is not supported for now — "
|
||||
"only backend='openai' (the Hugging Face Jobs flow) is. Run the pipeline via "
|
||||
"examples/annotations/run_hf_job.py, which serves the model with vLLM in the "
|
||||
"vllm/vllm-openai image and talks to it over the OpenAI-compatible API."
|
||||
)
|
||||
raise ValueError(f"Unknown VLM backend: {config.backend!r}")
|
||||
|
||||
|
||||
def _make_openai_client(config: VlmConfig) -> VlmClient:
|
||||
"""Backend that talks to any OpenAI-compatible server.
|
||||
|
||||
Compatible with ``vllm serve``, ``transformers serve``,
|
||||
``ktransformers serve``, and hosted endpoints. By default the server
|
||||
is expected to be already running. Set ``auto_serve=True`` to have
|
||||
this client spawn one (default: ``transformers serve``), wait until
|
||||
it's ready, and tear it down on process exit.
|
||||
|
||||
Image blocks ``{"type":"image", "image":<PIL.Image>}`` are
|
||||
auto-converted to ``image_url`` data-URLs. Video blocks
|
||||
``{"type":"video", "video":[<PIL>...]}`` are forwarded as
|
||||
multi-frame ``video_url`` items where supported.
|
||||
"""
|
||||
try:
|
||||
from openai import OpenAI # type: ignore[import-not-found]
|
||||
except ImportError as exc:
|
||||
raise ImportError(
|
||||
"openai package is required for backend='openai'. Install with `pip install openai`."
|
||||
) from exc
|
||||
|
||||
api_base = config.api_base
|
||||
api_key = config.api_key
|
||||
auto_serve = config.auto_serve
|
||||
api_bases: list[str] = [api_base]
|
||||
|
||||
print(
|
||||
f"[lerobot-annotate] backend=openai model={config.model_id} "
|
||||
f"api_base={api_base} auto_serve={auto_serve}",
|
||||
flush=True,
|
||||
)
|
||||
if auto_serve:
|
||||
if config.parallel_servers > 1:
|
||||
print(
|
||||
f"[lerobot-annotate] spawning {config.parallel_servers} parallel servers",
|
||||
flush=True,
|
||||
)
|
||||
api_bases = _spawn_parallel_inference_servers(config)
|
||||
elif _server_is_up(api_base):
|
||||
print(f"[lerobot-annotate] reusing server already up at {api_base}", flush=True)
|
||||
else:
|
||||
print("[lerobot-annotate] no server reachable; spawning one", flush=True)
|
||||
api_base = _spawn_inference_server(config)
|
||||
api_bases = [api_base]
|
||||
print(f"[lerobot-annotate] server ready at {api_base}", flush=True)
|
||||
|
||||
clients = [OpenAI(base_url=base, api_key=api_key) for base in api_bases]
|
||||
# round-robin counter for parallel mode
|
||||
rr_counter = {"i": 0}
|
||||
|
||||
# ``mm_processor_kwargs`` is a vllm-specific extra; transformers serve
|
||||
# rejects it with HTTP 422. Send it only when explicitly opted in via
|
||||
# an env var (e.g. ``LEROBOT_OPENAI_SEND_MM_KWARGS=1`` for vllm).
|
||||
send_mm_kwargs = os.environ.get("LEROBOT_OPENAI_SEND_MM_KWARGS", "").lower() in {"1", "true", "yes"}
|
||||
|
||||
rr_lock = threading.Lock()
|
||||
|
||||
def _one_call(messages: Sequence[dict[str, Any]], max_tok: int, temp: float) -> str:
|
||||
api_messages, mm_kwargs = _to_openai_messages(messages)
|
||||
kwargs: dict[str, Any] = {
|
||||
"model": config.model_id,
|
||||
"messages": api_messages,
|
||||
"max_tokens": max_tok,
|
||||
"temperature": temp,
|
||||
}
|
||||
extra_body: dict[str, Any] = {}
|
||||
if send_mm_kwargs and mm_kwargs:
|
||||
extra_body["mm_processor_kwargs"] = {**mm_kwargs, "do_sample_frames": True}
|
||||
if config.chat_template_kwargs:
|
||||
extra_body["chat_template_kwargs"] = config.chat_template_kwargs
|
||||
if extra_body:
|
||||
kwargs["extra_body"] = extra_body
|
||||
with rr_lock:
|
||||
chosen = clients[rr_counter["i"] % len(clients)]
|
||||
rr_counter["i"] += 1
|
||||
response = chosen.chat.completions.create(**kwargs)
|
||||
return response.choices[0].message.content or ""
|
||||
|
||||
def _gen(batch: Sequence[Sequence[dict[str, Any]]], max_tok: int, temp: float) -> list[str]:
|
||||
if len(batch) <= 1 or config.client_concurrency <= 1:
|
||||
return [_one_call(messages, max_tok, temp) for messages in batch]
|
||||
# Parallel fan-out — vllm batches these on the server side.
|
||||
max_workers = min(config.client_concurrency, len(batch))
|
||||
with ThreadPoolExecutor(max_workers=max_workers) as pool:
|
||||
futures = [pool.submit(_one_call, messages, max_tok, temp) for messages in batch]
|
||||
return [f.result() for f in futures]
|
||||
|
||||
return _GenericTextClient(_gen, config)
|
||||
|
||||
|
||||
def _bind_serve_port(cmd: str, port: int) -> str:
|
||||
"""Bind a serve command to ``port``: substitute a ``{port}`` placeholder
|
||||
if present, else append ``--port`` when the command omits it (leaving an
|
||||
explicit ``--port`` untouched). Shared by the single- and parallel-server
|
||||
paths so a serve_command never reaches the server with a literal
|
||||
``{port}``."""
|
||||
if "{port}" in cmd:
|
||||
return cmd.replace("{port}", str(port))
|
||||
if "--port" not in cmd:
|
||||
return f"{cmd} --port {port}"
|
||||
return cmd
|
||||
|
||||
|
||||
def _spawn_parallel_inference_servers(config: VlmConfig) -> list[str]:
|
||||
"""Spawn ``config.parallel_servers`` independent vllm replicas.
|
||||
|
||||
Each replica:
|
||||
- is pinned to a single GPU via ``CUDA_VISIBLE_DEVICES``
|
||||
- listens on ``serve_port + i``
|
||||
- is shut down via the same atexit hook as the single-server path
|
||||
|
||||
Returns the list of ``api_base`` URLs the client should round-robin
|
||||
across.
|
||||
"""
|
||||
n = config.parallel_servers
|
||||
api_bases: list[str] = []
|
||||
procs: list[subprocess.Popen] = []
|
||||
ready_events: list[threading.Event] = []
|
||||
# Multiple readiness signals — uvicorn's own banner is suppressed at
|
||||
# ``--uvicorn-log-level warning``, so we also accept vllm's own
|
||||
# "Starting vLLM API server" line and the route-listing line. The
|
||||
# HTTP probe below is the ultimate fallback.
|
||||
ready_markers = (
|
||||
"Uvicorn running",
|
||||
"Application startup complete",
|
||||
"Starting vLLM API server",
|
||||
"Available routes are",
|
||||
)
|
||||
# Single lock for all server-stream threads so multibyte chars from
|
||||
# different servers don't interleave and tear UTF-8 sequences.
|
||||
print_lock = threading.Lock()
|
||||
|
||||
base_cmd = config.serve_command or (
|
||||
f"vllm serve {shlex.quote(config.model_id)} "
|
||||
f"--tensor-parallel-size 1 "
|
||||
f"--max-model-len {config.max_model_len or 32768} "
|
||||
f"--uvicorn-log-level warning"
|
||||
)
|
||||
|
||||
num_gpus = config.num_gpus if config.num_gpus > 0 else n
|
||||
for i in range(n):
|
||||
port = config.serve_port + i
|
||||
gpu = i % num_gpus
|
||||
env = os.environ.copy()
|
||||
env["CUDA_VISIBLE_DEVICES"] = str(gpu)
|
||||
cmd = _bind_serve_port(base_cmd, port)
|
||||
api_base = f"http://localhost:{port}/v1"
|
||||
api_bases.append(api_base)
|
||||
print(f"[server-{i}] launching on GPU {gpu} port {port}: {cmd}", flush=True)
|
||||
proc = subprocess.Popen(
|
||||
shlex.split(cmd),
|
||||
stdout=subprocess.PIPE,
|
||||
stderr=subprocess.STDOUT,
|
||||
text=True,
|
||||
bufsize=1,
|
||||
env=env,
|
||||
)
|
||||
procs.append(proc)
|
||||
ready = threading.Event()
|
||||
ready_events.append(ready)
|
||||
|
||||
def _stream(idx: int, p: subprocess.Popen, ev: threading.Event) -> None:
|
||||
# Read whole lines and emit each line atomically under the
|
||||
# shared print_lock so output from N servers stays readable.
|
||||
assert p.stdout is not None
|
||||
for line in iter(p.stdout.readline, ""):
|
||||
with print_lock:
|
||||
sys.stdout.write(f"[server-{idx}] {line}")
|
||||
if not line.endswith(("\n", "\r")):
|
||||
sys.stdout.write("\n")
|
||||
sys.stdout.flush()
|
||||
if any(m in line for m in ready_markers):
|
||||
ev.set()
|
||||
|
||||
threading.Thread(target=_stream, args=(i, proc, ready), daemon=True).start()
|
||||
|
||||
def _probe(idx: int, base: str, ev: threading.Event, p: subprocess.Popen) -> None:
|
||||
while not ev.is_set() and p.poll() is None:
|
||||
if _server_is_up(base):
|
||||
print(f"[server-{idx}] ready (http probe)", flush=True)
|
||||
ev.set()
|
||||
return
|
||||
time.sleep(2)
|
||||
|
||||
threading.Thread(target=_probe, args=(i, api_base, ready, proc), daemon=True).start()
|
||||
|
||||
def _shutdown() -> None:
|
||||
for i, p in enumerate(procs):
|
||||
if p.poll() is None:
|
||||
print(f"[server-{i}] stopping pid={p.pid}", flush=True)
|
||||
p.send_signal(signal.SIGINT)
|
||||
for p in procs:
|
||||
try:
|
||||
p.wait(timeout=15)
|
||||
except subprocess.TimeoutExpired:
|
||||
p.kill()
|
||||
p.wait(timeout=5)
|
||||
|
||||
atexit.register(_shutdown)
|
||||
|
||||
deadline = time.monotonic() + config.serve_ready_timeout_s
|
||||
while any(not ev.is_set() for ev in ready_events) and time.monotonic() < deadline:
|
||||
for i, p in enumerate(procs):
|
||||
if p.poll() is not None:
|
||||
raise RuntimeError(
|
||||
f"[server-{i}] inference server exited unexpectedly with rc={p.returncode}"
|
||||
)
|
||||
time.sleep(2)
|
||||
if any(not ev.is_set() for ev in ready_events):
|
||||
raise RuntimeError(f"[server] not all replicas became ready within {config.serve_ready_timeout_s}s")
|
||||
print(f"[lerobot-annotate] all {n} servers ready: {api_bases}", flush=True)
|
||||
return api_bases
|
||||
|
||||
|
||||
def _server_is_up(api_base: str) -> bool:
|
||||
"""Return True if ``api_base/models`` answers 200 within 2 seconds."""
|
||||
url = api_base.rstrip("/") + "/models"
|
||||
# ``api_base`` is the user-configured local-server URL we just spawned
|
||||
# or the user passed in via ``--vlm.api_base``; the bandit B310 warning
|
||||
# is for arbitrary user-controlled URLs with file:/ schemes which
|
||||
# cannot reach this code path.
|
||||
try:
|
||||
with urllib.request.urlopen(url, timeout=2) as resp: # noqa: S310 # nosec B310
|
||||
return resp.status == 200
|
||||
except Exception: # noqa: BLE001
|
||||
return False
|
||||
|
||||
|
||||
def _spawn_inference_server(config: VlmConfig) -> str:
|
||||
"""Spawn ``transformers serve`` (or ``serve_command``), wait until it
|
||||
accepts ``/v1/models``, and register a shutdown hook.
|
||||
|
||||
Streams the server's stdout/stderr to the parent terminal in
|
||||
real-time on a background thread so users can see model-load
|
||||
progress and errors as they happen.
|
||||
|
||||
Returns the full ``api_base`` URL the OpenAI client should use.
|
||||
"""
|
||||
cmd = config.serve_command
|
||||
if not cmd:
|
||||
cmd = (
|
||||
f"transformers serve {shlex.quote(config.model_id)} "
|
||||
f"--port {config.serve_port} --continuous-batching"
|
||||
)
|
||||
# Bind the single server to ``serve_port`` (what ``api_base`` below
|
||||
# targets): substitute a literal ``{port}`` placeholder, else append
|
||||
# ``--port``. Without this a serve_command carrying ``{port}`` would
|
||||
# reach the server unsubstituted and fail to parse.
|
||||
cmd = _bind_serve_port(cmd, config.serve_port)
|
||||
api_base = f"http://localhost:{config.serve_port}/v1"
|
||||
print(f"[server] launching: {cmd}", flush=True)
|
||||
proc = subprocess.Popen(
|
||||
shlex.split(cmd),
|
||||
stdout=subprocess.PIPE,
|
||||
stderr=subprocess.STDOUT,
|
||||
text=True,
|
||||
bufsize=1,
|
||||
)
|
||||
|
||||
# Watch the server output for the uvicorn readiness banner. This is
|
||||
# more reliable than polling /v1/models because transformers serve
|
||||
# rescans its cache on every model-list request, which can exceed
|
||||
# the urllib timeout and trigger an infinite probe loop.
|
||||
ready_event = threading.Event()
|
||||
# See _spawn_parallel_inference_servers for why we accept these.
|
||||
ready_markers = (
|
||||
"Uvicorn running",
|
||||
"Application startup complete",
|
||||
"Starting vLLM API server",
|
||||
"Available routes are",
|
||||
)
|
||||
|
||||
def _probe() -> None:
|
||||
while not ready_event.is_set() and proc.poll() is None:
|
||||
if _server_is_up(api_base):
|
||||
print("[server] ready (http probe)", flush=True)
|
||||
ready_event.set()
|
||||
return
|
||||
time.sleep(2)
|
||||
|
||||
threading.Thread(target=_probe, daemon=True).start()
|
||||
|
||||
def _stream_output() -> None:
|
||||
# Read raw chunks instead of iterating lines so tqdm progress
|
||||
# bars (which overwrite using \r) flush in real time.
|
||||
assert proc.stdout is not None
|
||||
buf = ""
|
||||
prefix_started = False
|
||||
while True:
|
||||
ch = proc.stdout.read(1)
|
||||
if ch == "":
|
||||
# process exited; flush any tail
|
||||
if buf:
|
||||
sys.stdout.write(buf)
|
||||
sys.stdout.flush()
|
||||
return
|
||||
if not prefix_started:
|
||||
sys.stdout.write("[server] ")
|
||||
prefix_started = True
|
||||
sys.stdout.write(ch)
|
||||
sys.stdout.flush()
|
||||
buf += ch
|
||||
if ch in ("\n", "\r"):
|
||||
if any(marker in buf for marker in ready_markers):
|
||||
ready_event.set()
|
||||
buf = ""
|
||||
prefix_started = False
|
||||
|
||||
threading.Thread(target=_stream_output, daemon=True).start()
|
||||
|
||||
def _shutdown() -> None:
|
||||
if proc.poll() is None:
|
||||
print(f"[server] stopping pid={proc.pid}", flush=True)
|
||||
proc.send_signal(signal.SIGINT)
|
||||
try:
|
||||
proc.wait(timeout=15)
|
||||
except subprocess.TimeoutExpired:
|
||||
proc.kill()
|
||||
proc.wait(timeout=5)
|
||||
|
||||
atexit.register(_shutdown)
|
||||
|
||||
deadline = time.monotonic() + config.serve_ready_timeout_s
|
||||
while time.monotonic() < deadline:
|
||||
if proc.poll() is not None:
|
||||
raise RuntimeError(
|
||||
f"[server] inference server exited unexpectedly with rc={proc.returncode}. "
|
||||
f"See [server] log lines above for the cause."
|
||||
)
|
||||
if ready_event.wait(timeout=2):
|
||||
return api_base
|
||||
proc.terminate()
|
||||
raise RuntimeError(f"[server] did not become ready within {config.serve_ready_timeout_s}s")
|
||||
|
||||
|
||||
def _to_openai_messages(
|
||||
messages: Sequence[dict[str, Any]],
|
||||
) -> tuple[list[dict[str, Any]], dict[str, Any]]:
|
||||
"""Convert internal messages to OpenAI chat format.
|
||||
|
||||
Returns ``(api_messages, mm_kwargs)``. Multimodal-processor kwargs
|
||||
(``fps`` from ``video_url`` blocks) are extracted out so the caller
|
||||
can pass them via ``extra_body.mm_processor_kwargs`` rather than
|
||||
inside the content blocks (which transformers serve rejects).
|
||||
|
||||
File-URL video blocks are inlined as base64 data URLs.
|
||||
"""
|
||||
out_messages: list[dict[str, Any]] = []
|
||||
mm_kwargs: dict[str, Any] = {}
|
||||
for message in messages:
|
||||
content = message.get("content")
|
||||
if not isinstance(content, list):
|
||||
out_messages.append({"role": message["role"], "content": content})
|
||||
continue
|
||||
out_blocks: list[dict[str, Any]] = []
|
||||
for block in content:
|
||||
block_type = block.get("type") if isinstance(block, dict) else None
|
||||
if block_type == "text":
|
||||
out_blocks.append({"type": "text", "text": block.get("text", "")})
|
||||
elif block_type == "image":
|
||||
out_blocks.append(
|
||||
{"type": "image_url", "image_url": {"url": _pil_to_data_url(block["image"])}}
|
||||
)
|
||||
elif block_type == "video":
|
||||
frames = block.get("video", [])
|
||||
for img in frames:
|
||||
out_blocks.append({"type": "image_url", "image_url": {"url": _pil_to_data_url(img)}})
|
||||
elif block_type == "video_url":
|
||||
video_url = dict(block["video_url"])
|
||||
url = video_url.get("url", "")
|
||||
if url.startswith("file://"):
|
||||
video_url["url"] = _file_to_data_url(url[len("file://") :])
|
||||
out_blocks.append({"type": "video_url", "video_url": video_url})
|
||||
fps = block.get("fps")
|
||||
if fps is not None:
|
||||
mm_kwargs["fps"] = fps
|
||||
else:
|
||||
out_blocks.append(block)
|
||||
out_messages.append({"role": message["role"], "content": out_blocks})
|
||||
return out_messages, mm_kwargs
|
||||
|
||||
|
||||
def _file_to_data_url(path: str) -> str:
|
||||
"""Read a local video file and return a base64 ``data:video/mp4`` URL."""
|
||||
with open(path, "rb") as f:
|
||||
b64 = base64.b64encode(f.read()).decode("ascii")
|
||||
return f"data:video/mp4;base64,{b64}"
|
||||
|
||||
|
||||
def _pil_to_data_url(image: Any) -> str:
|
||||
"""Encode a PIL.Image as a base64 data URL."""
|
||||
buf = io.BytesIO()
|
||||
image.save(buf, format="PNG")
|
||||
b64 = base64.b64encode(buf.getvalue()).decode("ascii")
|
||||
return f"data:image/png;base64,{b64}"
|
||||
@@ -1,341 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
|
||||
# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
"""Final parquet rewrite.
|
||||
|
||||
For every episode the writer:
|
||||
|
||||
1. reads the staged module outputs,
|
||||
2. partitions them into a persistent slice (PERSISTENT_STYLES) and an event
|
||||
slice (EVENT_ONLY_STYLES + style=None tool-call atoms),
|
||||
3. sorts each slice deterministically,
|
||||
4. broadcasts the persistent slice across every frame in the episode,
|
||||
5. for each frame, materializes the sublist of event rows whose timestamp
|
||||
exactly equals that frame's timestamp,
|
||||
6. drops the legacy ``subtask_index`` column,
|
||||
7. writes the parquet shard back in place.
|
||||
|
||||
The writer does NOT add a dataset-level ``tools`` column. Tool *calls* are
|
||||
emitted per-row via the existing ``tool_calls`` field on the v3.1 row
|
||||
struct for every speech atom. The tool *schema* (the description
|
||||
of the ``say`` function and its parameters) is a fixed code constant —
|
||||
``SAY_TOOL_SCHEMA`` below — and downstream chat-template consumers import
|
||||
it directly rather than reading a redundant per-row column.
|
||||
|
||||
Invariants enforced here (and re-checked by the validator):
|
||||
|
||||
- per-episode persistent slice is byte-identical across every frame;
|
||||
- ``language_events`` rows on a frame all have ``timestamp == frame_ts``
|
||||
(timestamps come straight from the source parquet — never recomputed);
|
||||
- every row passes ``column_for_style(style)``.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
from collections import defaultdict
|
||||
from collections.abc import Sequence
|
||||
from dataclasses import dataclass
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
|
||||
import pyarrow as pa
|
||||
import pyarrow.parquet as pq
|
||||
|
||||
from lerobot.datasets.io_utils import write_table_one_row_group_per_episode
|
||||
from lerobot.datasets.language import (
|
||||
EVENT_ONLY_STYLES,
|
||||
LANGUAGE_EVENTS,
|
||||
LANGUAGE_PERSISTENT,
|
||||
PERSISTENT_STYLES,
|
||||
column_for_style,
|
||||
validate_camera_field,
|
||||
)
|
||||
|
||||
from .reader import EpisodeRecord
|
||||
from .staging import EpisodeStaging
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
# Tool schema constants live in lerobot.datasets.language — single
|
||||
# source of truth. Re-exported here so existing imports
|
||||
# (``from lerobot.annotations.steerable_pipeline.writer import SAY_TOOL_SCHEMA``)
|
||||
# keep working.
|
||||
from lerobot.datasets.language import DEFAULT_TOOLS, SAY_TOOL_SCHEMA # noqa: F401, E402
|
||||
|
||||
|
||||
def _row_persistent_sort_key(row: dict[str, Any]) -> tuple:
|
||||
return (float(row["timestamp"]), row.get("style") or "", row.get("role") or "")
|
||||
|
||||
|
||||
def _row_event_sort_key(row: dict[str, Any]) -> tuple:
|
||||
# events are bucketed per-frame, but within a frame we still want determinism
|
||||
return (
|
||||
row.get("style") or "",
|
||||
row.get("role") or "",
|
||||
row.get("camera") or "",
|
||||
)
|
||||
|
||||
|
||||
def _normalize_row(row: dict[str, Any], style: str | None, *, with_timestamp: bool) -> dict[str, Any]:
|
||||
"""Coerce a staged row into the language-column struct shape.
