chore(rollout): remove speculative action order fix

Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>

AGENTS.md

@@ -1,5 +1,7 @@
 This file provides guidance to AI agents when working with code in this repository.
 
+> **User-facing help → [`AGENT_GUIDE.md`](./AGENT_GUIDE.md)** (SO-101 setup, recording, picking a policy, training duration, eval — with copy-pasteable commands).
+
 ## Project Overview
 
 LeRobot is a PyTorch-based library for real-world robotics, providing datasets, pretrained policies, and tools for training, evaluation, data collection, and robot control. It integrates with Hugging Face Hub for model/dataset sharing.

AGENT_GUIDE.md

@@ -0,0 +1,410 @@
+# AGENT_GUIDE.md — LeRobot Helper for AI Agents & Users
+
+This file is a practical, copy-paste-friendly companion for any AI agent (Cursor, Claude, ChatGPT, Codex, etc.) helping a user work with LeRobot. It complements [`AGENTS.md`](./AGENTS.md) (dev/contributor context) with **user-facing guidance**: how to start, what to train, how long, how to record, and how to calibrate an SO-101.
+
+---
+
+## 1. Start here — ask the user first (MANDATORY)
+
+Before suggesting any command, an agent MUST ask the user at least these questions and wait for answers:
+
+1. **What's your goal?** (e.g. "teach my SO-101 to fold a cloth", "train a policy on an existing HF dataset", "contribute a PR", "understand the codebase")
+2. **What hardware do you have?**
+   - Robot: none / SO-100 / SO-101 / Koch / LeKiwi / Reachy / other
+   - Teleop: leader arm / phone / keyboard / gamepad / none
+   - Cameras: how many, resolution, fixed or moving?
+3. **What machine will you train on?**
+   - GPU model + VRAM (e.g. "laptop 3060 6 GB", "RTX 4090 24 GB", "A100 80 GB", "CPU only")
+   - OS: macOS / Linux / Windows
+4. **Skill level & time budget?** First time, some ML, experienced? Hours, days, a weekend?
+5. **Do you already have a dataset?** Yes (HF repo id?) / no / want to record one
+6. **How can I help right now?** (pick one concrete next step)
+
+Only after you have answers, propose a concrete path. If something is ambiguous, ask again rather than guessing. Bias toward **the simplest thing that works** for the user's hardware and goal.
+
+---
+
+## 2. LeRobot in 60 seconds
+
+LeRobot = **datasets + policies + envs + robot control**, unified by a small set of strong abstractions.
+
+- **`LeRobotDataset`** — episode-aware dataset (video or images + actions + state), loadable from the Hub or disk.
+- **Policies** (`ACT`, `Diffusion`, `SmolVLA`, `π0`, `π0.5`, `Wall-X`, `X-VLA`, `VQ-BeT`, `TD-MPC`, …) — all inherit `PreTrainedPolicy` and can be pushed/pulled from the Hub.
+- **Processors** — small composable transforms between dataset → policy → robot.
+- **Envs** (sim) and **Robots** (real) — same action/observation contract so code swaps cleanly.
+- **CLI** — `lerobot-record`, `lerobot-train`, `lerobot-eval`, `lerobot-teleoperate`, `lerobot-calibrate`, `lerobot-find-port`, `lerobot-setup-motors`, `lerobot-replay`.
+
+See [`AGENTS.md`](./AGENTS.md) for repo architecture.
+
+---
+
+## 3. Quickstart paths (pick one)
+
+### Path A — "I have an SO-101 and want my first trained policy"
+
+Go to §4 (SO-101 end-to-end), then §5 (data tips), then §6 (pick a policy — likely **ACT**), then §7 (how long), then §8 (eval).
+
+### Path B — "No hardware, I want to train on an existing dataset"
+
+Skip §4. Pick a policy in §6, pick a duration in §7, then run `lerobot-train` per §4.9 with a Hub `--dataset.repo_id` and an `--env.type` for eval. Finish with §8.
+
+### Path C — "I just want to understand the codebase"
+
+Read §2 above, then `AGENTS.md` "Architecture", then open `src/lerobot/policies/act/` and `src/lerobot/datasets/lerobot_dataset.py` as canonical examples.
+
+---
+
+## 4. SO-101 end-to-end cheat-sheet
+