|
||||
|
||||
Key order matches ``PERSISTENT_ROW_FIELDS`` / ``EVENT_ROW_FIELDS`` — the
|
||||
writer infers the parquet struct schema from insertion order, so
|
||||
``timestamp`` (persistent rows only) sits between ``style`` and ``camera``.
|
||||
"""
|
||||
camera = row.get("camera")
|
||||
validate_camera_field(style, camera)
|
||||
out: dict[str, Any] = {
|
||||
"role": str(row["role"]),
|
||||
"content": None if row.get("content") is None else str(row["content"]),
|
||||
"style": style,
|
||||
}
|
||||
if with_timestamp:
|
||||
out["timestamp"] = float(row["timestamp"])
|
||||
out["camera"] = None if camera is None else str(camera)
|
||||
out["tool_calls"] = _normalize_tool_calls(row.get("tool_calls"))
|
||||
return out
|
||||
|
||||
|
||||
def _normalize_persistent_row(row: dict[str, Any]) -> dict[str, Any]:
|
||||
"""Coerce a staged row into the persistent column's struct shape."""
|
||||
style = row.get("style")
|
||||
if style not in PERSISTENT_STYLES:
|
||||
raise ValueError(
|
||||
f"persistent slice contains row with non-persistent style {style!r}; "
|
||||
"row would be misrouted under column_for_style()"
|
||||
)
|
||||
if "timestamp" not in row:
|
||||
raise ValueError(f"persistent row missing timestamp: {row!r}")
|
||||
if "role" not in row:
|
||||
# Friendly error from the writer instead of a raw KeyError below;
|
||||
# the validator doesn't check ``role`` yet.
|
||||
raise ValueError(f"persistent row missing role: {row!r}")
|
||||
return _normalize_row(row, style, with_timestamp=True)
|
||||
|
||||
|
||||
def _normalize_event_row(row: dict[str, Any]) -> dict[str, Any]:
|
||||
"""Coerce a staged row into the event column's struct shape (no timestamp)."""
|
||||
style = row.get("style")
|
||||
if style is not None and style not in EVENT_ONLY_STYLES:
|
||||
raise ValueError(
|
||||
f"event slice contains row with style {style!r}; expected None or one of {EVENT_ONLY_STYLES}"
|
||||
)
|
||||
if column_for_style(style) != LANGUAGE_EVENTS:
|
||||
raise ValueError(f"event row with style {style!r} would not route to language_events")
|
||||
if "role" not in row:
|
||||
raise ValueError(f"event row missing role: {row!r}")
|
||||
return _normalize_row(row, style, with_timestamp=False)
|
||||
|
||||
|
||||
def _normalize_tool_calls(value: Any) -> list[Any] | None:
|
||||
if value is None:
|
||||
return None
|
||||
if not isinstance(value, list):
|
||||
raise ValueError(f"tool_calls must be a list or None, got {type(value).__name__}")
|
||||
return list(value)
|
||||
|
||||
|
||||
def _validate_atom_invariants(row: dict[str, Any]) -> None:
|
||||
"""At-least-one of content/tool_calls; style=None implies tool_calls."""
|
||||
has_content = row.get("content") is not None
|
||||
has_tools = row.get("tool_calls") is not None
|
||||
if not (has_content or has_tools):
|
||||
raise ValueError(f"row has neither content nor tool_calls: {row!r}")
|
||||
if row.get("style") is None and not has_tools:
|
||||
raise ValueError(f"style=None requires tool_calls: {row!r}")
|
||||
|
||||
|
||||
def _validate_speech_atom(row: dict[str, Any]) -> None:
|
||||
"""Speech atoms: role=assistant, style=None, content=None, say tool call."""
|
||||
if row.get("style") is not None:
|
||||
return # not a speech atom
|
||||
if row.get("role") != "assistant":
|
||||
raise ValueError(f"speech atom must have role=assistant: {row!r}")
|
||||
if row.get("content") is not None:
|
||||
raise ValueError(f"speech atom must have content=null: {row!r}")
|
||||
tool_calls = row.get("tool_calls")
|
||||
if not tool_calls or not isinstance(tool_calls, list):
|
||||
raise ValueError(f"speech atom must have non-empty tool_calls list: {row!r}")
|
||||
first = tool_calls[0]
|
||||
if not isinstance(first, dict):
|
||||
raise ValueError(f"speech atom tool_calls[0] must be a dict: {row!r}")
|
||||
if first.get("type") != "function":
|
||||
raise ValueError(f"speech atom tool_calls[0].type must be 'function': {row!r}")
|
||||
fn = first.get("function") or {}
|
||||
if fn.get("name") != "say":
|
||||
raise ValueError(f"speech atom tool_calls[0].function.name must be 'say': {row!r}")
|
||||
args = fn.get("arguments") or {}
|
||||
if not isinstance(args, dict) or "text" not in args or not isinstance(args["text"], str):
|
||||
raise ValueError(f"speech atom must carry 'text' string in arguments: {row!r}")
|
||||
|
||||
|
||||
@dataclass
|
||||
class LanguageColumnsWriter:
|
||||
"""Rewrite ``data/chunk-*/file-*.parquet`` with the two language columns."""
|
||||
|
||||
drop_existing_subtask_index: bool = True
|
||||
|
||||
def write_all(
|
||||
self,
|
||||
records: Sequence[EpisodeRecord],
|
||||
staging_dir: Path,
|
||||
root: Path,
|
||||
) -> list[Path]:
|
||||
episodes_by_path: dict[Path, list[EpisodeRecord]] = defaultdict(list)
|
||||
for record in records:
|
||||
episodes_by_path[record.data_path].append(record)
|
||||
|
||||
written: list[Path] = []
|
||||
for path, eps in episodes_by_path.items():
|
||||
self._rewrite_one(path, eps, staging_dir, root)
|
||||
written.append(path)
|
||||
return written
|
||||
|
||||
def _rewrite_one(
|
||||
self,
|
||||
path: Path,
|
||||
episodes: Sequence[EpisodeRecord],
|
||||
staging_dir: Path,
|
||||
root: Path,
|
||||
) -> None:
|
||||
table = pq.read_table(path)
|
||||
n_rows = table.num_rows
|
||||
|
||||
# Ensure we cover every episode in the file. Episodes that don't have
|
||||
# staging artifacts are passed through with empty annotation lists —
|
||||
# this keeps the writer idempotent and safe for partial reruns.
|
||||
staged_per_ep: dict[int, dict[str, list[dict[str, Any]]]] = {}
|
||||
for record in episodes:
|
||||
staging = EpisodeStaging(staging_dir, record.episode_index)
|
||||
staged_per_ep[record.episode_index] = staging.read_all()
|
||||
|
||||
persistent_by_ep: dict[int, list[dict[str, Any]]] = {}
|
||||
events_by_ep_ts: dict[int, dict[float, list[dict[str, Any]]]] = {}
|
||||
|
||||
for ep_index, ep_staged in staged_per_ep.items():
|
||||
persistent_rows: list[dict[str, Any]] = []
|
||||
event_rows: list[dict[str, Any]] = [] # carry timestamp until bucketed
|
||||
for _module_name, rows in ep_staged.items():
|
||||
for row in rows:
|
||||
style = row.get("style")
|
||||
if column_for_style(style) == LANGUAGE_PERSISTENT:
|
||||
persistent_rows.append(row)
|
||||
else:
|
||||
event_rows.append(row)
|
||||
|
||||
persistent_rows.sort(key=_row_persistent_sort_key)
|
||||
normalized_persistent = []
|
||||
for r in persistent_rows:
|
||||
_validate_atom_invariants(r)
|
||||
_validate_speech_atom(r)
|
||||
normalized_persistent.append(_normalize_persistent_row(r))
|
||||
persistent_by_ep[ep_index] = normalized_persistent
|
||||
|
||||
buckets: dict[float, list[dict[str, Any]]] = defaultdict(list)
|
||||
for r in event_rows:
|
||||
_validate_atom_invariants(r)
|
||||
_validate_speech_atom(r)
|
||||
ts = float(r["timestamp"])
|
||||
buckets[ts].append(_normalize_event_row(r))
|
||||
for ts in list(buckets.keys()):
|
||||
buckets[ts].sort(key=_row_event_sort_key)
|
||||
events_by_ep_ts[ep_index] = buckets
|
||||
|
||||
episode_col = (
|
||||
table.column("episode_index").to_pylist() if "episode_index" in table.column_names else None
|
||||
)
|
||||
ts_col = table.column("timestamp").to_pylist() if "timestamp" in table.column_names else None
|
||||
if episode_col is None or ts_col is None:
|
||||
raise ValueError(f"{path} is missing 'episode_index' or 'timestamp' — required by the writer.")
|
||||
|
||||
per_row_persistent: list[list[dict[str, Any]]] = []
|
||||
per_row_events: list[list[dict[str, Any]]] = []
|
||||
for i in range(n_rows):
|
||||
ep = episode_col[i]
|
||||
ts = float(ts_col[i])
|
||||
per_row_persistent.append(persistent_by_ep.get(ep, []))
|
||||
buckets = events_by_ep_ts.get(ep, {})
|
||||
per_row_events.append(buckets.get(ts, []))
|
||||
|
||||
new_table = self._materialize_table(
|
||||
table, per_row_persistent, per_row_events, drop_old=self.drop_existing_subtask_index
|
||||
)
|
||||
# Re-emit one row group per episode (a bulk pq.write_table would collapse
|
||||
# them into one). Write to a sibling tmp path and atomically rename so a
|
||||
# crash mid-write can't leave a half-written shard.
|
||||
tmp_path = path.with_suffix(path.suffix + ".tmp")
|
||||
write_table_one_row_group_per_episode(new_table, tmp_path)
|
||||
tmp_path.replace(path)
|
||||
|
||||
def _materialize_table(
|
||||
self,
|
||||
table: pa.Table,
|
||||
persistent: list[list[dict[str, Any]]],
|
||||
events: list[list[dict[str, Any]]],
|
||||
*,
|
||||
drop_old: bool,
|
||||
) -> pa.Table:
|
||||
cols = []
|
||||
names = []
|
||||
for name in table.column_names:
|
||||
if drop_old and name == "subtask_index":
|
||||
continue
|
||||
if name in (LANGUAGE_PERSISTENT, LANGUAGE_EVENTS):
|
||||
continue # we'll re-add canonical versions
|
||||
# Strip any legacy ``tools`` column previously emitted by older
|
||||
# writers — the schema no longer uses it (constant lives in
|
||||
# SAY_TOOL_SCHEMA / DEFAULT_TOOLS).
|
||||
if name == "tools":
|
||||
continue
|
||||
cols.append(table.column(name))
|
||||
names.append(name)
|
||||
|
||||
# We let pyarrow infer struct/list schema rather than passing the
|
||||
# canonical type from `lerobot.datasets.language` directly: that type
|
||||
# uses `pa.json_()` for the `tool_calls` element type, which
|
||||
# `pa.array(..., type=...)` cannot materialize from Python lists on
|
||||
# current pyarrow versions. The inferred schema round-trips through
|
||||
# parquet and `LeRobotDataset` correctly — `tests/datasets/test_language.py`
|
||||
# exercises the same flow.
|
||||
persistent_arr = pa.array(persistent)
|
||||
events_arr = pa.array(events)
|
||||
|
||||
cols.extend([persistent_arr, events_arr])
|
||||
names.extend([LANGUAGE_PERSISTENT, LANGUAGE_EVENTS])
|
||||
|
||||
return pa.Table.from_arrays(cols, names=names)
|
||||
|
||||
|
||||
def speech_atom(timestamp: float, text: str) -> dict[str, Any]:
|
||||
"""Build a canonical speech tool-call atom for the events column."""
|
||||
return {
|
||||
"role": "assistant",
|
||||
"content": None,
|
||||
"style": None,
|
||||
"timestamp": float(timestamp),
|
||||
"camera": None,
|
||||
"tool_calls": [
|
||||
{
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": "say",
|
||||
"arguments": {"text": text},
|
||||
},
|
||||
}
|
||||
],
|
||||
}
|
||||
@@ -1,30 +0,0 @@
|
||||
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
"""
|
||||
Async inference server/client.
|
||||
|
||||
Requires: ``pip install 'lerobot[async]'``
|
||||
|
||||
Available modules (import directly)::
|
||||
|
||||
from lerobot.async_inference.policy_server import ...
|
||||
from lerobot.async_inference.robot_client import ...
|
||||
"""
|
||||
|
||||
from lerobot.utils.import_utils import require_package
|
||||
|
||||
require_package("grpcio", extra="async", import_name="grpc")
|
||||
|
||||
__all__: list[str] = []
|
||||
@@ -1,203 +0,0 @@
|
||||
# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
from collections.abc import Callable
|
||||
from dataclasses import dataclass, field
|
||||
|
||||
import torch
|
||||
|
||||
from lerobot.robots.config import RobotConfig
|
||||
|
||||
from .constants import (
|
||||
DEFAULT_FPS,
|
||||
DEFAULT_INFERENCE_LATENCY,
|
||||
DEFAULT_OBS_QUEUE_TIMEOUT,
|
||||
)
|
||||
|
||||
# Aggregate function registry for CLI usage
|
||||
AGGREGATE_FUNCTIONS = {
|
||||
"weighted_average": lambda old, new: 0.3 * old + 0.7 * new,
|
||||
"latest_only": lambda old, new: new,
|
||||
"average": lambda old, new: 0.5 * old + 0.5 * new,
|
||||
"conservative": lambda old, new: 0.7 * old + 0.3 * new,
|
||||
}
|
||||
|
||||
|
||||
def get_aggregate_function(name: str) -> Callable[[torch.Tensor, torch.Tensor], torch.Tensor]:
|
||||
"""Get aggregate function by name from registry."""
|
||||
if name not in AGGREGATE_FUNCTIONS:
|
||||
available = list(AGGREGATE_FUNCTIONS.keys())
|
||||
raise ValueError(f"Unknown aggregate function '{name}'. Available: {available}")
|
||||
return AGGREGATE_FUNCTIONS[name]
|
||||
|
||||
|
||||
@dataclass
|
||||
class PolicyServerConfig:
|
||||
"""Configuration for PolicyServer.
|
||||
|
||||
This class defines all configurable parameters for the PolicyServer,
|
||||
including networking settings and action chunking specifications.
|
||||
"""
|
||||
|
||||
# Networking configuration
|
||||
host: str = field(default="localhost", metadata={"help": "Host address to bind the server to"})
|
||||
port: int = field(default=8080, metadata={"help": "Port number to bind the server to"})
|
||||
|
||||
# Timing configuration
|
||||
fps: int = field(default=DEFAULT_FPS, metadata={"help": "Frames per second"})
|
||||
inference_latency: float = field(
|
||||
default=DEFAULT_INFERENCE_LATENCY, metadata={"help": "Target inference latency in seconds"}
|
||||
)
|
||||
|
||||
obs_queue_timeout: float = field(
|
||||
default=DEFAULT_OBS_QUEUE_TIMEOUT, metadata={"help": "Timeout for observation queue in seconds"}
|
||||
)
|
||||
|
||||
def __post_init__(self):
|
||||
"""Validate configuration after initialization."""
|
||||
if self.port < 1 or self.port > 65535:
|
||||
raise ValueError(f"Port must be between 1 and 65535, got {self.port}")
|
||||
|
||||
if self.environment_dt <= 0:
|
||||
raise ValueError(f"environment_dt must be positive, got {self.environment_dt}")
|
||||
|
||||
if self.inference_latency < 0:
|
||||
raise ValueError(f"inference_latency must be non-negative, got {self.inference_latency}")
|
||||
|
||||
if self.obs_queue_timeout < 0:
|
||||
raise ValueError(f"obs_queue_timeout must be non-negative, got {self.obs_queue_timeout}")
|
||||
|
||||
@classmethod
|
||||
def from_dict(cls, config_dict: dict) -> "PolicyServerConfig":
|
||||
"""Create a PolicyServerConfig from a dictionary."""
|
||||
return cls(**config_dict)
|
||||
|
||||
@property
|
||||
def environment_dt(self) -> float:
|
||||
"""Environment time step, in seconds"""
|
||||
return 1 / self.fps
|
||||
|
||||
def to_dict(self) -> dict:
|
||||
"""Convert the configuration to a dictionary."""
|
||||
return {
|
||||
"host": self.host,
|
||||
"port": self.port,
|
||||
"fps": self.fps,
|
||||
"environment_dt": self.environment_dt,
|
||||
"inference_latency": self.inference_latency,
|
||||
}
|
||||
|
||||
|
||||
@dataclass
|
||||
class RobotClientConfig:
|
||||
"""Configuration for RobotClient.
|
||||
|
||||
This class defines all configurable parameters for the RobotClient,
|
||||
including network connection, policy settings, and control behavior.
|
||||
"""
|
||||
|
||||
# Policy configuration
|
||||
policy_type: str = field(metadata={"help": "Type of policy to use"})
|
||||
pretrained_name_or_path: str = field(metadata={"help": "Pretrained model name or path"})
|
||||
|
||||
# Robot configuration (for CLI usage - robot instance will be created from this)
|
||||
robot: RobotConfig = field(metadata={"help": "Robot configuration"})
|
||||
|
||||
# Policies typically output K actions at max, but we can use less to avoid wasting bandwidth (as actions
|
||||
# would be aggregated on the client side anyway, depending on the value of `chunk_size_threshold`)
|
||||
actions_per_chunk: int = field(metadata={"help": "Number of actions per chunk"})
|
||||
|
||||
# Task instruction for the robot to execute (e.g., 'fold my tshirt')
|
||||
task: str = field(default="", metadata={"help": "Task instruction for the robot to execute"})
|
||||
|
||||
# Network configuration
|
||||
server_address: str = field(default="localhost:8080", metadata={"help": "Server address to connect to"})
|
||||
|
||||
# Device configuration
|
||||
policy_device: str = field(default="cpu", metadata={"help": "Device for policy inference"})
|
||||
client_device: str = field(
|
||||
default="cpu",
|
||||
metadata={
|
||||
"help": "Device to move actions to after receiving from server (e.g., for downstream planners)"
|
||||
},
|
||||
)
|
||||
|
||||
# Control behavior configuration
|
||||
chunk_size_threshold: float = field(default=0.5, metadata={"help": "Threshold for chunk size control"})
|
||||
fps: int = field(default=DEFAULT_FPS, metadata={"help": "Frames per second"})
|
||||
|
||||
# Aggregate function configuration (CLI-compatible)
|
||||
aggregate_fn_name: str = field(
|
||||
default="weighted_average",
|
||||
metadata={"help": f"Name of aggregate function to use. Options: {list(AGGREGATE_FUNCTIONS.keys())}"},
|
||||
)
|
||||
|
||||
# Debug configuration
|
||||
debug_visualize_queue_size: bool = field(
|
||||
default=False, metadata={"help": "Visualize the action queue size"}
|
||||
)
|
||||
|
||||
@property
|
||||
def environment_dt(self) -> float:
|
||||
"""Environment time step, in seconds"""
|
||||
return 1 / self.fps
|
||||
|
||||
def __post_init__(self):
|
||||
"""Validate configuration after initialization."""
|
||||
if not self.server_address:
|
||||
raise ValueError("server_address cannot be empty")
|
||||
|
||||
if not self.policy_type:
|
||||
raise ValueError("policy_type cannot be empty")
|
||||
|
||||
if not self.pretrained_name_or_path:
|
||||
raise ValueError("pretrained_name_or_path cannot be empty")
|
||||
|
||||
if not self.policy_device:
|
||||
raise ValueError("policy_device cannot be empty")
|
||||
|
||||
if not self.client_device:
|
||||
raise ValueError("client_device cannot be empty")
|
||||
|
||||
if self.chunk_size_threshold < 0 or self.chunk_size_threshold > 1:
|
||||
raise ValueError(f"chunk_size_threshold must be between 0 and 1, got {self.chunk_size_threshold}")
|
||||
|
||||
if self.fps <= 0:
|
||||
raise ValueError(f"fps must be positive, got {self.fps}")
|
||||
|
||||
if self.actions_per_chunk <= 0:
|
||||
raise ValueError(f"actions_per_chunk must be positive, got {self.actions_per_chunk}")
|
||||
|
||||
self.aggregate_fn = get_aggregate_function(self.aggregate_fn_name)
|
||||
|
||||
@classmethod
|
||||
def from_dict(cls, config_dict: dict) -> "RobotClientConfig":
|
||||
"""Create a RobotClientConfig from a dictionary."""
|
||||
return cls(**config_dict)
|
||||
|
||||
def to_dict(self) -> dict:
|
||||
"""Convert the configuration to a dictionary."""
|
||||
return {
|
||||
"server_address": self.server_address,
|
||||
"policy_type": self.policy_type,
|
||||
"pretrained_name_or_path": self.pretrained_name_or_path,
|
||||
"policy_device": self.policy_device,
|
||||
"client_device": self.client_device,
|
||||
"chunk_size_threshold": self.chunk_size_threshold,
|
||||
"fps": self.fps,
|
||||
"actions_per_chunk": self.actions_per_chunk,
|
||||
"task": self.task,
|
||||
"debug_visualize_queue_size": self.debug_visualize_queue_size,
|
||||
"aggregate_fn_name": self.aggregate_fn_name,
|
||||
}
|
||||
@@ -1,29 +0,0 @@
|
||||
# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
"""Client side: The environment evolves with a time resolution equal to 1/fps"""
|
||||
|
||||
DEFAULT_FPS = 30
|
||||
|
||||
"""Server side: Running inference on (at most) 1/fps"""
|
||||
DEFAULT_INFERENCE_LATENCY = 1 / DEFAULT_FPS
|
||||
|
||||
"""Server side: Timeout for observation queue in seconds"""
|
||||
DEFAULT_OBS_QUEUE_TIMEOUT = 2
|
||||
|
||||
# All action chunking policies
|
||||
SUPPORTED_POLICIES = ["act", "smolvla", "diffusion", "tdmpc", "vqbet", "pi0", "pi05", "groot"]
|
||||
|
||||
# TODO: Add all other robots
|
||||
SUPPORTED_ROBOTS = ["so100_follower", "so101_follower", "bi_so_follower", "omx_follower"]
|
||||
@@ -1,298 +0,0 @@
|
||||
# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
import logging
|
||||
import logging.handlers
|
||||
import os
|
||||
import time
|
||||
from dataclasses import dataclass, field
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
|
||||
import torch
|
||||
|
||||
from lerobot.configs import PolicyFeature
|
||||
|
||||
# NOTE: Configs need to be loaded for the client to be able to instantiate the policy config
|
||||
from lerobot.policies import ( # noqa: F401
|
||||
ACTConfig,
|
||||
DiffusionConfig,
|
||||
PI0Config,
|
||||
PI05Config,
|
||||
SmolVLAConfig,
|
||||
VQBeTConfig,
|
||||
)
|
||||
from lerobot.robots.robot import Robot
|
||||
from lerobot.utils.constants import OBS_IMAGES, OBS_STATE, OBS_STR
|
||||
from lerobot.utils.feature_utils import build_dataset_frame, hw_to_dataset_features
|
||||
from lerobot.utils.utils import init_logging
|
||||
|
||||
Action = torch.Tensor
|
||||
|
||||
# observation as received from the robot (can be numpy arrays, floats, etc.)
|
||||
RawObservation = dict[str, Any]
|
||||
|
||||
# observation as those recorded in LeRobot dataset (keys are different)
|
||||
LeRobotObservation = dict[str, torch.Tensor]
|
||||
|
||||
# observation, ready for policy inference (image keys resized)
|
||||
Observation = dict[str, torch.Tensor]
|
||||
|
||||
|
||||
def visualize_action_queue_size(action_queue_size: list[int]) -> None:
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
_, ax = plt.subplots()
|
||||
ax.set_title("Action Queue Size Over Time")
|
||||
ax.set_xlabel("Environment steps")
|
||||
ax.set_ylabel("Action Queue Size")
|
||||
ax.set_ylim(0, max(action_queue_size) * 1.1)
|
||||
ax.grid(True, alpha=0.3)
|
||||
ax.plot(range(len(action_queue_size)), action_queue_size)
|
||||
plt.show()
|
||||
|
||||
|
||||
def map_robot_keys_to_lerobot_features(robot: Robot) -> dict[str, dict]:
|
||||
return hw_to_dataset_features(robot.observation_features, OBS_STR, use_video=False)
|
||||
|
||||
|
||||
def is_image_key(k: str) -> bool:
|
||||
return k.startswith(OBS_IMAGES)
|
||||
|
||||
|
||||
def resize_robot_observation_image(image: torch.tensor, resize_dims: tuple[int, int, int]) -> torch.tensor:
|
||||
assert image.ndim == 3, f"Image must be (C, H, W)! Received {image.shape}"
|
||||
# (H, W, C) -> (C, H, W) for resizing from robot obsevation resolution to policy image resolution
|
||||
image = image.permute(2, 0, 1)
|
||||
dims = (resize_dims[1], resize_dims[2])
|
||||
# Add batch dimension for interpolate: (C, H, W) -> (1, C, H, W)
|
||||
image_batched = image.unsqueeze(0)
|
||||
# Interpolate and remove batch dimension: (1, C, H, W) -> (C, H, W)
|
||||
resized = torch.nn.functional.interpolate(image_batched, size=dims, mode="bilinear", align_corners=False)
|
||||
|
||||
return resized.squeeze(0)
|
||||
|
||||
|
||||
# TODO(Steven): Consider implementing a pipeline step for this
|
||||
def raw_observation_to_observation(
|
||||
raw_observation: RawObservation,
|
||||
lerobot_features: dict[str, dict],
|
||||
policy_image_features: dict[str, PolicyFeature],
|
||||
) -> Observation:
|
||||
observation = {}
|
||||
|
||||
observation = prepare_raw_observation(raw_observation, lerobot_features, policy_image_features)
|
||||
for k, v in observation.items():
|
||||
if isinstance(v, torch.Tensor): # VLAs present natural-language instructions in observations
|
||||
if "image" in k:
|
||||
# Policy expects images in shape (B, C, H, W)
|
||||
observation[k] = prepare_image(v).unsqueeze(0)
|
||||
else:
|
||||
observation[k] = v
|
||||
|
||||
return observation
|
||||
|
||||
|
||||
def prepare_image(image: torch.Tensor) -> torch.Tensor:
|
||||
"""Minimal preprocessing to turn RGB uint8 images to float32 in [0, 1], and create a memory-contiguous tensor"""
|
||||
if image.dtype == torch.uint8:
|
||||
image = image.type(torch.float32) / 255
|
||||
image = image.contiguous()
|
||||
|
||||
return image
|
||||
|
||||
|
||||
def extract_state_from_raw_observation(
|
||||
lerobot_obs: RawObservation,
|
||||
) -> torch.Tensor:
|
||||
"""Extract the state from a raw observation."""
|
||||
state = torch.tensor(lerobot_obs[OBS_STATE])
|
||||
|
||||
if state.ndim == 1:
|
||||
state = state.unsqueeze(0)
|
||||
|
||||
return state
|
||||
|
||||
|
||||
def extract_images_from_raw_observation(
|
||||
lerobot_obs: RawObservation,
|
||||
camera_key: str,
|
||||
) -> dict[str, torch.Tensor]:
|
||||
"""Extract the images from a raw observation."""
|
||||
return torch.tensor(lerobot_obs[camera_key])
|
||||
|
||||
|
||||
def make_lerobot_observation(
|
||||
robot_obs: RawObservation,
|
||||
lerobot_features: dict[str, dict],
|
||||
) -> LeRobotObservation:
|
||||
"""Make a lerobot observation from a raw observation."""
|
||||
return build_dataset_frame(lerobot_features, robot_obs, prefix=OBS_STR)
|
||||
|
||||
|
||||
def prepare_raw_observation(
|
||||
robot_obs: RawObservation,
|
||||
lerobot_features: dict[str, dict],
|
||||
policy_image_features: dict[str, PolicyFeature],
|
||||
) -> Observation:
|
||||
"""Matches keys from the raw robot_obs dict to the keys expected by a given policy (passed as
|
||||
policy_image_features)."""
|
||||
# 1. {motor.pos1:value1, motor.pos2:value2, ..., laptop:np.ndarray} ->
|
||||
# -> {observation.state:[value1,value2,...], observation.images.laptop:np.ndarray}
|
||||
lerobot_obs = make_lerobot_observation(robot_obs, lerobot_features)
|
||||
|
||||
# 2. Greps all observation.images.<> keys
|
||||
image_keys = list(filter(is_image_key, lerobot_obs))
|
||||
# state's shape is expected as (B, state_dim)
|
||||
state_dict = {OBS_STATE: extract_state_from_raw_observation(lerobot_obs)}
|
||||
image_dict = {
|
||||
image_k: extract_images_from_raw_observation(lerobot_obs, image_k) for image_k in image_keys
|
||||
}
|
||||
|
||||
# Turns the image features to (C, H, W) with H, W matching the policy image features.
|
||||
# This reduces the resolution of the images
|
||||
image_dict = {
|
||||
key: resize_robot_observation_image(torch.tensor(lerobot_obs[key]), policy_image_features[key].shape)
|
||||
for key in image_keys
|
||||
}
|
||||
|
||||
if "task" in robot_obs:
|
||||
state_dict["task"] = robot_obs["task"]
|
||||
|
||||
return {**state_dict, **image_dict}
|
||||
|
||||
|
||||
def get_logger(name: str, log_to_file: bool = True) -> logging.Logger:
|
||||
"""
|
||||
Get a logger using the standardized logging setup from utils.py.
|
||||
|
||||
Args:
|
||||
name: Logger name (e.g., 'policy_server', 'robot_client')
|
||||
log_to_file: Whether to also log to a file
|
||||
|
||||
Returns:
|
||||
Configured logger instance
|
||||
"""
|
||||
# Create logs directory if logging to file
|
||||
if log_to_file:
|
||||
os.makedirs("logs", exist_ok=True)
|
||||
log_file = Path(f"logs/{name}_{int(time.time())}.log")
|
||||
else:
|
||||
log_file = None
|
||||
|
||||
# Initialize the standardized logging
|
||||
init_logging(log_file=log_file, display_pid=False)
|
||||
|
||||
# Return a named logger
|
||||
return logging.getLogger(name)
|
||||
|
||||
|
||||
@dataclass
|
||||
class TimedData:
|
||||
"""A data object with timestamp and timestep information.
|
||||
|
||||
Args:
|
||||
timestamp: Unix timestamp relative to data's creation.
|
||||
data: The actual data to wrap a timestamp around.
|
||||
timestep: The timestep of the data.
|
||||
"""
|
||||
|
||||
timestamp: float
|
||||
timestep: int
|
||||
|
||||
def get_timestamp(self):
|
||||
return self.timestamp
|
||||
|
||||
def get_timestep(self):
|
||||
return self.timestep
|
||||
|
||||
|
||||
@dataclass
|
||||
class TimedAction(TimedData):
|
||||
action: Action
|
||||
|
||||
def get_action(self):
|
||||
return self.action
|
||||
|
||||
|
||||
@dataclass
|
||||
class TimedObservation(TimedData):
|
||||
observation: RawObservation
|
||||
must_go: bool = False
|
||||
|
||||
def get_observation(self):
|
||||
return self.observation
|
||||
|
||||
|
||||
@dataclass
|
||||
class FPSTracker:
|
||||
"""Utility class to track FPS metrics over time."""
|
||||
|
||||
target_fps: float
|
||||
first_timestamp: float = None
|
||||
total_obs_count: int = 0
|
||||
|
||||
def calculate_fps_metrics(self, current_timestamp: float) -> dict[str, float]:
|
||||
"""Calculate average FPS vs target"""
|
||||
self.total_obs_count += 1
|
||||
|
||||
# Initialize first observation time
|
||||
if self.first_timestamp is None:
|
||||
self.first_timestamp = current_timestamp
|
||||
|
||||
# Calculate overall average FPS (since start)
|
||||
total_duration = current_timestamp - self.first_timestamp
|
||||
avg_fps = (self.total_obs_count - 1) / total_duration if total_duration > 1e-6 else 0.0
|
||||
|
||||
return {"avg_fps": avg_fps, "target_fps": self.target_fps}
|
||||
|
||||
def reset(self):
|
||||
"""Reset the FPS tracker state"""
|
||||
self.first_timestamp = None
|
||||
self.total_obs_count = 0
|
||||
|
||||
|
||||
@dataclass
|
||||
class RemotePolicyConfig:
|
||||
policy_type: str
|
||||
pretrained_name_or_path: str
|
||||
lerobot_features: dict[str, PolicyFeature]
|
||||
actions_per_chunk: int
|
||||
device: str = "cpu"
|
||||
rename_map: dict[str, str] = field(default_factory=dict)
|
||||
|
||||
|
||||
def _compare_observation_states(obs1_state: torch.Tensor, obs2_state: torch.Tensor, atol: float) -> bool:
|
||||
"""Check if two observation states are similar, under a tolerance threshold"""
|
||||
return bool(torch.linalg.norm(obs1_state - obs2_state) < atol)
|
||||
|
||||
|
||||
def observations_similar(
|
||||
obs1: TimedObservation, obs2: TimedObservation, lerobot_features: dict[str, dict], atol: float = 1
|
||||
) -> bool:
|
||||
"""Check if two observations are similar, under a tolerance threshold. Measures distance between
|
||||
observations as the difference in joint-space between the two observations.
|
||||
|
||||
NOTE(fracapuano): This is a very simple check, and it is enough for the current use case.
|
||||
An immediate next step is to use (fast) perceptual difference metrics comparing some camera views,
|
||||
to surpass this joint-space similarity check.
|
||||
"""
|
||||
obs1_state = extract_state_from_raw_observation(
|
||||
make_lerobot_observation(obs1.get_observation(), lerobot_features)
|
||||
)
|
||||
obs2_state = extract_state_from_raw_observation(
|
||||
make_lerobot_observation(obs2.get_observation(), lerobot_features)
|
||||
)
|
||||
|
||||
return _compare_observation_states(obs1_state, obs2_state, atol=atol)
|
||||
@@ -1,439 +0,0 @@
|
||||
# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
"""
|
||||
Example:
|
||||
```shell
|
||||
python -m lerobot.async_inference.policy_server \
|
||||
--host=127.0.0.1 \
|
||||
--port=8080 \
|
||||
--fps=30 \
|
||||
--inference_latency=0.033 \
|
||||
--obs_queue_timeout=1
|
||||
```
|
||||
"""
|
||||
|
||||
import logging
|
||||
import pickle # nosec
|
||||
import threading
|
||||
import time
|
||||
from concurrent import futures
|
||||
from dataclasses import asdict
|
||||
from pprint import pformat
|
||||
from queue import Empty, Queue
|
||||
from typing import Any
|
||||
|
||||
import draccus
|
||||
import grpc
|
||||
import torch
|
||||
|
||||
from lerobot.policies import get_policy_class, make_pre_post_processors
|
||||
from lerobot.processor import PolicyProcessorPipeline
|
||||
from lerobot.transport import (
|
||||
services_pb2, # type: ignore
|
||||
services_pb2_grpc, # type: ignore
|
||||
)
|
||||
from lerobot.transport.utils import receive_bytes_in_chunks
|
||||
from lerobot.types import PolicyAction
|
||||
|
||||
from .configs import PolicyServerConfig
|
||||
from .constants import SUPPORTED_POLICIES
|
||||
from .helpers import (
|
||||
FPSTracker,
|
||||
Observation,
|
||||
RemotePolicyConfig,
|
||||
TimedAction,
|
||||
TimedObservation,
|
||||
get_logger,
|
||||
observations_similar,
|
||||
raw_observation_to_observation,
|
||||
)
|
||||
|
||||
|
||||
class PolicyServer(services_pb2_grpc.AsyncInferenceServicer):
|
||||
prefix = "policy_server"
|
||||
logger = get_logger(prefix)
|
||||
|
||||
def __init__(self, config: PolicyServerConfig):
|
||||
self.config = config
|
||||
self.shutdown_event = threading.Event()
|
||||
|
||||
# FPS measurement
|
||||
self.fps_tracker = FPSTracker(target_fps=config.fps)
|
||||
|
||||
self.observation_queue = Queue(maxsize=1)
|
||||
|
||||
self._predicted_timesteps_lock = threading.Lock()
|
||||
self._predicted_timesteps = set()
|
||||
|
||||
self.last_processed_obs = None
|
||||
|
||||
# Attributes will be set by SendPolicyInstructions
|
||||
self.device = None
|
||||
self.policy_type = None
|
||||
self.lerobot_features = None
|
||||
self.actions_per_chunk = None
|
||||
self.policy = None
|
||||
self.preprocessor: PolicyProcessorPipeline[dict[str, Any], dict[str, Any]] | None = None
|
||||
self.postprocessor: PolicyProcessorPipeline[PolicyAction, PolicyAction] | None = None
|
||||
|
||||
@property
|
||||
def running(self):
|
||||
return not self.shutdown_event.is_set()
|
||||
|
||||
@property
|
||||
def policy_image_features(self):
|
||||
return self.policy.config.image_features
|
||||
|
||||
def _reset_server(self) -> None:
|
||||
"""Flushes server state when new client connects."""
|
||||
# only running inference on the latest observation received by the server
|
||||
self.shutdown_event.set()
|
||||
self.observation_queue = Queue(maxsize=1)
|
||||
|
||||
with self._predicted_timesteps_lock:
|
||||
self._predicted_timesteps = set()
|
||||
|
||||
def Ready(self, request, context): # noqa: N802
|
||||
client_id = context.peer()
|
||||
self.logger.info(f"Client {client_id} connected and ready")
|
||||
self._reset_server()
|
||||
self.shutdown_event.clear()
|
||||
|
||||
return services_pb2.Empty()
|
||||
|
||||
def SendPolicyInstructions(self, request, context): # noqa: N802
|
||||
"""Receive policy instructions from the robot client"""
|
||||
|
||||
if not self.running:
|
||||
self.logger.warning("Server is not running. Ignoring policy instructions.")
|
||||
return services_pb2.Empty()
|
||||
|
||||
client_id = context.peer()
|
||||
|
||||
policy_specs = pickle.loads(request.data) # nosec
|
||||
|
||||
if not isinstance(policy_specs, RemotePolicyConfig):
|
||||
raise TypeError(f"Policy specs must be a RemotePolicyConfig. Got {type(policy_specs)}")
|
||||
|
||||
if policy_specs.policy_type not in SUPPORTED_POLICIES:
|
||||
raise ValueError(
|
||||
f"Policy type {policy_specs.policy_type} not supported. "
|
||||
f"Supported policies: {SUPPORTED_POLICIES}"
|
||||
)
|
||||
|
||||
self.logger.info(
|
||||
f"Receiving policy instructions from {client_id} | "
|
||||
f"Policy type: {policy_specs.policy_type} | "
|
||||
f"Pretrained name or path: {policy_specs.pretrained_name_or_path} | "
|
||||
f"Actions per chunk: {policy_specs.actions_per_chunk} | "
|
||||
f"Device: {policy_specs.device}"
|
||||
)
|
||||
|
||||
self.device = policy_specs.device
|
||||
self.policy_type = policy_specs.policy_type # act, pi0, etc.
|
||||
self.lerobot_features = policy_specs.lerobot_features
|
||||
self.actions_per_chunk = policy_specs.actions_per_chunk
|
||||
|
||||
policy_class = get_policy_class(self.policy_type)
|
||||
|
||||
start = time.perf_counter()
|
||||
self.policy = policy_class.from_pretrained(policy_specs.pretrained_name_or_path)
|
||||
self.policy.to(self.device)
|
||||
|
||||
# Load preprocessor and postprocessor, overriding device to match requested device
|
||||
device_override = {"device": self.device}
|
||||
self.preprocessor, self.postprocessor = make_pre_post_processors(
|
||||
self.policy.config,
|
||||
pretrained_path=policy_specs.pretrained_name_or_path,
|
||||
preprocessor_overrides={
|
||||
"device_processor": device_override,
|
||||
"rename_observations_processor": {"rename_map": policy_specs.rename_map},
|
||||
},
|
||||
postprocessor_overrides={"device_processor": device_override},
|
||||
)
|
||||
|
||||
end = time.perf_counter()
|
||||
|
||||
self.logger.info(f"Time taken to put policy on {self.device}: {end - start:.4f} seconds")
|
||||
|
||||
return services_pb2.Empty()
|
||||
|
||||
def SendObservations(self, request_iterator, context): # noqa: N802
|
||||
"""Receive observations from the robot client"""
|
||||
client_id = context.peer()
|
||||
self.logger.debug(f"Receiving observations from {client_id}")
|
||||
|
||||
receive_time = time.time() # comparing timestamps so need time.time()
|
||||
start_deserialize = time.perf_counter()
|
||||
received_bytes = receive_bytes_in_chunks(
|
||||
request_iterator, None, self.shutdown_event, self.logger
|
||||
) # blocking call while looping over request_iterator
|
||||
timed_observation = pickle.loads(received_bytes) # nosec
|
||||
deserialize_time = time.perf_counter() - start_deserialize
|
||||
|
||||
self.logger.debug(f"Received observation #{timed_observation.get_timestep()}")
|
||||
|
||||
obs_timestep = timed_observation.get_timestep()
|
||||
obs_timestamp = timed_observation.get_timestamp()
|
||||
|
||||
# Calculate FPS metrics
|
||||
fps_metrics = self.fps_tracker.calculate_fps_metrics(obs_timestamp)
|
||||
|
||||
self.logger.debug(
|
||||
f"Received observation #{obs_timestep} | "
|
||||
f"Avg FPS: {fps_metrics['avg_fps']:.2f} | " # fps at which observations are received from client
|
||||
f"Target: {fps_metrics['target_fps']:.2f} | "
|
||||
f"One-way latency: {(receive_time - obs_timestamp) * 1000:.2f}ms"
|
||||
)
|
||||
|
||||
self.logger.debug(
|
||||
f"Server timestamp: {receive_time:.6f} | "
|
||||
f"Client timestamp: {obs_timestamp:.6f} | "
|
||||
f"Deserialization time: {deserialize_time:.6f}s"
|
||||
)
|
||||
|
||||
if not self._enqueue_observation(
|
||||
timed_observation # wrapping a RawObservation
|
||||
):
|
||||
self.logger.debug(f"Observation #{obs_timestep} has been filtered out")
|
||||
|
||||
return services_pb2.Empty()
|
||||
|
||||
def GetActions(self, request, context): # noqa: N802
|
||||
"""Returns actions to the robot client. Actions are sent as a single
|
||||
chunk, containing multiple actions."""
|
||||
client_id = context.peer()
|
||||
self.logger.debug(f"Client {client_id} connected for action streaming")
|
||||
|
||||
# Generate action based on the most recent observation and its timestep
|
||||
try:
|
||||
getactions_starts = time.perf_counter()
|
||||
obs = self.observation_queue.get(timeout=self.config.obs_queue_timeout)
|
||||
self.logger.info(
|
||||
f"Running inference for observation #{obs.get_timestep()} (must_go: {obs.must_go})"
|
||||
)
|
||||
|
||||
with self._predicted_timesteps_lock:
|
||||
self._predicted_timesteps.add(obs.get_timestep())
|
||||
|
||||
start_time = time.perf_counter()
|
||||
action_chunk = self._predict_action_chunk(obs)
|
||||
inference_time = time.perf_counter() - start_time
|
||||
|
||||
start_time = time.perf_counter()
|
||||
actions_bytes = pickle.dumps(action_chunk) # nosec
|
||||
serialize_time = time.perf_counter() - start_time
|
||||
|
||||
# Create and return the action chunk
|
||||
actions = services_pb2.Actions(data=actions_bytes)
|
||||
|
||||
self.logger.info(
|
||||
f"Action chunk #{obs.get_timestep()} generated | "
|
||||
f"Total time: {(inference_time + serialize_time) * 1000:.2f}ms"
|
||||
)
|
||||
|
||||
self.logger.debug(
|
||||
f"Action chunk #{obs.get_timestep()} generated | "
|
||||
f"Inference time: {inference_time:.2f}s |"
|
||||
f"Serialize time: {serialize_time:.2f}s |"
|
||||
f"Total time: {inference_time + serialize_time:.2f}s"
|
||||
)
|
||||
|
||||
time.sleep(
|
||||
max(0, self.config.inference_latency - max(0, time.perf_counter() - getactions_starts))
|
||||
) # sleep controls inference latency
|
||||
|
||||
return actions
|
||||
|
||||
except Empty: # no observation added to queue in obs_queue_timeout
|
||||
return services_pb2.Empty()
|
||||
|
||||
except Exception as e:
|
||||
self.logger.error(f"Error in StreamActions: {e}")
|
||||
|
||||
return services_pb2.Empty()
|
||||
|
||||
def _obs_sanity_checks(self, obs: TimedObservation, previous_obs: TimedObservation) -> bool:
|
||||
"""Check if the observation is valid to be processed by the policy"""
|
||||
with self._predicted_timesteps_lock:
|
||||
predicted_timesteps = self._predicted_timesteps
|
||||
|
||||
if obs.get_timestep() in predicted_timesteps:
|
||||
self.logger.debug(f"Skipping observation #{obs.get_timestep()} - Timestep predicted already!")
|
||||
return False
|
||||
|
||||
elif observations_similar(obs, previous_obs, lerobot_features=self.lerobot_features):
|
||||
self.logger.debug(
|
||||
f"Skipping observation #{obs.get_timestep()} - Observation too similar to last obs predicted!"
|
||||
)
|
||||
return False
|
||||
|
||||
else:
|
||||
return True
|
||||
|
||||
def _enqueue_observation(self, obs: TimedObservation) -> bool:
|
||||
"""Enqueue an observation if it must go through processing, otherwise skip it.
|
||||
Observations not in queue are never run through the policy network"""
|
||||
|
||||
if (
|
||||
obs.must_go
|
||||
or self.last_processed_obs is None
|
||||
or self._obs_sanity_checks(obs, self.last_processed_obs)
|
||||
):
|
||||
last_obs = self.last_processed_obs.get_timestep() if self.last_processed_obs else "None"
|
||||
self.logger.debug(
|
||||
f"Enqueuing observation. Must go: {obs.must_go} | Last processed obs: {last_obs}"
|
||||
)
|
||||
|
||||
# If queue is full, get the old observation to make room
|
||||
if self.observation_queue.full():
|
||||
# pops from queue
|
||||
_ = self.observation_queue.get_nowait()
|
||||
self.logger.debug("Observation queue was full, removed oldest observation")
|
||||
|
||||
# Now put the new observation (never blocks as queue is non-full here)
|
||||
self.observation_queue.put(obs)
|
||||
return True
|
||||
|
||||
return False
|
||||
|
||||
def _time_action_chunk(self, t_0: float, action_chunk: list[torch.Tensor], i_0: int) -> list[TimedAction]:
|
||||
"""Turn a chunk of actions into a list of TimedAction instances,
|
||||
with the first action corresponding to t_0 and the rest corresponding to
|
||||
t_0 + i*environment_dt for i in range(len(action_chunk))
|
||||
"""
|
||||
return [
|
||||
TimedAction(timestamp=t_0 + i * self.config.environment_dt, timestep=i_0 + i, action=action)
|
||||
for i, action in enumerate(action_chunk)
|
||||
]
|
||||
|
||||
def _get_action_chunk(self, observation: dict[str, torch.Tensor]) -> torch.Tensor:
|
||||
"""Get an action chunk from the policy. The chunk contains only"""
|
||||
chunk = self.policy.predict_action_chunk(observation)
|
||||
if chunk.ndim != 3:
|
||||
chunk = chunk.unsqueeze(0) # adding batch dimension, now shape is (B, chunk_size, action_dim)
|
||||
|
||||
return chunk[:, : self.actions_per_chunk, :]
|
||||
|
||||
def _predict_action_chunk(self, observation_t: TimedObservation) -> list[TimedAction]:
|
||||
"""Predict an action chunk based on an observation.
|
||||
|
||||
Pipeline:
|
||||
1. Convert raw observation to LeRobot format
|
||||
2. Apply preprocessor (tokenization, normalization, batching, device placement)
|
||||
3. Run policy inference to get action chunk
|
||||
4. Apply postprocessor (unnormalization, device movement)
|
||||
5. Convert to TimedAction list
|
||||
"""
|
||||
"""1. Prepare observation"""
|
||||
start_prepare = time.perf_counter()
|
||||
observation: Observation = raw_observation_to_observation(
|
||||
observation_t.get_observation(),
|
||||
self.lerobot_features,
|
||||
self.policy_image_features,
|
||||
)
|
||||
prepare_time = time.perf_counter() - start_prepare
|
||||
|
||||
"""2. Apply preprocessor"""
|
||||
start_preprocess = time.perf_counter()
|
||||
observation = self.preprocessor(observation)
|
||||
self.last_processed_obs: TimedObservation = observation_t
|
||||
preprocessing_time = time.perf_counter() - start_preprocess
|
||||
|
||||
"""3. Get action chunk"""
|
||||
start_inference = time.perf_counter()
|
||||
action_tensor = self._get_action_chunk(observation)
|
||||
inference_time = time.perf_counter() - start_inference
|
||||
self.logger.info(
|
||||
f"Preprocessing and inference took {inference_time:.4f}s, action shape: {action_tensor.shape}"
|
||||
)
|
||||
|
||||
"""4. Apply postprocessor"""
|
||||
# Apply postprocessor (handles unnormalization and device movement)
|
||||
# Postprocessor expects (B, action_dim) per action, but we have (B, chunk_size, action_dim)
|
||||
# So we process each action in the chunk individually
|
||||
start_postprocess = time.perf_counter()
|
||||
_, chunk_size, _ = action_tensor.shape
|
||||
|
||||
# Process each action in the chunk
|
||||
processed_actions = []
|
||||
for i in range(chunk_size):
|
||||
# Extract action at timestep i: (B, action_dim)
|
||||
single_action = action_tensor[:, i, :]
|
||||
processed_action = self.postprocessor(single_action)
|
||||
processed_actions.append(processed_action)
|
||||
|
||||
# Stack back to (B, chunk_size, action_dim), then remove batch dim
|
||||
action_tensor = torch.stack(processed_actions, dim=1).squeeze(0)
|
||||
self.logger.debug(f"Postprocessed action shape: {action_tensor.shape}")
|
||||
|
||||
action_tensor = action_tensor.detach().cpu()
|
||||
|
||||
"""5. Convert to TimedAction list"""
|
||||
action_chunk = self._time_action_chunk(
|
||||
observation_t.get_timestamp(), list(action_tensor), observation_t.get_timestep()
|
||||
)
|
||||
postprocess_stops = time.perf_counter()
|
||||
postprocessing_time = postprocess_stops - start_postprocess
|
||||
|
||||
self.logger.info(
|
||||
f"Observation {observation_t.get_timestep()} | "
|
||||
f"Total time: {1000 * (postprocess_stops - start_prepare):.2f}ms"
|
||||
)
|
||||
|
||||
self.logger.debug(
|
||||
f"Observation {observation_t.get_timestep()} | "
|
||||
f"Prepare time: {1000 * prepare_time:.2f}ms | "
|
||||
f"Preprocessing time: {1000 * preprocessing_time:.2f}ms | "
|
||||
f"Inference time: {1000 * inference_time:.2f}ms | "
|
||||
f"Postprocessing time: {1000 * postprocessing_time:.2f}ms | "
|
||||
f"Total time: {1000 * (postprocess_stops - start_prepare):.2f}ms"
|
||||
)
|
||||
|
||||
return action_chunk
|
||||
|
||||
def stop(self):
|
||||
"""Stop the server"""
|
||||
self._reset_server()
|
||||
self.logger.info("Server stopping...")
|
||||
|
||||
|
||||
@draccus.wrap()
|
||||
def serve(cfg: PolicyServerConfig):
|
||||
"""Start the PolicyServer with the given configuration.
|
||||
|
||||
Args:
|
||||
config: PolicyServerConfig instance. If None, uses default configuration.
|
||||
"""
|
||||
logging.info(pformat(asdict(cfg)))
|
||||
|
||||
# Create the server instance first
|
||||
policy_server = PolicyServer(cfg)
|
||||
|
||||
# Setup and start gRPC server
|
||||
server = grpc.server(futures.ThreadPoolExecutor(max_workers=4))
|
||||
services_pb2_grpc.add_AsyncInferenceServicer_to_server(policy_server, server)
|
||||
server.add_insecure_port(f"{cfg.host}:{cfg.port}")
|
||||
|
||||
policy_server.logger.info(f"PolicyServer started on {cfg.host}:{cfg.port}")
|
||||
server.start()
|
||||
|
||||
server.wait_for_termination()
|
||||
|
||||
policy_server.logger.info("Server terminated")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
serve()
|
||||
@@ -1,517 +0,0 @@
|
||||
# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
"""
|
||||
Example command:
|
||||
```shell
|
||||
python src/lerobot/async_inference/robot_client.py \
|
||||
--robot.type=so100_follower \
|
||||
--robot.port=/dev/tty.usbmodem58760431541 \
|
||||
--robot.cameras="{ front: {type: opencv, index_or_path: 0, width: 1920, height: 1080, fps: 30}}" \
|
||||
--robot.id=black \
|
||||
--task="dummy" \
|
||||
--server_address=127.0.0.1:8080 \
|
||||
--policy_type=act \
|
||||
--pretrained_name_or_path=user/model \
|
||||
--policy_device=mps \
|
||||
--client_device=cpu \
|
||||
--actions_per_chunk=50 \
|
||||
--chunk_size_threshold=0.5 \
|
||||
--aggregate_fn_name=weighted_average \
|
||||
--debug_visualize_queue_size=True
|
||||
```
|
||||
"""
|
||||
|
||||
import logging
|
||||
import pickle # nosec
|
||||
import threading
|
||||
import time
|
||||
from collections.abc import Callable
|
||||
from dataclasses import asdict
|
||||
from pprint import pformat
|
||||
from queue import Queue
|
||||
from typing import Any
|
||||
|
||||
import draccus
|
||||
import grpc
|
||||
import torch
|
||||
|
||||
from lerobot.cameras.opencv import OpenCVCameraConfig # noqa: F401
|
||||
from lerobot.cameras.realsense import RealSenseCameraConfig # noqa: F401
|
||||
from lerobot.robots import ( # noqa: F401
|
||||
Robot,
|
||||
RobotConfig,
|
||||
bi_so_follower,
|
||||
koch_follower,
|
||||
make_robot_from_config,
|
||||
omx_follower,
|
||||
so_follower,
|
||||
)
|
||||
from lerobot.transport import (
|
||||
services_pb2, # type: ignore
|
||||
services_pb2_grpc, # type: ignore
|
||||
)
|
||||
from lerobot.transport.utils import grpc_channel_options, send_bytes_in_chunks
|
||||
from lerobot.utils.import_utils import register_third_party_plugins
|
||||
|
||||
from .configs import RobotClientConfig
|
||||
from .helpers import (
|
||||
Action,
|
||||
FPSTracker,
|
||||
Observation,
|
||||
RawObservation,
|
||||
RemotePolicyConfig,
|
||||
TimedAction,
|
||||
TimedObservation,
|
||||
get_logger,
|
||||
map_robot_keys_to_lerobot_features,
|
||||
visualize_action_queue_size,
|
||||
)
|
||||
|
||||
|
||||
class RobotClient:
|
||||
prefix = "robot_client"
|
||||
logger = get_logger(prefix)
|
||||
|
||||
def __init__(self, config: RobotClientConfig):
|
||||
"""Initialize RobotClient with unified configuration.
|
||||
|
||||
Args:
|
||||
config: RobotClientConfig containing all configuration parameters
|
||||
"""
|
||||
# Store configuration
|
||||
self.config = config
|
||||
self.robot = make_robot_from_config(config.robot)
|
||||
self.robot.connect()
|
||||
|
||||
lerobot_features = map_robot_keys_to_lerobot_features(self.robot)
|
||||
|
||||
# Use environment variable if server_address is not provided in config
|
||||
self.server_address = config.server_address
|
||||
|
||||
self.policy_config = RemotePolicyConfig(
|
||||
config.policy_type,
|
||||
config.pretrained_name_or_path,
|
||||
lerobot_features,
|
||||
config.actions_per_chunk,
|
||||
config.policy_device,
|
||||
)
|
||||
self.channel = grpc.insecure_channel(
|
||||
self.server_address, grpc_channel_options(initial_backoff=f"{config.environment_dt:.4f}s")
|
||||
)
|
||||
self.stub = services_pb2_grpc.AsyncInferenceStub(self.channel)
|
||||
self.logger.info(f"Initializing client to connect to server at {self.server_address}")
|
||||
|
||||
self.shutdown_event = threading.Event()
|
||||
|
||||
# Initialize client side variables
|
||||
self.latest_action_lock = threading.Lock()
|
||||
self.latest_action = -1
|
||||
self.action_chunk_size = -1
|
||||
|
||||
self._chunk_size_threshold = config.chunk_size_threshold
|
||||
|
||||
self.action_queue = Queue()
|
||||
self.action_queue_lock = threading.Lock() # Protect queue operations
|
||||
self.action_queue_size = []
|
||||
self.start_barrier = threading.Barrier(2) # 2 threads: action receiver, control loop
|
||||
|
||||
# FPS measurement
|
||||
self.fps_tracker = FPSTracker(target_fps=self.config.fps)
|
||||
|
||||
self.logger.info("Robot connected and ready")
|
||||
|
||||
# Use an event for thread-safe coordination
|
||||
self.must_go = threading.Event()
|
||||
self.must_go.set() # Initially set - observations qualify for direct processing
|
||||
|
||||
@property
|
||||
def running(self):
|
||||
return not self.shutdown_event.is_set()
|
||||
|
||||
def start(self):
|
||||
"""Start the robot client and connect to the policy server"""
|
||||
try:
|
||||
# client-server handshake
|
||||
start_time = time.perf_counter()
|
||||
self.stub.Ready(services_pb2.Empty())
|
||||
end_time = time.perf_counter()
|
||||
self.logger.debug(f"Connected to policy server in {end_time - start_time:.4f}s")
|
||||
|
||||
# send policy instructions
|
||||
policy_config_bytes = pickle.dumps(self.policy_config)
|
||||
policy_setup = services_pb2.PolicySetup(data=policy_config_bytes)
|
||||
|
||||
self.logger.info("Sending policy instructions to policy server")
|
||||
self.logger.debug(
|
||||
f"Policy type: {self.policy_config.policy_type} | "
|
||||
f"Pretrained name or path: {self.policy_config.pretrained_name_or_path} | "
|
||||
f"Device: {self.policy_config.device}"
|
||||
)
|
||||
|
||||
self.stub.SendPolicyInstructions(policy_setup)
|
||||
|
||||
self.shutdown_event.clear()
|
||||
|
||||
return True
|
||||
|
||||
except grpc.RpcError as e:
|
||||
self.logger.error(f"Failed to connect to policy server: {e}")
|
||||
return False
|
||||
|
||||
def stop(self):
|
||||
"""Stop the robot client"""
|
||||
self.shutdown_event.set()
|
||||
|
||||
self.robot.disconnect()
|
||||
self.logger.debug("Robot disconnected")
|
||||
|
||||
self.channel.close()
|
||||
self.logger.debug("Client stopped, channel closed")
|
||||
|
||||
def send_observation(
|
||||
self,
|
||||
obs: TimedObservation,
|
||||
) -> bool:
|
||||
"""Send observation to the policy server.
|
||||
Returns True if the observation was sent successfully, False otherwise."""
|
||||
if not self.running:
|
||||
raise RuntimeError("Client not running. Run RobotClient.start() before sending observations.")
|
||||
|
||||
if not isinstance(obs, TimedObservation):
|
||||
raise ValueError("Input observation needs to be a TimedObservation!")
|
||||
|
||||
start_time = time.perf_counter()
|
||||
observation_bytes = pickle.dumps(obs)
|
||||
serialize_time = time.perf_counter() - start_time
|
||||
self.logger.debug(f"Observation serialization time: {serialize_time:.6f}s")
|
||||
|
||||
try:
|
||||
observation_iterator = send_bytes_in_chunks(
|
||||
observation_bytes,
|
||||
services_pb2.Observation,
|
||||
log_prefix="[CLIENT] Observation",
|
||||
silent=True,
|
||||
)
|
||||
_ = self.stub.SendObservations(observation_iterator)
|
||||
obs_timestep = obs.get_timestep()
|
||||
self.logger.debug(f"Sent observation #{obs_timestep} | ")
|
||||
|
||||
return True
|
||||
|
||||
except grpc.RpcError as e:
|
||||
self.logger.error(f"Error sending observation #{obs.get_timestep()}: {e}")
|
||||
return False
|
||||
|
||||
def _inspect_action_queue(self):
|
||||
with self.action_queue_lock:
|
||||
queue_size = self.action_queue.qsize()
|
||||
timestamps = sorted([action.get_timestep() for action in self.action_queue.queue])
|
||||
self.logger.debug(f"Queue size: {queue_size}, Queue contents: {timestamps}")
|
||||
return queue_size, timestamps
|
||||
|
||||
def _aggregate_action_queues(
|
||||
self,
|
||||
incoming_actions: list[TimedAction],
|
||||
aggregate_fn: Callable[[torch.Tensor, torch.Tensor], torch.Tensor] | None = None,
|
||||
):
|
||||
"""Finds the same timestep actions in the queue and aggregates them using the aggregate_fn"""
|
||||
if aggregate_fn is None:
|
||||
# default aggregate function: take the latest action
|
||||
def aggregate_fn(x1, x2):
|
||||
return x2
|
||||
|
||||
future_action_queue = Queue()
|
||||
with self.action_queue_lock:
|
||||
internal_queue = self.action_queue.queue
|
||||
|
||||
current_action_queue = {action.get_timestep(): action.get_action() for action in internal_queue}
|
||||
|
||||
for new_action in incoming_actions:
|
||||
with self.latest_action_lock:
|
||||
latest_action = self.latest_action
|
||||
|
||||
# New action is older than the latest action in the queue, skip it
|
||||
if new_action.get_timestep() <= latest_action:
|
||||
continue
|
||||
|
||||
# If the new action's timestep is not in the current action queue, add it directly
|
||||
elif new_action.get_timestep() not in current_action_queue:
|
||||
future_action_queue.put(new_action)
|
||||
continue
|
||||
|
||||
# If the new action's timestep is in the current action queue, aggregate it
|
||||
# TODO: There is probably a way to do this with broadcasting of the two action tensors
|
||||
future_action_queue.put(
|
||||
TimedAction(
|
||||
timestamp=new_action.get_timestamp(),
|
||||
timestep=new_action.get_timestep(),
|
||||
action=aggregate_fn(
|
||||
current_action_queue[new_action.get_timestep()], new_action.get_action()
|
||||
),
|
||||
)
|
||||
)
|
||||
|
||||
with self.action_queue_lock:
|
||||
self.action_queue = future_action_queue
|
||||
|
||||
def receive_actions(self, verbose: bool = False):
|
||||
"""Receive actions from the policy server"""
|
||||
# Wait at barrier for synchronized start
|
||||
self.start_barrier.wait()
|
||||
self.logger.info("Action receiving thread starting")
|
||||
|
||||
while self.running:
|
||||
try:
|
||||
# Use StreamActions to get a stream of actions from the server
|
||||
actions_chunk = self.stub.GetActions(services_pb2.Empty())
|
||||
if len(actions_chunk.data) == 0:
|
||||
continue # received `Empty` from server, wait for next call
|
||||
|
||||
receive_time = time.time()
|
||||
|
||||
# Deserialize bytes back into list[TimedAction]
|
||||
deserialize_start = time.perf_counter()
|
||||
timed_actions = pickle.loads(actions_chunk.data) # nosec
|
||||
deserialize_time = time.perf_counter() - deserialize_start
|
||||
|
||||
# Log device type of received actions
|
||||
if len(timed_actions) > 0:
|
||||
received_device = timed_actions[0].get_action().device.type
|
||||
self.logger.debug(f"Received actions on device: {received_device}")
|
||||
|
||||
# Move actions to client_device (e.g., for downstream planners that need GPU)
|
||||
client_device = self.config.client_device
|
||||
if client_device != "cpu":
|
||||
for timed_action in timed_actions:
|
||||
if timed_action.get_action().device.type != client_device:
|
||||
timed_action.action = timed_action.get_action().to(client_device)
|
||||
self.logger.debug(f"Converted actions to device: {client_device}")
|
||||
else:
|
||||
self.logger.debug(f"Actions kept on device: {client_device}")
|
||||
|
||||
self.action_chunk_size = max(self.action_chunk_size, len(timed_actions))
|
||||
|
||||
# Calculate network latency if we have matching observations
|
||||
if len(timed_actions) > 0 and verbose:
|
||||
with self.latest_action_lock:
|
||||
latest_action = self.latest_action
|
||||
|
||||
self.logger.debug(f"Current latest action: {latest_action}")
|
||||
|
||||
# Get queue state before changes
|
||||
old_size, old_timesteps = self._inspect_action_queue()
|
||||
if not old_timesteps:
|
||||
old_timesteps = [latest_action] # queue was empty
|
||||
|
||||
# Log incoming actions
|
||||
incoming_timesteps = [a.get_timestep() for a in timed_actions]
|
||||
|
||||
first_action_timestep = timed_actions[0].get_timestep()
|
||||
server_to_client_latency = (receive_time - timed_actions[0].get_timestamp()) * 1000
|
||||
|
||||
self.logger.info(
|
||||
f"Received action chunk for step #{first_action_timestep} | "
|
||||
f"Latest action: #{latest_action} | "
|
||||
f"Incoming actions: {incoming_timesteps[0]}:{incoming_timesteps[-1]} | "
|
||||
f"Network latency (server->client): {server_to_client_latency:.2f}ms | "
|
||||
f"Deserialization time: {deserialize_time * 1000:.2f}ms"
|
||||
)
|
||||
|
||||
# Update action queue
|
||||
start_time = time.perf_counter()
|
||||
self._aggregate_action_queues(timed_actions, self.config.aggregate_fn)
|
||||
queue_update_time = time.perf_counter() - start_time
|
||||
|
||||
self.must_go.set() # after receiving actions, next empty queue triggers must-go processing!
|
||||
|
||||
if verbose:
|
||||
# Get queue state after changes
|
||||
new_size, new_timesteps = self._inspect_action_queue()
|
||||
|
||||
with self.latest_action_lock:
|
||||
latest_action = self.latest_action
|
||||
|
||||
self.logger.info(
|
||||
f"Latest action: {latest_action} | "
|
||||
f"Old action steps: {old_timesteps[0]}:{old_timesteps[-1]} | "
|
||||
f"Incoming action steps: {incoming_timesteps[0]}:{incoming_timesteps[-1]} | "
|
||||
f"Updated action steps: {new_timesteps[0]}:{new_timesteps[-1]}"
|
||||
)
|
||||
self.logger.debug(
|
||||
f"Queue update complete ({queue_update_time:.6f}s) | "
|
||||
f"Before: {old_size} items | "
|
||||
f"After: {new_size} items | "
|
||||
)
|
||||
|
||||
except grpc.RpcError as e:
|
||||
self.logger.error(f"Error receiving actions: {e}")
|
||||
|
||||
def actions_available(self):
|
||||
"""Check if there are actions available in the queue"""
|
||||
with self.action_queue_lock:
|
||||
return not self.action_queue.empty()
|
||||
|
||||
def _action_tensor_to_action_dict(self, action_tensor: torch.Tensor) -> dict[str, float]:
|
||||
action = {key: action_tensor[i].item() for i, key in enumerate(self.robot.action_features)}
|
||||
return action
|
||||
|
||||
def control_loop_action(self, verbose: bool = False) -> dict[str, Any]:
|
||||
"""Reading and performing actions in local queue"""
|
||||
|
||||
# Lock only for queue operations
|
||||
get_start = time.perf_counter()
|
||||
with self.action_queue_lock:
|
||||
self.action_queue_size.append(self.action_queue.qsize())
|
||||
# Get action from queue
|
||||
timed_action = self.action_queue.get_nowait()
|
||||
get_end = time.perf_counter() - get_start
|
||||
|
||||
_performed_action = self.robot.send_action(
|
||||
self._action_tensor_to_action_dict(timed_action.get_action())
|
||||
)
|
||||
with self.latest_action_lock:
|
||||
self.latest_action = timed_action.get_timestep()
|
||||
|
||||
if verbose:
|
||||
with self.action_queue_lock:
|
||||
current_queue_size = self.action_queue.qsize()
|
||||
|
||||
self.logger.debug(
|
||||
f"Ts={timed_action.get_timestamp()} | "
|
||||
f"Action #{timed_action.get_timestep()} performed | "
|
||||
f"Queue size: {current_queue_size}"
|
||||
)
|
||||
|
||||
self.logger.debug(
|
||||
f"Popping action from queue to perform took {get_end:.6f}s | Queue size: {current_queue_size}"
|
||||
)
|
||||
|
||||
return _performed_action
|
||||
|
||||
def _ready_to_send_observation(self):
|
||||
"""Flags when the client is ready to send an observation"""
|
||||
with self.action_queue_lock:
|
||||
return self.action_queue.qsize() / self.action_chunk_size <= self._chunk_size_threshold
|
||||
|
||||
def control_loop_observation(self, task: str, verbose: bool = False) -> RawObservation:
|
||||
try:
|
||||
# Get serialized observation bytes from the function
|
||||
start_time = time.perf_counter()
|
||||
|
||||
raw_observation: RawObservation = self.robot.get_observation()
|
||||
raw_observation["task"] = task
|
||||
|
||||
with self.latest_action_lock:
|
||||
latest_action = self.latest_action
|
||||
|
||||
observation = TimedObservation(
|
||||
timestamp=time.time(), # need time.time() to compare timestamps across client and server
|
||||
observation=raw_observation,
|
||||
timestep=max(latest_action, 0),
|
||||
)
|
||||
|
||||
obs_capture_time = time.perf_counter() - start_time
|
||||
|
||||
# If there are no actions left in the queue, the observation must go through processing!
|
||||
with self.action_queue_lock:
|
||||
observation.must_go = self.must_go.is_set() and self.action_queue.empty()
|
||||
current_queue_size = self.action_queue.qsize()
|
||||
|
||||
_ = self.send_observation(observation)
|
||||
|
||||
self.logger.debug(f"QUEUE SIZE: {current_queue_size} (Must go: {observation.must_go})")
|
||||
if observation.must_go:
|
||||
# must-go event will be set again after receiving actions
|
||||
self.must_go.clear()
|
||||
|
||||
if verbose:
|
||||
# Calculate comprehensive FPS metrics
|
||||
fps_metrics = self.fps_tracker.calculate_fps_metrics(observation.get_timestamp())
|
||||
|
||||
self.logger.info(
|
||||
f"Obs #{observation.get_timestep()} | "
|
||||
f"Avg FPS: {fps_metrics['avg_fps']:.2f} | "
|
||||
f"Target: {fps_metrics['target_fps']:.2f}"
|
||||
)
|
||||
|
||||
self.logger.debug(
|
||||
f"Ts={observation.get_timestamp():.6f} | Capturing observation took {obs_capture_time:.6f}s"
|
||||
)
|
||||
|
||||
return raw_observation
|
||||
|
||||
except Exception as e:
|
||||
self.logger.error(f"Error in observation sender: {e}")
|
||||
|
||||
def control_loop(self, task: str, verbose: bool = False) -> tuple[Observation, Action]:
|
||||
"""Combined function for executing actions and streaming observations"""
|
||||
# Wait at barrier for synchronized start
|
||||
self.start_barrier.wait()
|
||||
self.logger.info("Control loop thread starting")
|
||||
|
||||
_performed_action = None
|
||||
_captured_observation = None
|
||||
|
||||
while self.running:
|
||||
control_loop_start = time.perf_counter()
|
||||
"""Control loop: (1) Performing actions, when available"""
|
||||
if self.actions_available():
|
||||
_performed_action = self.control_loop_action(verbose)
|
||||
|
||||
"""Control loop: (2) Streaming observations to the remote policy server"""
|
||||
if self._ready_to_send_observation():
|
||||
_captured_observation = self.control_loop_observation(task, verbose)
|
||||
|
||||
self.logger.debug(f"Control loop (ms): {(time.perf_counter() - control_loop_start) * 1000:.2f}")
|
||||
# Dynamically adjust sleep time to maintain the desired control frequency
|
||||
time.sleep(max(0, self.config.environment_dt - (time.perf_counter() - control_loop_start)))
|
||||
|
||||
return _captured_observation, _performed_action
|
||||
|
||||
|
||||
@draccus.wrap()
|
||||
def async_client(cfg: RobotClientConfig):
|
||||
logging.info(pformat(asdict(cfg)))
|
||||
|
||||
# TODO: Assert if checking robot support is still needed with the plugin system
|
||||
# if cfg.robot.type not in SUPPORTED_ROBOTS:
|
||||
# raise ValueError(f"Robot {cfg.robot.type} not yet supported!")
|
||||
|
||||
client = RobotClient(cfg)
|
||||
|
||||
if client.start():
|
||||
client.logger.info("Starting action receiver thread...")
|
||||
|
||||
# Create and start action receiver thread
|
||||
action_receiver_thread = threading.Thread(target=client.receive_actions, daemon=True)
|
||||
|
||||
# Start action receiver thread
|
||||
action_receiver_thread.start()
|
||||
|
||||
try:
|
||||
# The main thread runs the control loop
|
||||
client.control_loop(task=cfg.task)
|
||||
|
||||
finally:
|
||||
client.stop()
|
||||
action_receiver_thread.join()
|
||||
if cfg.debug_visualize_queue_size:
|
||||
visualize_action_queue_size(client.action_queue_size)
|
||||
client.logger.info("Client stopped")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
register_third_party_plugins()
|
||||
async_client() # run the client
|
||||
@@ -436,18 +436,17 @@ class OpenCVCamera(Camera):
|
||||
Internal loop run by the background thread for asynchronous reading.
|
||||
|
||||
On each iteration:
|
||||
1. Reads a color frame (blocking call)
|
||||
1. Reads a color frame
|
||||
2. Stores result in latest_frame and updates timestamp (thread-safe)
|
||||
3. Sets new_frame_event to notify listeners
|
||||
|
||||
Stops on DeviceNotConnectedError, logs other errors and continues.
|
||||
"""
|
||||
stop_event = self.stop_event
|
||||
if stop_event is None:
|
||||
if self.stop_event is None:
|
||||
raise RuntimeError(f"{self}: stop_event is not initialized before starting read loop.")
|
||||
|
||||
failure_count = 0
|
||||
while not stop_event.is_set():
|
||||
while not self.stop_event.is_set():
|
||||
try:
|
||||
raw_frame = self._read_from_hardware()
|
||||
processed_frame = self._postprocess_image(raw_frame)
|
||||
@@ -485,8 +484,6 @@ class OpenCVCamera(Camera):
|
||||
|
||||
if self.thread is not None and self.thread.is_alive():
|
||||
self.thread.join(timeout=2.0)
|
||||
if self.thread.is_alive():
|
||||
logger.warning(f"{self} read thread did not terminate within timeout.")
|
||||
|
||||
self.thread = None
|
||||
self.stop_event = None
|
||||
|
||||
@@ -128,7 +128,6 @@ class RealSenseCamera(Camera):
|
||||
|
||||
self.fps = config.fps
|
||||
self.color_mode = config.color_mode
|
||||
self.use_rgb = config.use_rgb
|
||||
self.use_depth = config.use_depth
|
||||
self.warmup_s = config.warmup_s
|
||||
|
||||
@@ -196,15 +195,12 @@ class RealSenseCamera(Camera):
|
||||
# NOTE(Steven/Caroline): Enforcing at least one second of warmup as RS cameras need a bit of time before the first read. If we don't wait, the first read from the warmup will raise.
|
||||
self.warmup_s = max(self.warmup_s, 1)
|
||||
|
||||
warmup_read = self.async_read if self.use_rgb else self.async_read_depth
|
||||
start_time = time.time()
|
||||
while time.time() - start_time < self.warmup_s:
|
||||
warmup_read(timeout_ms=self.warmup_s * 1000)
|
||||
self.async_read(timeout_ms=self.warmup_s * 1000)
|
||||
time.sleep(0.1)
|
||||
with self.frame_lock:
|
||||
if (self.use_rgb and self.latest_color_frame is None) or (
|
||||
self.use_depth and self.latest_depth_frame is None
|
||||
):
|
||||
if self.latest_color_frame is None or self.use_depth and self.latest_depth_frame is None:
|
||||
raise ConnectionError(f"{self} failed to capture frames during warmup.")
|
||||
|
||||
logger.info(f"{self} connected.")
|
||||
@@ -272,13 +268,13 @@ class RealSenseCamera(Camera):
|
||||
)
|
||||
|
||||
if len(found_devices) > 1:
|
||||
serial_numbers = [dev["id"] for dev in found_devices]
|
||||
serial_numbers = [dev["serial_number"] for dev in found_devices]
|
||||
raise ValueError(
|
||||
f"Multiple RealSense cameras found with name '{name}'. "
|
||||
f"Please use a unique serial number instead. Found SNs: {serial_numbers}"
|
||||
)
|
||||
|
||||
serial_number = str(found_devices[0]["id"])
|
||||
serial_number = str(found_devices[0]["serial_number"])
|
||||
return serial_number
|
||||
|
||||
def _configure_rs_pipeline_config(self, rs_config: Any) -> None:
|
||||
@@ -286,17 +282,15 @@ class RealSenseCamera(Camera):
|
||||
rs.config.enable_device(rs_config, self.serial_number)
|
||||
|
||||
if self.width and self.height and self.fps:
|
||||
if self.use_rgb:
|
||||
rs_config.enable_stream(
|
||||
rs.stream.color, self.capture_width, self.capture_height, rs.format.rgb8, self.fps
|
||||
)
|
||||
rs_config.enable_stream(
|
||||
rs.stream.color, self.capture_width, self.capture_height, rs.format.rgb8, self.fps
|
||||
)
|
||||
if self.use_depth:
|
||||
rs_config.enable_stream(
|
||||
rs.stream.depth, self.capture_width, self.capture_height, rs.format.z16, self.fps
|
||||
)
|
||||
else:
|
||||
if self.use_rgb:
|
||||
rs_config.enable_stream(rs.stream.color)
|
||||
rs_config.enable_stream(rs.stream.color)
|
||||
if self.use_depth:
|
||||
rs_config.enable_stream(rs.stream.depth)
|
||||
|
||||
@@ -304,9 +298,8 @@ class RealSenseCamera(Camera):
|
||||
def _configure_capture_settings(self) -> None:
|
||||
"""Sets fps, width, and height from device stream if not already configured.
|
||||
|
||||
Uses the color stream profile (or the depth stream profile when the color
|
||||
stream is disabled) to update unset attributes. Handles rotation by swapping
|
||||
width/height when needed. Original capture dimensions are always stored.
|
||||
Uses the color stream profile to update unset attributes. Handles rotation by
|
||||
swapping width/height when needed. Original capture dimensions are always stored.
|
||||
|
||||
Raises:
|
||||
DeviceNotConnectedError: If device is not connected.
|
||||
@@ -315,8 +308,7 @@ class RealSenseCamera(Camera):
|
||||
if self.rs_profile is None:
|
||||
raise RuntimeError(f"{self}: rs_profile must be initialized before use.")
|
||||
|
||||
rs_stream = rs.stream.color if self.use_rgb else rs.stream.depth
|
||||
stream = self.rs_profile.get_stream(rs_stream).as_video_stream_profile()
|
||||
stream = self.rs_profile.get_stream(rs.stream.color).as_video_stream_profile()
|
||||
|
||||
if self.fps is None:
|
||||
self.fps = stream.fps()
|
||||
@@ -331,14 +323,6 @@ class RealSenseCamera(Camera):
|
||||
self.width, self.height = actual_width, actual_height
|
||||
self.capture_width, self.capture_height = actual_width, actual_height
|
||||
|
||||
def _read(self, read_depth: bool = False) -> NDArray[Any]:
|
||||
"""Shared helper for :meth:`read`/:meth:`read_depth`: wait for a fresh color or depth frame."""
|
||||
if self.thread is None or not self.thread.is_alive():
|
||||
raise RuntimeError(f"{self} read thread is not running.")
|
||||
|
||||
self.new_frame_event.clear()
|
||||
return self._async_read(timeout_ms=10000, read_depth=read_depth)
|
||||
|
||||
@check_if_not_connected
|
||||
def read_depth(self, timeout_ms: int = 200) -> NDArray[Any]:
|
||||
"""
|
||||
@@ -348,8 +332,8 @@ class RealSenseCamera(Camera):
|
||||
from the camera hardware via the RealSense pipeline.
|
||||
|
||||
Returns:
|
||||
np.ndarray: The depth map as a NumPy array (height, width, 1)
|
||||
of type `np.uint16` (raw depth values in millimeters).
|
||||
np.ndarray: The depth map as a NumPy array (height, width)
|
||||
of type `np.uint16` (raw depth values in millimeters) and rotation.
|
||||
|
||||
Raises:
|
||||
DeviceNotConnectedError: If the camera is not connected.
|
||||
@@ -365,7 +349,20 @@ class RealSenseCamera(Camera):
|
||||
f"Failed to capture depth frame '.read_depth()'. Depth stream is not enabled for {self}."
|
||||
)
|
||||
|
||||
return self._read(read_depth=True)
|
||||
if self.thread is None or not self.thread.is_alive():
|
||||
raise RuntimeError(f"{self} read thread is not running.")
|
||||
|
||||
self.new_frame_event.clear()
|
||||
|
||||
_ = self.async_read(timeout_ms=10000)
|
||||
|
||||
with self.frame_lock:
|
||||
depth_map = self.latest_depth_frame
|
||||
|
||||
if depth_map is None:
|
||||
raise RuntimeError("No depth frame available. Ensure camera is streaming.")
|
||||
|
||||
return depth_map
|
||||
|
||||
def _read_from_hardware(self):
|
||||
if self.rs_pipeline is None:
|
||||
@@ -408,10 +405,12 @@ class RealSenseCamera(Camera):
|
||||
f"{self} read() timeout_ms parameter is deprecated and will be removed in future versions."
|
||||
)
|
||||
|
||||
if not self.use_rgb:
|
||||
raise RuntimeError(f"{self}: cannot read color — camera was configured with use_rgb=False.")
|
||||
if self.thread is None or not self.thread.is_alive():
|
||||
raise RuntimeError(f"{self} read thread is not running.")
|
||||
|
||||
frame = self._read()
|
||||
self.new_frame_event.clear()
|
||||
|
||||
frame = self.async_read(timeout_ms=10000)
|
||||
|
||||
read_duration_ms = (time.perf_counter() - start_time) * 1e3
|
||||
logger.debug(f"{self} read took: {read_duration_ms:.1f}ms")
|
||||
@@ -466,38 +465,32 @@ class RealSenseCamera(Camera):
|
||||
Internal loop run by the background thread for asynchronous reading.
|
||||
|
||||
On each iteration:
|
||||
1. Reads a color/depth frame (blocking call with 10s timeout)
|
||||
2. Stores result in latest_color_frame/latest_depth_frame and updates timestamp (thread-safe)
|
||||
1. Reads a color frame with 500ms timeout
|
||||
2. Stores result in latest_frame and updates timestamp (thread-safe)
|
||||
3. Sets new_frame_event to notify listeners
|
||||
|
||||
Stops on DeviceNotConnectedError, logs other errors and continues.
|
||||
"""
|
||||
stop_event = self.stop_event
|
||||
if stop_event is None:
|
||||
if self.stop_event is None:
|
||||
raise RuntimeError(f"{self}: stop_event is not initialized before starting read loop.")
|
||||
|
||||
failure_count = 0
|
||||
while not stop_event.is_set():
|
||||
while not self.stop_event.is_set():
|
||||
try:
|
||||
frame = self._read_from_hardware()
|
||||
|
||||
if self.use_rgb:
|
||||
color_frame_raw = frame.get_color_frame()
|
||||
color_frame = np.asanyarray(color_frame_raw.get_data())
|
||||
processed_color_frame = self._postprocess_image(color_frame)
|
||||
color_frame_raw = frame.get_color_frame()
|
||||
color_frame = np.asanyarray(color_frame_raw.get_data())
|
||||
processed_color_frame = self._postprocess_image(color_frame)
|
||||
|
||||
if self.use_depth:
|
||||
depth_frame_raw = frame.get_depth_frame()
|
||||
depth_frame = np.asanyarray(depth_frame_raw.get_data())
|
||||
processed_depth_frame = self._postprocess_image(depth_frame, depth_frame=True)
|
||||
if processed_depth_frame.ndim == 2: # (H, W) -> (H, W, 1)
|
||||
processed_depth_frame = processed_depth_frame[..., np.newaxis]
|
||||
|
||||
capture_time = time.perf_counter()
|
||||
|
||||
with self.frame_lock:
|
||||
if self.use_rgb:
|
||||
self.latest_color_frame = processed_color_frame
|
||||
self.latest_color_frame = processed_color_frame
|
||||
if self.use_depth:
|
||||
self.latest_depth_frame = processed_depth_frame
|
||||
self.latest_timestamp = capture_time
|
||||
@@ -529,8 +522,6 @@ class RealSenseCamera(Camera):
|
||||
|
||||
if self.thread is not None and self.thread.is_alive():
|
||||
self.thread.join(timeout=2.0)
|
||||
if self.thread.is_alive(): # pragma: no cover
|
||||
logger.warning(f"{self} read thread did not terminate within timeout.")
|
||||
|
||||
self.thread = None
|
||||
self.stop_event = None
|
||||
@@ -541,26 +532,7 @@ class RealSenseCamera(Camera):
|
||||
self.latest_timestamp = None
|
||||
self.new_frame_event.clear()
|
||||
|
||||
def _async_read(self, timeout_ms: float, read_depth: bool = False) -> NDArray[Any]:
|
||||
"""Shared helper for :meth:`async_read`/:meth:`async_read_depth`: return the latest buffered frame."""
|
||||
if self.thread is None or not self.thread.is_alive():
|
||||
raise RuntimeError(f"{self} read thread is not running.")
|
||||
|
||||
if not self.new_frame_event.wait(timeout=timeout_ms / 1000.0):
|
||||
raise TimeoutError(
|
||||
f"Timed out waiting for frame from camera {self} after {timeout_ms} ms. "
|
||||
f"Read thread alive: {self.thread.is_alive()}."
|
||||
)
|
||||
|
||||
with self.frame_lock:
|
||||
frame = self.latest_depth_frame if read_depth else self.latest_color_frame
|
||||
self.new_frame_event.clear()
|
||||
|
||||
if frame is None:
|
||||
raise RuntimeError(f"Internal error: Event set but no frame available for {self}.")
|
||||
|
||||
return frame
|
||||
|
||||
# NOTE(Steven): Missing implementation for depth for now
|
||||
@check_if_not_connected
|
||||
def async_read(self, timeout_ms: float = 200) -> NDArray[Any]:
|
||||
"""
|
||||
@@ -585,31 +557,25 @@ class RealSenseCamera(Camera):
|
||||
RuntimeError: If the background thread died unexpectedly or another error occurs.
|
||||
"""
|
||||
|
||||
if not self.use_rgb:
|
||||
raise RuntimeError(f"{self}: cannot read color — camera was configured with use_rgb=False.")
|
||||
|
||||
return self._async_read(timeout_ms=timeout_ms)
|
||||
|
||||
def _read_latest(self, max_age_ms: int, read_depth: bool = False) -> NDArray[Any]:
|
||||
"""Shared helper for :meth:`read_latest`/:meth:`read_latest_depth`: peek the latest buffered frame."""
|
||||
if self.thread is None or not self.thread.is_alive():
|
||||
raise RuntimeError(f"{self} read thread is not running.")
|
||||
|
||||
with self.frame_lock:
|
||||
frame = self.latest_depth_frame if read_depth else self.latest_color_frame
|
||||
timestamp = self.latest_timestamp
|
||||
|
||||
if frame is None or timestamp is None:
|
||||
raise RuntimeError(f"{self} has not captured any frames yet.")
|
||||
|
||||
age_ms = (time.perf_counter() - timestamp) * 1e3
|
||||
if age_ms > max_age_ms:
|
||||
if not self.new_frame_event.wait(timeout=timeout_ms / 1000.0):
|
||||
raise TimeoutError(
|
||||
f"{self} latest frame is too old: {age_ms:.1f} ms (max allowed: {max_age_ms} ms)."
|
||||
f"Timed out waiting for frame from camera {self} after {timeout_ms} ms. "
|
||||
f"Read thread alive: {self.thread.is_alive()}."
|
||||
)
|
||||
|
||||
with self.frame_lock:
|
||||
frame = self.latest_color_frame
|
||||
self.new_frame_event.clear()
|
||||
|
||||
if frame is None:
|
||||
raise RuntimeError(f"Internal error: Event set but no frame available for {self}.")
|
||||
|
||||
return frame
|
||||
|
||||
# NOTE(Steven): Missing implementation for depth for now
|
||||
@check_if_not_connected
|
||||
def read_latest(self, max_age_ms: int = 500) -> NDArray[Any]:
|
||||
"""Return the most recent (color) frame captured immediately (Peeking).
|
||||
@@ -626,48 +592,24 @@ class RealSenseCamera(Camera):
|
||||
DeviceNotConnectedError: If the camera is not connected.
|
||||
RuntimeError: If the camera is connected but has not captured any frames yet.
|
||||
"""
|
||||
if not self.use_rgb:
|
||||
raise RuntimeError(f"{self}: cannot read color — camera was configured with use_rgb=False.")
|
||||
|
||||
return self._read_latest(max_age_ms=max_age_ms)
|
||||
if self.thread is None or not self.thread.is_alive():
|
||||
raise RuntimeError(f"{self} read thread is not running.")
|
||||
|
||||
@check_if_not_connected
|
||||
def async_read_depth(self, timeout_ms: float = 200) -> NDArray[np.uint16]:
|
||||
"""Read the latest depth frame asynchronously, in millimeters.
|
||||
with self.frame_lock:
|
||||
frame = self.latest_color_frame
|
||||
timestamp = self.latest_timestamp
|
||||
|
||||
Mirrors :meth:`async_read` but returns the depth stream rather than the
|
||||
color stream. Output is ``np.uint16`` of shape ``(H, W, 1)``, where each
|
||||
pixel is the distance from the sensor in millimeters.
|
||||
if frame is None or timestamp is None:
|
||||
raise RuntimeError(f"{self} has not captured any frames yet.")
|
||||
|
||||
Raises:
|
||||
DeviceNotConnectedError: If the camera is not connected.
|
||||
RuntimeError: If ``use_depth`` is ``False`` for this camera, or if
|
||||
the background read thread is not running.
|
||||
TimeoutError: If no frame becomes available within ``timeout_ms``.
|
||||
"""
|
||||
if not self.use_depth:
|
||||
raise RuntimeError(f"{self}: cannot read depth — camera was configured with use_depth=False.")
|
||||
age_ms = (time.perf_counter() - timestamp) * 1e3
|
||||
if age_ms > max_age_ms:
|
||||
raise TimeoutError(
|
||||
f"{self} latest frame is too old: {age_ms:.1f} ms (max allowed: {max_age_ms} ms)."
|
||||
)
|
||||
|
||||
return self._async_read(timeout_ms=timeout_ms, read_depth=True)
|
||||
|
||||
@check_if_not_connected
|
||||
def read_latest_depth(self, max_age_ms: int = 500) -> NDArray[Any]:
|
||||
"""Return the most recent depth frame in millimeters (peeking).
|
||||
|
||||
Non-blocking counterpart of :meth:`read_latest` for the depth stream.
|
||||
Output is ``np.uint16`` of shape ``(H, W, 1)``, where each pixel is the
|
||||
distance from the sensor in millimeters.
|
||||
|
||||
Raises:
|
||||
DeviceNotConnectedError: If the camera is not connected.
|
||||
RuntimeError: If ``use_depth`` is ``False`` for this camera, or if
|
||||
no depth frame has been captured yet.
|
||||
TimeoutError: If the latest depth frame is older than ``max_age_ms``.
|
||||
"""
|
||||
if not self.use_depth:
|
||||
raise RuntimeError(f"{self}: cannot read depth — camera was configured with use_depth=False.")
|
||||
|
||||
return self._read_latest(max_age_ms=max_age_ms, read_depth=True)
|
||||
return frame
|
||||
|
||||
def disconnect(self) -> None:
|
||||
"""
|
||||
|
||||
@@ -42,14 +42,12 @@ class RealSenseCameraConfig(CameraConfig):
|
||||
height: Requested frame height in pixels for the color stream.
|
||||
serial_number_or_name: Unique serial number or human-readable name to identify the camera.
|
||||
color_mode: Color mode for image output (RGB or BGR). Defaults to RGB.
|
||||
use_rgb: Whether to enable the color stream. Defaults to True.
|
||||
use_depth: Whether to enable depth stream. Defaults to False.
|
||||
rotation: Image rotation setting (0°, 90°, 180°, or 270°). Defaults to no rotation.
|
||||
warmup_s: Time reading frames before returning from connect (in seconds)
|
||||
|
||||
Note:
|
||||
- Either name or serial_number must be specified.
|
||||
- At least one of `use_rgb` or `use_depth` must be enabled.
|
||||
- Depth stream configuration (if enabled) will use the same FPS as the color stream.
|
||||
- The actual resolution and FPS may be adjusted by the camera to the nearest supported mode.
|
||||
- For `fps`, `width` and `height`, either all of them need to be set, or none of them.
|
||||
@@ -57,7 +55,6 @@ class RealSenseCameraConfig(CameraConfig):
|
||||
|
||||
serial_number_or_name: str
|
||||
color_mode: ColorMode = ColorMode.RGB
|
||||
use_rgb: bool = True
|
||||
use_depth: bool = False
|
||||
rotation: Cv2Rotation = Cv2Rotation.NO_ROTATION
|
||||
warmup_s: int = 1
|
||||
@@ -66,9 +63,6 @@ class RealSenseCameraConfig(CameraConfig):
|
||||
self.color_mode = ColorMode(self.color_mode)
|
||||
self.rotation = Cv2Rotation(self.rotation)
|
||||
|
||||
if not self.use_rgb and not self.use_depth:
|
||||
raise ValueError("At least one of `use_rgb` or `use_depth` must be enabled.")
|
||||
|
||||
values = (self.fps, self.width, self.height)
|
||||
if any(v is not None for v in values) and any(v is None for v in values):
|
||||
raise ValueError(
|
||||
|
||||
@@ -246,12 +246,11 @@ class ZMQCamera(Camera):
|
||||
"""
|
||||
Internal loop run by the background thread for asynchronous reading.
|
||||
"""
|
||||
stop_event = self.stop_event
|
||||
if stop_event is None:
|
||||
if self.stop_event is None:
|
||||
raise RuntimeError(f"{self}: stop_event is not initialized.")
|
||||
|
||||
failure_count = 0
|
||||
while not stop_event.is_set():
|
||||
while not self.stop_event.is_set():
|
||||
try:
|
||||
frame = self._read_from_hardware()
|
||||
capture_time = time.perf_counter()
|
||||
@@ -293,8 +292,6 @@ class ZMQCamera(Camera):
|
||||
|
||||
if self.thread is not None and self.thread.is_alive():
|
||||
self.thread.join(timeout=2.0)
|
||||
if self.thread.is_alive():
|
||||
logger.warning(f"{self} read thread did not terminate within timeout.")
|
||||
|
||||
self.thread = None
|
||||
self.stop_event = None
|
||||
|
||||
@@ -17,9 +17,12 @@ from __future__ import annotations
|
||||
########################################################################################
|
||||
# Utilities
|
||||
########################################################################################
|
||||
import logging
|
||||
import time
|
||||
import traceback
|
||||
from contextlib import nullcontext
|
||||
from copy import copy
|
||||
from functools import cache
|
||||
from typing import TYPE_CHECKING, Any
|
||||
|
||||
import numpy as np
|
||||
@@ -40,6 +43,34 @@ from lerobot.robots import Robot
|
||||
from lerobot.types import PolicyAction
|
||||
|
||||
|
||||
@cache
|
||||
def is_headless():
|
||||
"""
|
||||
Detects if the Python script is running in a headless environment (e.g., without a display).
|
||||
|
||||
This function attempts to import `pynput`, a library that requires a graphical environment.
|
||||
If the import fails, it assumes the environment is headless. The result is cached to avoid
|
||||
re-running the check.
|
||||
|
||||
Returns:
|
||||
True if the environment is determined to be headless, False otherwise.
|
||||
"""
|
||||
try:
|
||||
import pynput # noqa
|
||||
|
||||
return False
|
||||
except Exception:
|
||||
print(
|
||||
"Error trying to import pynput. Switching to headless mode. "
|
||||
"As a result, the video stream from the cameras won't be shown, "
|
||||
"and you won't be able to change the control flow with keyboards. "
|
||||
"For more info, see traceback below.\n"
|
||||
)
|
||||
traceback.print_exc()
|
||||
print()
|
||||
return True
|
||||
|
||||
|
||||
def predict_action(
|
||||
observation: dict[str, np.ndarray],
|
||||
policy: PreTrainedPolicy,
|
||||
@@ -91,6 +122,59 @@ def predict_action(
|
||||
return action
|
||||
|
||||
|
||||
def init_keyboard_listener():
|
||||
"""
|
||||
Initializes a non-blocking keyboard listener for real-time user interaction.
|
||||
|
||||
This function sets up a listener for specific keys (right arrow, left arrow, escape) to control
|
||||
the program flow during execution, such as stopping recording or exiting loops. It gracefully
|
||||
handles headless environments where keyboard listening is not possible.
|
||||
|
||||
Returns:
|
||||
A tuple containing:
|
||||
- The `pynput.keyboard.Listener` instance, or `None` if in a headless environment.
|
||||
- A dictionary of event flags (e.g., `exit_early`) that are set by key presses.
|
||||
"""
|
||||
# Allow to exit early while recording an episode or resetting the environment,
|
||||
# by tapping the right arrow key '->'. This might require a sudo permission
|
||||
# to allow your terminal to monitor keyboard events.
|
||||
events = {}
|
||||
events["exit_early"] = False
|
||||
events["rerecord_episode"] = False
|
||||
events["stop_recording"] = False
|
||||
|
||||
if is_headless():
|
||||
logging.warning(
|
||||
"Headless environment detected. On-screen cameras display and keyboard inputs will not be available."
|
||||
)
|
||||
listener = None
|
||||
return listener, events
|
||||
|
||||
# Only import pynput if not in a headless environment
|
||||
from pynput import keyboard
|
||||
|
||||
def on_press(key):
|
||||
try:
|
||||
if key == keyboard.Key.right:
|
||||
print("Right arrow key pressed. Exiting loop...")
|
||||
events["exit_early"] = True
|
||||
elif key == keyboard.Key.left:
|
||||
print("Left arrow key pressed. Exiting loop and rerecord the last episode...")
|
||||
events["rerecord_episode"] = True
|
||||
events["exit_early"] = True
|
||||
elif key == keyboard.Key.esc:
|
||||
print("Escape key pressed. Stopping data recording...")
|
||||
events["stop_recording"] = True
|
||||
events["exit_early"] = True
|
||||
except Exception as e:
|
||||
print(f"Error handling key press: {e}")
|
||||
|
||||
listener = keyboard.Listener(on_press=on_press)
|
||||
listener.start()
|
||||
|
||||
return listener, events
|
||||
|
||||
|
||||
def sanity_check_dataset_name(repo_id, policy_cfg):
|
||||
"""
|
||||
Validates the dataset repository name against the presence of a policy configuration.
|
||||
|
||||
@@ -15,14 +15,12 @@
|
||||
# limitations under the License.
|
||||
from pathlib import Path
|
||||
|
||||
from huggingface_hub import HfApi, snapshot_download
|
||||
from torch.optim import Optimizer
|
||||
from torch.optim.lr_scheduler import LRScheduler
|
||||
|
||||
from lerobot.configs.train import TrainPipelineConfig
|
||||
from lerobot.optim import (
|
||||
load_optimizer_state,
|
||||
load_optimizer_state_dict,
|
||||
load_scheduler_state,
|
||||
save_optimizer_state,
|
||||
save_scheduler_state,
|
||||
@@ -36,7 +34,6 @@ from lerobot.utils.constants import (
|
||||
TRAINING_STATE_DIR,
|
||||
TRAINING_STEP,
|
||||
)
|
||||
from lerobot.utils.hub import find_latest_hub_checkpoint
|
||||
from lerobot.utils.io_utils import load_json, write_json
|
||||
from lerobot.utils.random_utils import load_rng_state, save_rng_state
|
||||
|
||||
@@ -52,19 +49,8 @@ def get_step_checkpoint_dir(output_dir: Path, total_steps: int, step: int) -> Pa
|
||||
return output_dir / CHECKPOINTS_DIR / step_identifier
|
||||
|
||||
|
||||
def save_training_step(
|
||||
step: int, save_dir: Path, num_processes: int | None = None, batch_size: int | None = None
|
||||
) -> None:
|
||||
state: dict = {"step": step}
|
||||
# num_processes and batch_size are recorded so a resumed run can detect a changed world size or
|
||||
# batch size: the sampler's resume offset is computed from the (num_processes, batch_size) that
|
||||
# produced `step`, since both scale how many sampler positions a step consumes (see
|
||||
# compute_sampler_state).
|
||||
if num_processes is not None:
|
||||
state["num_processes"] = num_processes
|
||||
if batch_size is not None:
|
||||
state["batch_size"] = batch_size
|
||||
write_json(state, save_dir / TRAINING_STEP)
|
||||
def save_training_step(step: int, save_dir: Path) -> None:
|
||||
write_json({"step": step}, save_dir / TRAINING_STEP)
|
||||
|
||||
|
||||
def load_training_step(save_dir: Path) -> int:
|
||||
@@ -72,16 +58,6 @@ def load_training_step(save_dir: Path) -> int:
|
||||
return training_step["step"]
|
||||
|
||||
|
||||
def load_training_num_processes(checkpoint_dir: Path) -> int | None:
|
||||
"""World size recorded at checkpoint time, or None for checkpoints written before it was stored."""
|
||||
return load_json(checkpoint_dir / TRAINING_STATE_DIR / TRAINING_STEP).get("num_processes")
|
||||
|
||||
|
||||
def load_training_batch_size(checkpoint_dir: Path) -> int | None:
|
||||
"""Per-process batch size recorded at checkpoint time, or None for older checkpoints."""
|
||||
return load_json(checkpoint_dir / TRAINING_STATE_DIR / TRAINING_STEP).get("batch_size")
|
||||
|
||||
|
||||
def update_last_checkpoint(checkpoint_dir: Path) -> Path:
|
||||
last_checkpoint_dir = checkpoint_dir.parent / LAST_CHECKPOINT_LINK
|
||||
if last_checkpoint_dir.is_symlink():
|
||||
@@ -99,10 +75,6 @@ def save_checkpoint(
|
||||
scheduler: LRScheduler | None = None,
|
||||
preprocessor: PolicyProcessorPipeline | None = None,
|
||||
postprocessor: PolicyProcessorPipeline | None = None,
|
||||
num_processes: int | None = None,
|
||||
batch_size: int | None = None,
|
||||
model_state_dict: dict | None = None,
|
||||
optim_state_dict: dict | None = None,
|
||||
) -> None:
|
||||
"""This function creates the following directory structure:
|
||||
|
||||
@@ -128,22 +100,9 @@ def save_checkpoint(
|
||||
scheduler (LRScheduler | None, optional): The scheduler to save the state from. Defaults to None.
|
||||
preprocessor: The preprocessor/pipeline to save. Defaults to None.
|
||||
postprocessor: The postprocessor/pipeline to save. Defaults to None.
|
||||
num_processes (int | None, optional): Distributed world size to record for sample-exact
|
||||
resume. Defaults to None (not recorded).
|
||||
batch_size (int | None, optional): Per-process batch size to record for sample-exact
|
||||
resume. Defaults to None (not recorded).
|
||||
model_state_dict: Pre-gathered full (unsharded) model state dict. Required under FSDP,
|
||||
where `policy.state_dict()` would return sharded tensors; the caller gathers it via a
|
||||
cross-rank collective and passes it here so rank 0 can write it directly. It holds
|
||||
FSDP's fp32 master weights and is saved as-is (the loader casts to the policy dtype on
|
||||
read). When None (DDP / single-GPU), the model is saved the normal way. Defaults to None.
|
||||
optim_state_dict: Pre-gathered full (unsharded) optimizer state dict. Required under FSDP
|
||||
(gathered alongside `model_state_dict` via `gather_fsdp_state_dicts`); saved in the same
|
||||
safetensors format as the single-GPU path. When None, `optimizer.state_dict()` is used.
|
||||
Defaults to None.
|
||||
"""
|
||||
pretrained_dir = checkpoint_dir / PRETRAINED_MODEL_DIR
|
||||
policy.save_pretrained(pretrained_dir, state_dict=model_state_dict)
|
||||
policy.save_pretrained(pretrained_dir)
|
||||
cfg.save_pretrained(pretrained_dir)
|
||||
if cfg.peft is not None:
|
||||
# When using PEFT, policy.save_pretrained will only write the adapter weights + config, not the
|
||||
@@ -153,15 +112,7 @@ def save_checkpoint(
|
||||
preprocessor.save_pretrained(pretrained_dir)
|
||||
if postprocessor is not None:
|
||||
postprocessor.save_pretrained(pretrained_dir)
|
||||
save_training_state(
|
||||
checkpoint_dir,
|
||||
step,
|
||||
optimizer,
|
||||
scheduler,
|
||||
num_processes=num_processes,
|
||||
batch_size=batch_size,
|
||||
optim_state_dict=optim_state_dict,
|
||||
)
|
||||
save_training_state(checkpoint_dir, step, optimizer, scheduler)
|
||||
|
||||
|
||||
def save_training_state(
|
||||
@@ -169,9 +120,6 @@ def save_training_state(
|
||||
train_step: int,
|
||||
optimizer: Optimizer | None = None,
|
||||
scheduler: LRScheduler | None = None,
|
||||
num_processes: int | None = None,
|
||||
batch_size: int | None = None,
|
||||
optim_state_dict: dict | None = None,
|
||||
) -> None:
|
||||
"""
|
||||
Saves the training step, optimizer state, scheduler state, and rng state.
|
||||
@@ -183,23 +131,19 @@ def save_training_state(
|
||||
Defaults to None.
|
||||
scheduler (LRScheduler | None, optional): The scheduler from which to save the state_dict.
|
||||
Defaults to None.
|
||||
num_processes (int | None, optional): Distributed world size to record. Defaults to None.
|
||||
batch_size (int | None, optional): Per-process batch size to record. Defaults to None.
|
||||
optim_state_dict: Pre-gathered full optimizer state dict (for FSDP). Saved instead of
|
||||
`optimizer.state_dict()` when provided. Defaults to None.
|
||||
"""
|
||||
save_dir = checkpoint_dir / TRAINING_STATE_DIR
|
||||
save_dir.mkdir(parents=True, exist_ok=True)
|
||||
save_training_step(train_step, save_dir, num_processes=num_processes, batch_size=batch_size)
|
||||
save_training_step(train_step, save_dir)
|
||||
save_rng_state(save_dir)
|
||||
if optimizer is not None:
|
||||
save_optimizer_state(optimizer, save_dir, optim_state_dict=optim_state_dict)
|
||||
save_optimizer_state(optimizer, save_dir)
|
||||
if scheduler is not None:
|
||||
save_scheduler_state(scheduler, save_dir)
|
||||
|
||||
|
||||
def load_training_state(
|
||||
checkpoint_dir: Path, optimizer: Optimizer, scheduler: LRScheduler | None, load_optimizer: bool = True
|
||||
checkpoint_dir: Path, optimizer: Optimizer, scheduler: LRScheduler | None
|
||||
) -> tuple[int, Optimizer, LRScheduler | None]:
|
||||
"""
|
||||
Loads the training step, optimizer state, scheduler state, and rng state.
|
||||
@@ -209,10 +153,6 @@ def load_training_state(
|
||||
checkpoint_dir (Path): The checkpoint directory. Should contain a 'training_state' dir.
|
||||
optimizer (Optimizer): The optimizer to load the state_dict to.
|
||||
scheduler (LRScheduler | None): The scheduler to load the state_dict to (can be None).
|
||||
load_optimizer (bool, optional): Whether to load the optimizer state from disk. Defaults to
|
||||
True. Set to False under FSDP, where the sharded optimizer state must be loaded after
|
||||
`accelerator.prepare()` via `load_fsdp_optimizer_state` (the optimizer is returned
|
||||
untouched here).
|
||||
|
||||
Raises:
|
||||
NotADirectoryError: If 'checkpoint_dir' doesn't contain a 'training_state' dir
|
||||
@@ -227,119 +167,8 @@ def load_training_state(
|
||||
|
||||
load_rng_state(training_state_dir)
|
||||
step = load_training_step(training_state_dir)
|
||||
if load_optimizer:
|
||||
optimizer = load_optimizer_state(optimizer, training_state_dir)
|
||||
optimizer = load_optimizer_state(optimizer, training_state_dir)
|
||||
if scheduler is not None:
|
||||
scheduler = load_scheduler_state(scheduler, training_state_dir)
|
||||
|
||||
return step, optimizer, scheduler
|
||||
|
||||
|
||||
def gather_fsdp_state_dicts(model, optimizer) -> tuple[dict, dict]:
|
||||
"""Gather the full (unsharded) model and optimizer state dicts under FSDP.
|
||||
|
||||
`model.state_dict()` and `FSDP.optim_state_dict(...)` are cross-rank collectives, so this must be
|
||||
called on *every* rank with the prepared (FSDP-wrapped) `model` and `optimizer`. With
|
||||
`rank0_only=True` and `offload_to_cpu=True`, every rank runs the all-gather but only rank 0
|
||||
materializes the full dicts (the others get empty dicts) and they are kept on CPU to bound GPU
|
||||
memory. The returned optimizer state dict is keyed by parameter FQNs and is world-size
|
||||
independent; `load_fsdp_optimizer_state` reshards it on resume.
|
||||
|
||||
Returns:
|
||||
(model_state_dict, optim_state_dict): full dicts on rank 0, empty dicts on other ranks.
|
||||
"""
|
||||
from torch.distributed.fsdp import (
|
||||
FullOptimStateDictConfig,
|
||||
FullStateDictConfig,
|
||||
FullyShardedDataParallel as FSDP, # noqa F401
|
||||
StateDictType,
|
||||
)
|
||||
|
||||
state_cfg = FullStateDictConfig(offload_to_cpu=True, rank0_only=True)
|
||||
optim_cfg = FullOptimStateDictConfig(offload_to_cpu=True, rank0_only=True)
|
||||
with FSDP.state_dict_type(model, StateDictType.FULL_STATE_DICT, state_cfg, optim_cfg):
|
||||
model_state_dict = model.state_dict()
|
||||
optim_state_dict = FSDP.optim_state_dict(model, optimizer)
|
||||
return model_state_dict, optim_state_dict
|
||||
|
||||
|
||||
def load_fsdp_optimizer_state(model, optimizer, checkpoint_dir: Path) -> None:
|
||||
"""Load the FSDP optimizer state (saved as safetensors) and reshard it into the optimizer.
|
||||
|
||||
This is a cross-rank collective and must be called on every rank *after* `accelerator.prepare()`
|
||||
with the prepared (FSDP-wrapped) `model` and `optimizer`. The saved state is the full,
|
||||
world-size-independent optimizer state (keyed by parameter FQNs); `FSDP.optim_state_dict_to_load`
|
||||
reshards it to the current FSDP topology, so resume on a different number of GPUs works.
|
||||
"""
|
||||
from torch.distributed.fsdp import (
|
||||
FullOptimStateDictConfig,
|
||||
FullStateDictConfig,
|
||||
FullyShardedDataParallel as FSDP, # noqa F401
|
||||
StateDictType,
|
||||
)
|
||||
|
||||
# Every rank reads the same full state from the (shared) checkpoint dir, so rank0_only=False.
|
||||
full_osd = load_optimizer_state_dict(checkpoint_dir / TRAINING_STATE_DIR)
|
||||
state_cfg = FullStateDictConfig(rank0_only=False)
|
||||
optim_cfg = FullOptimStateDictConfig(rank0_only=False)
|
||||
with FSDP.state_dict_type(model, StateDictType.FULL_STATE_DICT, state_cfg, optim_cfg):
|
||||
sharded_osd = FSDP.optim_state_dict_to_load(model=model, optim=optimizer, optim_state_dict=full_osd)
|
||||
optimizer.load_state_dict(sharded_osd)
|
||||
|
||||
|
||||
def push_checkpoint_to_hub(
|
||||
checkpoint_dir: Path,
|
||||
repo_id: str,
|
||||
*,
|
||||
private: bool | None = None,
|
||||
) -> None:
|
||||
"""Upload a saved checkpoint directory to the Hub under checkpoints/<name>/.
|
||||
|
||||
Called once per save step when save_checkpoint_to_hub is enabled, so a
|
||||
timed-out or crashed run still leaves recoverable checkpoints on the Hub.
|
||||
The model repo is created idempotently, and the commit is tagged with the
|
||||
checkpoint step so a checkpoint can be recovered with
|
||||
--policy.pretrained_revision=<step> instead of a commit sha.
|
||||
"""
|
||||
api = HfApi()
|
||||
api.create_repo(repo_id=repo_id, repo_type="model", private=private, exist_ok=True)
|
||||
commit = api.upload_folder(
|
||||
folder_path=str(checkpoint_dir),
|
||||
repo_id=repo_id,
|
||||
repo_type="model",
|
||||
path_in_repo=f"checkpoints/{checkpoint_dir.name}",
|
||||
commit_message=f"checkpoint {checkpoint_dir.name}",
|
||||
)
|
||||
api.create_tag(
|
||||
repo_id=repo_id,
|
||||
tag=checkpoint_dir.name,
|
||||
revision=commit.oid,
|
||||
repo_type="model",
|
||||
exist_ok=True,
|
||||
)
|
||||
|
||||
|
||||
def resolve_resume_checkpoint(repo_id: str, output_dir: Path) -> Path:
|
||||
"""Download the latest checkpoint of a Hub training repo into a local run dir.
|
||||
|
||||
The symmetric counterpart to `push_checkpoint_to_hub`: given a model repo holding
|
||||
`checkpoints/<step>/{pretrained_model,training_state}` subtrees, download the highest-numbered step
|
||||
into `output_dir/checkpoints/<step>/`, recreate the local `last` symlink, and return that local
|
||||
checkpoint dir. Used to resume training from the Hub on a machine (or HF Jobs pod) that does not
|
||||
have the original local run dir.
|
||||
"""
|
||||
latest = find_latest_hub_checkpoint(repo_id)
|
||||
if latest is None:
|
||||
raise FileNotFoundError(
|
||||
f"No checkpoint found in '{repo_id}' under '{CHECKPOINTS_DIR}/'. "
|
||||
"Was the run trained with --save_checkpoint_to_hub?"
|
||||
)
|
||||
snapshot_download(
|
||||
repo_id=repo_id,
|
||||
repo_type="model",
|
||||
allow_patterns=f"{latest}/*",
|
||||
local_dir=str(output_dir),
|
||||
)
|
||||
checkpoint_dir = output_dir / latest
|
||||
update_last_checkpoint(checkpoint_dir)
|
||||
return checkpoint_dir
|
||||
|
||||
@@ -180,26 +180,24 @@ class WandBLogger:
|
||||
self._wandb_custom_step_key.add(new_custom_key)
|
||||
self._wandb.define_metric(new_custom_key, hidden=True)
|
||||
|
||||
batch_data = {}
|
||||
for k, v in d.items():
|
||||
# Skip the custom step key here, it's added to the batch below.
|
||||
if custom_step_key is not None and k == custom_step_key:
|
||||
continue
|
||||
|
||||
if not isinstance(v, (int | float | str)):
|
||||
logging.warning(
|
||||
f'WandB logging of key "{k}" was ignored as its type "{type(v)}" is not handled by this wrapper.'
|
||||
)
|
||||
continue
|
||||
|
||||
batch_data[f"{mode}/{k}"] = v
|
||||
# Do not log the custom step key itself.
|
||||
if self._wandb_custom_step_key is not None and k in self._wandb_custom_step_key:
|
||||
continue
|
||||
|
||||
if batch_data:
|
||||
if custom_step_key is not None:
|
||||
batch_data[f"{mode}/{custom_step_key}"] = d[custom_step_key]
|
||||
self._wandb.log(batch_data)
|
||||
else:
|
||||
self._wandb.log(data=batch_data, step=step)
|
||||
value_custom_step = d[custom_step_key]
|
||||
data = {f"{mode}/{k}": v, f"{mode}/{custom_step_key}": value_custom_step}
|
||||
self._wandb.log(data)
|
||||
continue
|
||||
|
||||
self._wandb.log(data={f"{mode}/{k}": v}, step=step)
|
||||
|
||||
def log_video(self, video_path: str, step: int, mode: str = "train"):
|
||||
if mode not in {"train", "eval"}:
|
||||
|
||||
@@ -22,7 +22,7 @@ Import them directly: ``from lerobot.configs.train import TrainPipelineConfig``
|
||||
"""
|
||||
|
||||
from .dataset import DatasetRecordConfig
|
||||
from .default import DatasetConfig, EvalConfig, JobConfig, PeftConfig, WandBConfig
|
||||
from .default import DatasetConfig, EvalConfig, PeftConfig, WandBConfig
|
||||
from .policies import PreTrainedConfig
|
||||
from .recipe import MessageTurn, TrainingRecipe, load_recipe
|
||||
from .types import (
|
||||
@@ -33,15 +33,10 @@ from .types import (
|
||||
RTCAttentionSchedule,
|
||||
)
|
||||
from .video import (
|
||||
DEFAULT_DEPTH_UNIT,
|
||||
VALID_VIDEO_CODECS,
|
||||
VIDEO_ENCODER_INFO_KEYS,
|
||||
DepthEncoderConfig,
|
||||
RGBEncoderConfig,
|
||||
VideoEncoderConfig,
|
||||
depth_encoder_defaults,
|
||||
encoder_config_from_video_info,
|
||||
rgb_encoder_defaults,
|
||||
camera_encoder_defaults,
|
||||
)
|
||||
|
||||
__all__ = [
|
||||
@@ -55,7 +50,6 @@ __all__ = [
|
||||
"DatasetRecordConfig",
|
||||
"DatasetConfig",
|
||||
"EvalConfig",
|
||||
"JobConfig",
|
||||
"MessageTurn",
|
||||
"PeftConfig",
|
||||
"PreTrainedConfig",
|
||||
@@ -63,15 +57,9 @@ __all__ = [
|
||||
"WandBConfig",
|
||||
"load_recipe",
|
||||
"VideoEncoderConfig",
|
||||
"RGBEncoderConfig",
|
||||
"DepthEncoderConfig",
|
||||
# Defaults
|
||||
"rgb_encoder_defaults",
|
||||
"depth_encoder_defaults",
|
||||
# Factories
|
||||
"encoder_config_from_video_info",
|
||||
"camera_encoder_defaults",
|
||||
# Constants
|
||||
"DEFAULT_DEPTH_UNIT",
|
||||
"VALID_VIDEO_CODECS",
|
||||
"VIDEO_ENCODER_INFO_KEYS",
|
||||
]
|
||||
|
||||
@@ -18,7 +18,7 @@ from dataclasses import dataclass, field
|
||||
from datetime import datetime
|
||||
from pathlib import Path
|
||||
|
||||
from .video import DepthEncoderConfig, RGBEncoderConfig, depth_encoder_defaults, rgb_encoder_defaults
|
||||
from .video import VideoEncoderConfig, camera_encoder_defaults
|
||||
|
||||
|
||||
@dataclass
|
||||
@@ -58,10 +58,8 @@ class DatasetRecordConfig:
|
||||
# Set to 1 for immediate encoding (default behavior), or higher for batched encoding
|
||||
video_encoding_batch_size: int = 1
|
||||
# Video encoder settings for camera MP4s (codec, quality, GOP, etc.). Tuned via CLI nested keys,
|
||||
# e.g. ``--dataset.rgb_encoder.vcodec=h264`` (see ``RGBEncoderConfig``).
|
||||
rgb_encoder: RGBEncoderConfig = field(default_factory=rgb_encoder_defaults)
|
||||
# Video encoder settings for depth-map MP4s (codec, quality, GOP, etc.). Tuned via CLI nested keys.
|
||||
depth_encoder: DepthEncoderConfig = field(default_factory=depth_encoder_defaults)
|
||||
# e.g. ``--dataset.camera_encoder.vcodec=h264`` (see ``VideoEncoderConfig``).
|
||||
camera_encoder: VideoEncoderConfig = field(default_factory=camera_encoder_defaults)
|
||||
# Enable streaming video encoding: encode frames in real-time during capture instead
|
||||
# of writing PNG images first. Makes save_episode() near-instant. More info in the documentation: https://huggingface.co/docs/lerobot/streaming_video_encoding
|
||||
streaming_encoding: bool = False
|
||||
|
||||
@@ -19,8 +19,6 @@ from dataclasses import dataclass, field
|
||||
from lerobot.transforms import ImageTransformsConfig
|
||||
from lerobot.utils.import_utils import get_safe_default_video_backend
|
||||
|
||||
from .video import DEFAULT_DEPTH_UNIT, DEPTH_METER_UNIT, DEPTH_MILLIMETER_UNIT
|
||||
|
||||
|
||||
@dataclass
|
||||
class DatasetConfig:
|
||||
@@ -37,23 +35,12 @@ class DatasetConfig:
|
||||
revision: str | None = None
|
||||
use_imagenet_stats: bool = True
|
||||
video_backend: str = field(default_factory=get_safe_default_video_backend)
|
||||
# When True, RGB video frames are returned as uint8 tensors (0-255) instead of float32 (0.0-1.0).
|
||||
# When True, video frames are returned as uint8 tensors (0-255) instead of float32 (0.0-1.0).
|
||||
# This reduces memory and speeds up DataLoader IPC. The training pipeline handles the conversion.
|
||||
return_uint8: bool = False
|
||||
# Physical unit depth maps are dequantized to at load time: "mm" (millimeters) or "m" (metres).
|
||||
# Has no effect on datasets without depth cameras.
|
||||
depth_output_unit: str = DEFAULT_DEPTH_UNIT
|
||||
streaming: bool = False
|
||||
# Fraction of episodes held out per task for offline evaluation (0.0 = disabled).
|
||||
eval_split: float = 0.0
|
||||
|
||||
def __post_init__(self) -> None:
|
||||
if self.depth_output_unit not in (DEPTH_METER_UNIT, DEPTH_MILLIMETER_UNIT):
|
||||
raise ValueError(
|
||||
f"depth_output_unit must be '{DEPTH_METER_UNIT}' or '{DEPTH_MILLIMETER_UNIT}', got {self.depth_output_unit!r}"
|
||||
)
|
||||
if not (0.0 <= self.eval_split < 1.0):
|
||||
raise ValueError(f"eval_split must be in [0.0, 1.0), got {self.eval_split}")
|
||||
if self.episodes is not None:
|
||||
if any(ep < 0 for ep in self.episodes):
|
||||
raise ValueError(
|
||||
@@ -86,17 +73,8 @@ class EvalConfig:
|
||||
# `use_async_envs` specifies whether to use asynchronous environments (multiprocessing).
|
||||
# Defaults to True; automatically downgraded to SyncVectorEnv when batch_size=1.
|
||||
use_async_envs: bool = True
|
||||
# Whether to record eval rollouts as a LeRobot dataset on disk.
|
||||
recording: bool = False
|
||||
# If set, push recorded eval datasets to the Hub under this repo id (one repo per task,
|
||||
# suffixed by task and env index). Requires recording=true.
|
||||
recording_repo_id: str | None = None
|
||||
# Whether the pushed recording repositories should be private.
|
||||
recording_private: bool = False
|
||||
|
||||
def __post_init__(self) -> None:
|
||||
if self.recording_repo_id is not None and not self.recording:
|
||||
raise ValueError("eval.recording_repo_id requires eval.recording=true.")
|
||||
if self.batch_size == 0:
|
||||
self.batch_size = self._auto_batch_size()
|
||||
if self.batch_size > self.n_episodes:
|
||||
@@ -145,35 +123,3 @@ class PeftConfig:
|
||||
# If None, the PEFT library defaults to alpha=8, which may dampen high-rank adapters.
|
||||
# Common values are r (alpha == rank) or 2*r.
|
||||
lora_alpha: int | None = None
|
||||
|
||||
|
||||
@dataclass
|
||||
class JobConfig:
|
||||
# Where training runs. None (omitted) or "local" runs on this machine.
|
||||
# Any other value is an HF Jobs flavor and submits the run to HF Jobs.
|
||||
# List available flavors + pricing with `hf jobs hardware` command.
|
||||
target: str | None = None
|
||||
# Runtime image for the remote job (ignored for local runs).
|
||||
image: str = "huggingface/lerobot-gpu:latest"
|
||||
# Max wall-clock for the remote job as an HF Jobs duration string (e.g. "2h").
|
||||
# Defaults to "2d": We pass an explicit, generous cap instead. Set a smaller
|
||||
# value to fail fast, or a larger one for long runs.
|
||||
timeout: str | None = "2d"
|
||||
# Submit and exit instead of streaming the job logs in the foreground.
|
||||
detach: bool = False
|
||||
# Extra tags attached to the HF job and to any dataset this run pushes to the
|
||||
# Hub. A "lerobot" tag is always added; e.g. --job.tags '["lelab"]' adds more.
|
||||
tags: list[str] = field(default_factory=list)
|
||||
|
||||
# Two entry points to the same predicate: the staticmethod tests a raw target string
|
||||
# straight from argv (before any JobConfig exists, to decide dispatch early), while the
|
||||
# property is the ergonomic accessor for code that already holds a config instance.
|
||||
@staticmethod
|
||||
def is_remote_target(target: str | None) -> bool:
|
||||
"""True when `target` names an HF Jobs flavor rather than a local run."""
|
||||
return target not in (None, "local")
|
||||
|
||||
@property
|
||||
def is_remote(self) -> bool:
|
||||
"""True when training should run on HF Jobs rather than this machine."""
|
||||
return self.is_remote_target(self.target)
|
||||
|
||||
@@ -79,8 +79,6 @@ class PreTrainedConfig(draccus.ChoiceRegistry, HubMixin, abc.ABC): # type: igno
|
||||
# Either the repo ID of a model hosted on the Hub or a path to a directory containing weights
|
||||
# saved using `Policy.save_pretrained`. If not provided, the policy is initialized from scratch.
|
||||
pretrained_path: Path | None = None
|
||||
# Optional Hub revision (commit hash, branch, or tag) to pin the pretrained model version.
|
||||
pretrained_revision: str | None = None
|
||||
|
||||
def __post_init__(self) -> None:
|
||||
if not self.device or not is_torch_device_available(self.device):
|
||||
|
||||
@@ -56,8 +56,6 @@ class RewardModelConfig(draccus.ChoiceRegistry, HubMixin, abc.ABC):
|
||||
device: str | None = None
|
||||
|
||||
pretrained_path: str | None = None
|
||||
# Optional Hub revision (commit hash, branch, or tag) to pin the pretrained reward model version.
|
||||
pretrained_revision: str | None = None
|
||||
|
||||
push_to_hub: bool = False
|
||||
repo_id: str | None = None
|
||||
|
||||
+43
-108
@@ -26,12 +26,11 @@ from huggingface_hub.errors import HfHubHTTPError
|
||||
|
||||
from lerobot import envs
|
||||
from lerobot.optim import LRSchedulerConfig, OptimizerConfig
|
||||
from lerobot.utils.constants import PRETRAINED_MODEL_DIR
|
||||
from lerobot.utils.hub import HubMixin, find_latest_hub_checkpoint
|
||||
from lerobot.utils.hub import HubMixin
|
||||
from lerobot.utils.sample_weighting import SampleWeightingConfig
|
||||
|
||||
from . import parser
|
||||
from .default import DatasetConfig, EvalConfig, JobConfig, PeftConfig, WandBConfig
|
||||
from .default import DatasetConfig, EvalConfig, PeftConfig, WandBConfig
|
||||
from .policies import PreTrainedConfig
|
||||
from .rewards import RewardModelConfig
|
||||
|
||||
@@ -84,11 +83,10 @@ class TrainPipelineConfig(HubMixin):
|
||||
# with the same value for `dir` its contents will be overwritten unless you set `resume` to true.
|
||||
output_dir: Path | None = None
|
||||
job_name: str | None = None
|
||||
# Set `resume` to true to resume a previous run. Pass `--config_path` pointing at either a local
|
||||
# checkpoint's train_config.json or a Hub repo id holding `checkpoints/<step>/` subtrees (the
|
||||
# latest checkpoint is downloaded and resumed from). Note that when resuming, the default behavior
|
||||
# is to use the configuration from the checkpoint, regardless of what's provided with the training
|
||||
# command at the time of resumption (CLI `--*` flags still override).
|
||||
# Set `resume` to true to resume a previous run. In order for this to work, you will need to make sure
|
||||
# `dir` is the directory of an existing run with at least one checkpoint in it.
|
||||
# Note that when resuming a run, the default behavior is to use the configuration from the checkpoint,
|
||||
# regardless of what's provided with the training command at the time of resumption.
|
||||
resume: bool = False
|
||||
# `seed` is used for training (eg: model initialization, dataset shuffling)
|
||||
# AND for the evaluation environments.
|
||||
@@ -102,13 +100,8 @@ class TrainPipelineConfig(HubMixin):
|
||||
prefetch_factor: int = 4
|
||||
persistent_workers: bool = True
|
||||
steps: int = 100_000
|
||||
# Run policy in the simulation environment every N steps to measure reward/success (0 = disabled).
|
||||
env_eval_freq: int = 20_000
|
||||
eval_freq: int = 20_000
|
||||
log_freq: int = 200
|
||||
# Compute eval loss on held-out episodes every N steps (0 = disabled). Requires eval_split > 0.
|
||||
eval_steps: int = 0
|
||||
# Cap on total eval samples, split uniformly across tasks (0 = use all held-out data).
|
||||
max_eval_samples: int = 0
|
||||
tolerance_s: float = 1e-4
|
||||
save_checkpoint: bool = True
|
||||
# Checkpoint is saved every `save_freq` training iterations and after the last training step.
|
||||
@@ -120,13 +113,6 @@ class TrainPipelineConfig(HubMixin):
|
||||
wandb: WandBConfig = field(default_factory=WandBConfig)
|
||||
peft: PeftConfig | None = None
|
||||
|
||||
# Where to run training (local default, or an HF Jobs flavor). See JobConfig.
|
||||
job: JobConfig = field(default_factory=JobConfig)
|
||||
# Push each saved checkpoint to the Hub (policy.repo_id) as it is written, not
|
||||
# just the final model (useful to monitor progress mid-run). Optional; the
|
||||
# final model is pushed regardless. Works the same locally and remotely.
|
||||
save_checkpoint_to_hub: bool = False
|
||||
|
||||
# Sample weighting configuration (e.g., for RA-BC training)
|
||||
sample_weighting: SampleWeightingConfig | None = None
|
||||
|
||||
@@ -146,17 +132,10 @@ class TrainPipelineConfig(HubMixin):
|
||||
return self.reward_model # type: ignore[return-value]
|
||||
return self.policy # type: ignore[return-value]
|
||||
|
||||
def _resolve_pretrained_from_cli(self) -> None:
|
||||
"""Resolve the pretrained source passed on the CLI into a loaded config.
|
||||
|
||||
The pretrained paths (`--policy.path`, `--reward_model.path`) and
|
||||
`--config_path` are only recoverable by re-reading the CLI args: draccus
|
||||
has already consumed them by the time `validate()` runs, so they are not
|
||||
reflected on `self`. Exactly one source applies, in priority order:
|
||||
reward-model path, policy path, then resume.
|
||||
"""
|
||||
reward_model_path = parser.get_path_arg("reward_model")
|
||||
def validate(self) -> None:
|
||||
# HACK: We parse again the cli args here to get the pretrained paths if there was some.
|
||||
policy_path = parser.get_path_arg("policy")
|
||||
reward_model_path = parser.get_path_arg("reward_model")
|
||||
|
||||
if reward_model_path:
|
||||
cli_overrides = parser.get_cli_overrides("reward_model")
|
||||
@@ -165,54 +144,31 @@ class TrainPipelineConfig(HubMixin):
|
||||
)
|
||||
self.reward_model.pretrained_path = str(Path(reward_model_path))
|
||||
elif policy_path:
|
||||
overrides = parser.get_yaml_overrides("policy") + (parser.get_cli_overrides("policy") or [])
|
||||
self.policy = PreTrainedConfig.from_pretrained(policy_path, cli_overrides=overrides)
|
||||
yaml_overrides = parser.get_yaml_overrides("policy")
|
||||
cli_overrides = parser.get_cli_overrides("policy") or []
|
||||
self.policy = PreTrainedConfig.from_pretrained(
|
||||
policy_path, cli_overrides=yaml_overrides + cli_overrides
|
||||
)
|
||||
self.policy.pretrained_path = Path(policy_path)
|
||||
elif self.resume:
|
||||
self._resolve_resume_checkpoint()
|
||||
config_path = parser.parse_arg("config_path")
|
||||
if not config_path:
|
||||
raise ValueError(
|
||||
f"A config_path is expected when resuming a run. Please specify path to {TRAIN_CONFIG_NAME}"
|
||||
)
|
||||
|
||||
def _resolve_resume_checkpoint(self) -> None:
|
||||
"""Point the trainable config at the checkpoint named by `--config_path`.
|
||||
if not Path(config_path).resolve().exists():
|
||||
raise NotADirectoryError(
|
||||
f"{config_path=} is expected to be a local path. "
|
||||
"Resuming from the hub is not supported for now."
|
||||
)
|
||||
|
||||
`config_path` is either a local path (to a checkpoint's train_config.json or its
|
||||
pretrained_model/ dir) or a Hub repo id. For a Hub repo, the latest checkpoint is downloaded
|
||||
into a fresh local run dir and resumed from there. The download is skipped when dispatching to
|
||||
an HF Job (`job.is_remote`): the pod performs it when it runs the resume locally, and
|
||||
`submit_to_hf` resolves the source repo for the remote command.
|
||||
"""
|
||||
config_path = parser.parse_arg("config_path")
|
||||
if not config_path:
|
||||
raise ValueError(
|
||||
f"A config_path is expected when resuming a run. Please specify path to {TRAIN_CONFIG_NAME}"
|
||||
)
|
||||
|
||||
if Path(config_path).resolve().exists():
|
||||
policy_dir = Path(config_path).parent
|
||||
if self.policy is not None:
|
||||
self.policy.pretrained_path = policy_dir
|
||||
if self.reward_model is not None:
|
||||
self.reward_model.pretrained_path = str(policy_dir)
|
||||
self.checkpoint_path = policy_dir.parent
|
||||
elif self.job.is_remote:
|
||||
return
|
||||
else:
|
||||
from lerobot.common.train_utils import resolve_resume_checkpoint
|
||||
|
||||
# `self.output_dir` was loaded from the checkpoint's config and points at the original
|
||||
# run's (now-absent) local dir. Resume into a fresh local dir instead, unless the user
|
||||
# passed --output_dir explicitly.
|
||||
cli_output_dir = parser.parse_arg("output_dir")
|
||||
if cli_output_dir:
|
||||
self.output_dir = Path(cli_output_dir)
|
||||
else:
|
||||
now = dt.datetime.now()
|
||||
self.output_dir = Path("outputs/train") / f"{now:%Y-%m-%d}/{now:%H-%M-%S}_resume"
|
||||
self.checkpoint_path = resolve_resume_checkpoint(config_path, self.output_dir)
|
||||
policy_dir = self.checkpoint_path / PRETRAINED_MODEL_DIR
|
||||
|
||||
if self.policy is not None:
|
||||
self.policy.pretrained_path = policy_dir
|
||||
if self.reward_model is not None:
|
||||
self.reward_model.pretrained_path = str(policy_dir)
|
||||
|
||||
def validate(self) -> None:
|
||||
self._resolve_pretrained_from_cli()
|
||||
|
||||
if self.policy is None and self.reward_model is None:
|
||||
raise ValueError(
|
||||
@@ -252,22 +208,9 @@ class TrainPipelineConfig(HubMixin):
|
||||
self.optimizer = active_cfg.get_optimizer_preset()
|
||||
self.scheduler = active_cfg.get_scheduler_preset()
|
||||
|
||||
if self.eval_steps > 0 and self.dataset.eval_split == 0.0:
|
||||
raise ValueError("eval_steps > 0 requires dataset.eval_split > 0.0 to hold out eval data.")
|
||||
|
||||
# Remote runs auto-generate the repo_id in submit_to_hf (the policy may only be
|
||||
# resolved here, from --policy.path), so don't demand it up front for them.
|
||||
if (
|
||||
hasattr(active_cfg, "push_to_hub")
|
||||
and active_cfg.push_to_hub
|
||||
and not active_cfg.repo_id
|
||||
and not self.job.is_remote
|
||||
):
|
||||
if hasattr(active_cfg, "push_to_hub") and active_cfg.push_to_hub and not active_cfg.repo_id:
|
||||
raise ValueError("'repo_id' argument missing. Please specify it to push the model to the hub.")
|
||||
|
||||
if self.save_checkpoint_to_hub and not (self.policy is not None and self.policy.repo_id):
|
||||
raise ValueError("save_checkpoint_to_hub requires --policy.repo_id.")
|
||||
|
||||
@classmethod
|
||||
def __get_path_fields__(cls) -> list[str]:
|
||||
"""Keys for draccus pretrained-path loading."""
|
||||
@@ -304,30 +247,22 @@ class TrainPipelineConfig(HubMixin):
|
||||
elif Path(model_id).is_file():
|
||||
config_file = model_id
|
||||
else:
|
||||
dl_kwargs = {
|
||||
"repo_id": model_id,
|
||||
"revision": revision,
|
||||
"cache_dir": cache_dir,
|
||||
"force_download": force_download,
|
||||
"proxies": proxies,
|
||||
"resume_download": resume_download,
|
||||
"token": token,
|
||||
"local_files_only": local_files_only,
|
||||
}
|
||||
try:
|
||||
config_file = hf_hub_download(filename=TRAIN_CONFIG_NAME, **dl_kwargs)
|
||||
except HfHubHTTPError as e:
|
||||
# No root train_config.json: this is a repo of periodic checkpoints from an
|
||||
# interrupted run. Fall back to the latest checkpoint's config so the run can be
|
||||
# resumed straight from the repo with `--config_path=<repo>`.
|
||||
latest = find_latest_hub_checkpoint(model_id, token=token, revision=revision)
|
||||
if latest is None:
|
||||
raise FileNotFoundError(
|
||||
f"{TRAIN_CONFIG_NAME} not found on the HuggingFace Hub in {model_id}"
|
||||
) from e
|
||||
config_file = hf_hub_download(
|
||||
filename=f"{latest}/{PRETRAINED_MODEL_DIR}/{TRAIN_CONFIG_NAME}", **dl_kwargs
|
||||
repo_id=model_id,
|
||||
filename=TRAIN_CONFIG_NAME,
|
||||
revision=revision,
|
||||
cache_dir=cache_dir,
|
||||
force_download=force_download,
|
||||
proxies=proxies,
|
||||
resume_download=resume_download,
|
||||
token=token,
|
||||
local_files_only=local_files_only,
|
||||
)
|
||||
except HfHubHTTPError as e:
|
||||
raise FileNotFoundError(
|
||||
f"{TRAIN_CONFIG_NAME} not found on the HuggingFace Hub in {model_id}"
|
||||
) from e
|
||||
|
||||
cli_args = kwargs.pop("cli_args", [])
|
||||
# Legacy RA-BC migration only applies to framework-saved checkpoints (always JSON).
|
||||
|
||||
+36
-123
@@ -20,7 +20,7 @@ from __future__ import annotations
|
||||
|
||||
import logging
|
||||
from dataclasses import dataclass, field
|
||||
from typing import Any, ClassVar, Self
|
||||
from typing import Any
|
||||
|
||||
from lerobot.utils.import_utils import require_package
|
||||
|
||||
@@ -40,6 +40,7 @@ VALID_VIDEO_CODECS: frozenset[str] = frozenset({"h264", "hevc", "libsvtav1", "au
|
||||
# Aliases for legacy video codec names.
|
||||
VIDEO_CODECS_ALIASES: dict[str, str] = {"av1": "libsvtav1"}
|
||||
|
||||
|
||||
LIBSVTAV1_DEFAULT_PRESET: int = 12
|
||||
|
||||
# Keys persisted under ``features[*]["info"]`` as ``video.<name>`` (from :class:`VideoEncoderConfig`).
|
||||
@@ -51,45 +52,40 @@ VIDEO_ENCODER_INFO_KEYS: frozenset[str] = frozenset(
|
||||
f"video.{name}" for name in VIDEO_ENCODER_INFO_FIELD_NAMES
|
||||
)
|
||||
|
||||
# Default depth quantization and encoding parameters.
|
||||
DEPTH_QUANT_BITS: int = 12
|
||||
DEPTH_QMAX: int = (1 << DEPTH_QUANT_BITS) - 1 # 4095
|
||||
|
||||
DEFAULT_DEPTH_MIN: float = 0.01
|
||||
DEFAULT_DEPTH_MAX: float = 10.0
|
||||
DEFAULT_DEPTH_SHIFT: float = 3.5
|
||||
DEFAULT_DEPTH_USE_LOG: bool = True
|
||||
DEFAULT_DEPTH_PIX_FMT: str = "gray12le"
|
||||
|
||||
DEPTH_METER_UNIT: str = "m"
|
||||
DEPTH_MILLIMETER_UNIT: str = "mm"
|
||||
DEFAULT_DEPTH_UNIT: str = DEPTH_MILLIMETER_UNIT
|
||||
|
||||
# Depth-specific tuning fields persisted under ``features[*]["info"]`` as ``video.<name>``.
|
||||
DEPTH_ENCODER_INFO_FIELD_NAMES: frozenset[str] = frozenset({"depth_min", "depth_max", "shift", "use_log"})
|
||||
|
||||
|
||||
@dataclass
|
||||
class VideoEncoderConfig:
|
||||
"""Video encoder configuration."""
|
||||
"""Video encoder configuration.
|
||||
|
||||
vcodec: str = "libsvtav1" # Video codec name. "auto" picks a hardware codec if available, else libsvtav1.
|
||||
pix_fmt: str = "yuv420p" # Pixel format (e.g. yuv420p).
|
||||
g: int | None = 2 # GOP size (keyframe interval).
|
||||
crf: int | float | None = 30 # Quality level. Lower means better quality and larger files.
|
||||
preset: int | str | None = None # Speed/quality preset. Accepted values are codec-specific.
|
||||
fast_decode: int = 0 # Fast-decode tuning. Accepted values are codec-specific, 0 disables it.
|
||||
Attributes:
|
||||
vcodec: Video encoder name. ``"auto"`` is resolved during
|
||||
construction (HW encoder if available, else ``libsvtav1``).
|
||||
pix_fmt: Pixel format (e.g. ``"yuv420p"``).
|
||||
g: GOP size (keyframe interval).
|
||||
crf: Quality level — mapped to the native quality parameter of the
|
||||
codec (``crf`` for software, ``qp`` for NVENC/VAAPI,
|
||||
``q:v`` for VideoToolbox, ``global_quality`` for QSV).
|
||||
preset: Speed/quality preset. Accepted type is per-codec.
|
||||
fast_decode: Fast-decode tuning. For ``libsvtav1`` this is a level (0-2)
|
||||
embedded in ``svtav1-params``. For ``h264`` and ``hevc`` non-zero values
|
||||
set ``tune=fastdecode``. Ignored for other codecs.
|
||||
video_backend: Python to be used for encoding. Only ``"pyav"``
|
||||
is currently supported.
|
||||
extra_options: Free-form dictionary of additional video encoder options
|
||||
(e.g. ``{"tune": "film", "profile:v": "high", "bf": 2}``).
|
||||
"""
|
||||
|
||||
vcodec: str = "libsvtav1" # TODO(CarolinePascal): rename to codec ?
|
||||
pix_fmt: str = "yuv420p"
|
||||
g: int | None = 2
|
||||
crf: int | float | None = 30
|
||||
preset: int | str | None = None
|
||||
fast_decode: int = 0
|
||||
# TODO(CarolinePascal): add torchcodec support + find a way to unify the
|
||||
# two backends (encoding and decoding).
|
||||
video_backend: str = "pyav" # Encoding backend. Only "pyav" is currently supported.
|
||||
# Extra codec options merged last, e.g. {"tune": "film"}.
|
||||
video_backend: str = "pyav"
|
||||
extra_options: dict[str, Any] = field(default_factory=dict)
|
||||
|
||||
# Source-data channel count this encoder is expected to handle. ``None``
|
||||
# disables the pix_fmt channel-count check; concrete subclasses set it
|
||||
# (3 for RGB, 1 for depth, etc.).
|
||||
_DEFAULT_CHANNELS: ClassVar[int | None] = None
|
||||
|
||||
def __post_init__(self) -> None:
|
||||
self.resolve_vcodec()
|
||||
# Empty-constructor ergonomics: ``VideoEncoderConfig()`` must "just work".
|
||||
@@ -98,9 +94,9 @@ class VideoEncoderConfig:
|
||||
self.validate()
|
||||
|
||||
@classmethod
|
||||
def _kwargs_from_video_info(cls, video_info: dict | None) -> dict[str, Any]:
|
||||
"""Parse the ``video.*`` keys of a feature ``info`` block into
|
||||
constructor kwargs.
|
||||
def from_video_info(cls, video_info: dict | None) -> VideoEncoderConfig:
|
||||
"""Reconstruct a :class:`VideoEncoderConfig` from a video feature's ``info`` block.
|
||||
Missing or ``None`` values fall back to the class defaults.
|
||||
"""
|
||||
video_info = video_info or {}
|
||||
kwargs: dict[str, Any] = {}
|
||||
@@ -119,15 +115,7 @@ class VideoEncoderConfig:
|
||||
continue
|
||||
kwargs[field_name] = value
|
||||
|
||||
return kwargs
|
||||
|
||||
@classmethod
|
||||
def from_video_info(cls, video_info: dict | None) -> Self:
|
||||
"""Reconstruct an encoder config from a video feature's ``info`` block.
|
||||
|
||||
Missing or ``None`` values fall back to the class defaults.
|
||||
"""
|
||||
return cls(**cls._kwargs_from_video_info(video_info))
|
||||
return cls(**kwargs)
|
||||
|
||||
def detect_available_encoders(self, encoders: list[str] | str) -> list[str]:
|
||||
"""Return the subset of available encoders based on the specified video backend.
|
||||
@@ -150,9 +138,7 @@ class VideoEncoderConfig:
|
||||
require_package("av", extra="dataset")
|
||||
from lerobot.datasets import check_video_encoder_parameters_pyav
|
||||
|
||||
check_video_encoder_parameters_pyav(
|
||||
self.vcodec, self.pix_fmt, self.get_codec_options(), channels=self._DEFAULT_CHANNELS
|
||||
)
|
||||
check_video_encoder_parameters_pyav(self.vcodec, self.pix_fmt, self.get_codec_options())
|
||||
|
||||
def resolve_vcodec(self) -> None:
|
||||
"""Check ``vcodec`` and, when it is ``"auto"``, pick a concrete encoder.
|
||||
@@ -244,79 +230,6 @@ class VideoEncoderConfig:
|
||||
return opts
|
||||
|
||||
|
||||
@dataclass
|
||||
class RGBEncoderConfig(VideoEncoderConfig):
|
||||
"""Encoder configuration for RGB camera streams.
|
||||
|
||||
Identical to :class:`VideoEncoderConfig` but declares the 3-channel
|
||||
source-data layout so ``pix_fmt`` is validated against RGB inputs.
|
||||
"""
|
||||
|
||||
_DEFAULT_CHANNELS: ClassVar[int] = 3
|
||||
|
||||
|
||||
def rgb_encoder_defaults() -> RGBEncoderConfig:
|
||||
"""Return a :class:`RGBEncoderConfig` with RGB-camera defaults."""
|
||||
return RGBEncoderConfig()
|
||||
|
||||
|
||||
@dataclass
|
||||
class DepthEncoderConfig(VideoEncoderConfig):
|
||||
"""Encoder configuration for depth-map streams.
|
||||
|
||||
Inherits the full :class:`VideoEncoderConfig` surface (codec, GOP, CRF,
|
||||
preset, ``extra_options``…) and adds the parameters of the depth quantizer.
|
||||
Defaults flip ``vcodec`` to ``"hevc"`` (Main 12 profile) and ``pix_fmt`` to
|
||||
``"gray12le"``.
|
||||
"""
|
||||
|
||||
vcodec: str = "hevc" # Video codec name. Defaults to HEVC Main 12 (a 12-bit-capable codec).
|
||||
pix_fmt: str = "gray12le" # Pixel format. Defaults to 12-bit grayscale.
|
||||
extra_options: dict[str, Any] = field(default_factory=lambda: {"x265-params": "lossless=1"})
|
||||
|
||||
depth_min: float = DEFAULT_DEPTH_MIN # Minimum depth in meters, mapped to the lowest quantum.
|
||||
depth_max: float = DEFAULT_DEPTH_MAX # Maximum depth in meters, mapped to the highest quantum.
|
||||
shift: float = DEFAULT_DEPTH_SHIFT # Pre-log offset in meters for numerical stability near zero.
|
||||
use_log: bool = DEFAULT_DEPTH_USE_LOG # Use logarithmic quantization (True) or linear (False).
|
||||
|
||||
_DEFAULT_CHANNELS: ClassVar[int] = 1
|
||||
|
||||
@classmethod
|
||||
def _kwargs_from_video_info(cls, video_info: dict | None) -> dict[str, Any]:
|
||||
"""Layer the depth-specific tuning (``depth_min`` / ``depth_max`` /
|
||||
``shift`` / ``use_log``) on top of the base parser. Missing keys
|
||||
fall back to the class defaults.
|
||||
"""
|
||||
kwargs = super()._kwargs_from_video_info(video_info)
|
||||
video_info = video_info or {}
|
||||
for name in DEPTH_ENCODER_INFO_FIELD_NAMES:
|
||||
value = video_info.get(f"video.{name}")
|
||||
if value is not None:
|
||||
kwargs[name] = value
|
||||
return kwargs
|
||||
|
||||
|
||||
def depth_encoder_defaults() -> DepthEncoderConfig:
|
||||
"""Return a :class:`DepthEncoderConfig` with depth-camera defaults."""
|
||||
return DepthEncoderConfig()
|
||||
|
||||
|
||||
def encoder_config_from_video_info(video_info: dict | None) -> VideoEncoderConfig:
|
||||
"""Build the appropriate encoder config from a feature's ``info`` block.
|
||||
|
||||
Dispatches to :class:`DepthEncoderConfig` when the dict marks the feature
|
||||
as a depth map and to :class:`RGBEncoderConfig`
|
||||
otherwise.
|
||||
|
||||
Args:
|
||||
video_info: A feature's ``info`` dict as persisted in ``info.json``,
|
||||
or ``None`` (treated as an empty dict).
|
||||
|
||||
Returns:
|
||||
A :class:`DepthEncoderConfig` for depth features, otherwise a
|
||||
:class:`RGBEncoderConfig`.
|
||||
"""
|
||||
video_info = video_info or {}
|
||||
is_depth = bool(video_info.get("is_depth_map") or video_info.get("video.is_depth_map"))
|
||||
cls: type[VideoEncoderConfig] = DepthEncoderConfig if is_depth else RGBEncoderConfig
|
||||
return cls.from_video_info(video_info)
|
||||
def camera_encoder_defaults() -> VideoEncoderConfig:
|
||||
"""Return a :class:`VideoEncoderConfig` with RGB-camera defaults."""
|
||||
return VideoEncoderConfig()
|
||||
|
||||
@@ -35,7 +35,7 @@ from .dataset_tools import (
|
||||
remove_feature,
|
||||
split_dataset,
|
||||
)
|
||||
from .factory import make_dataset, make_train_eval_datasets, resolve_delta_timestamps
|
||||
from .factory import make_dataset, resolve_delta_timestamps
|
||||
from .image_writer import safe_stop_image_writer
|
||||
from .io_utils import load_episodes, write_stats
|
||||
from .language import (
|
||||
@@ -50,7 +50,7 @@ from .lerobot_dataset import LeRobotDataset
|
||||
from .multi_dataset import MultiLeRobotDataset
|
||||
from .pipeline_features import aggregate_pipeline_dataset_features, create_initial_features
|
||||
from .pyav_utils import check_video_encoder_parameters_pyav, detect_available_encoders_pyav
|
||||
from .sampler import EpisodeAwareSampler, compute_sampler_state
|
||||
from .sampler import EpisodeAwareSampler
|
||||
from .streaming_dataset import StreamingLeRobotDataset
|
||||
from .utils import DEFAULT_EPISODES_PATH, create_lerobot_dataset_card
|
||||
from .video_utils import VideoEncodingManager
|
||||
@@ -82,14 +82,12 @@ __all__ = [
|
||||
"aggregate_stats",
|
||||
"convert_image_to_video_dataset",
|
||||
"create_initial_features",
|
||||
"compute_sampler_state",
|
||||
"create_lerobot_dataset_card",
|
||||
"column_for_style",
|
||||
"delete_episodes",
|
||||
"get_feature_stats",
|
||||
"load_episodes",
|
||||
"make_dataset",
|
||||
"make_train_eval_datasets",
|
||||
"merge_datasets",
|
||||
"modify_features",
|
||||
"modify_tasks",
|
||||
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user