+Full details in [`docs/source/so101.mdx`](./docs/source/so101.mdx) and [`docs/source/il_robots.mdx`](./docs/source/il_robots.mdx). Minimum commands in order. Confirm arms are assembled + powered before issuing.
+
+**4.1 Install**
+
+```bash
+pip install 'lerobot[feetech]'              # SO-100/SO-101 motor stack
+# pip install 'lerobot[all]'                # everything
+# pip install 'lerobot[aloha,pusht]'        # specific features
+# pip install 'lerobot[smolvla]'            # add SmolVLA deps
+git lfs install && git lfs pull
+hf auth login                               # required to push datasets/policies
+```
+
+Contributors can alternatively use `uv sync --locked --extra feetech` (see `AGENTS.md`).
+
+**4.2 Find USB ports** — run once per arm, unplug when prompted.
+
+```bash
+lerobot-find-port
+```
+
+macOS: `/dev/tty.usbmodem...`; Linux: `/dev/ttyACM0` (may need `sudo chmod 666 /dev/ttyACM0`).
+
+**4.3 Setup motor IDs & baudrate** (one-time, per arm)
+
+```bash
+lerobot-setup-motors --robot.type=so101_follower --robot.port=<FOLLOWER_PORT>
+lerobot-setup-motors --teleop.type=so101_leader  --teleop.port=<LEADER_PORT>
+```
+
+**4.4 Calibrate** — center all joints, press Enter, sweep each joint through its full range. The `id` is the calibration key — reuse it everywhere.
+
+```bash
+lerobot-calibrate --robot.type=so101_follower --robot.port=<FOLLOWER_PORT> --robot.id=my_follower
+lerobot-calibrate --teleop.type=so101_leader  --teleop.port=<LEADER_PORT>   --teleop.id=my_leader
+```
+
+**4.5 Teleoperate** (sanity check, no recording)
+
+```bash
+lerobot-teleoperate \
+  --robot.type=so101_follower --robot.port=<FOLLOWER_PORT> --robot.id=my_follower \
+  --teleop.type=so101_leader  --teleop.port=<LEADER_PORT>  --teleop.id=my_leader \
+  --robot.cameras="{ front: {type: opencv, index_or_path: 0, width: 640, height: 480, fps: 30}}" \
+  --display_data=true
+```
+
+> **Feetech timeout / comms error on SO-100 / SO-101?** Before touching software, check the **red motor LEDs** on the daisy chain.
+>
+> - **All steady red, gripper → base chain** → wiring OK.
+> - **One or more motors dark / chain stops mid-way** → wiring issue: reseat the 3-pin cables, check the controller-board power supply, and make sure each motor is fully clicked in.
+> - **LEDs blinking** → the motor is in an **error state**: usually overload (forcing a joint past its limit) **or wrong power supply voltage**. SO-100 / SO-101 ship in two variants — a **5 V / 7.4 V** build and a **12 V** build — they are NOT interchangeable. Using a 12 V PSU on a 5 V / 7.4 V arm (or vice-versa) will trip this error; confirm your motor variant before powering up.
+>
+> Most "timeout" errors are physical, not code.
+
+**4.6 Record a dataset** — keys: **→** next, **←** redo, **ESC** finish & upload.
+
+```bash
+HF_USER=$(NO_COLOR=1 hf auth whoami | awk -F': *' 'NR==1 {print $2}')
+
+lerobot-record \
+  --robot.type=so101_follower --robot.port=<FOLLOWER_PORT> --robot.id=my_follower \
+  --teleop.type=so101_leader  --teleop.port=<LEADER_PORT>  --teleop.id=my_leader \
+  --robot.cameras="{ front: {type: opencv, index_or_path: 0, width: 640, height: 480, fps: 30}}" \
+  --dataset.repo_id=${HF_USER}/my_task \
+  --dataset.single_task="<describe the task in one sentence>" \
+  --dataset.num_episodes=50 \
+  --dataset.episode_time_s=30 \
+  --dataset.reset_time_s=10 \
+  --display_data=true
+```
+
+**4.7 Visualize** — **always** do this before training. Look for missing frames, camera blur, unreachable targets, inconsistent object positions.
+After upload: https://huggingface.co/spaces/lerobot/visualize_dataset → paste `${HF_USER}/my_task`. Works for **any LeRobot-formatted Hub dataset** — use it to scout other datasets, inspect episode quality, or debug your own data before retraining.
+
+**4.8 Replay an episode** (sanity check)
+
+```bash
+lerobot-replay --robot.type=so101_follower --robot.port=<FOLLOWER_PORT> --robot.id=my_follower \
+  --dataset.repo_id=${HF_USER}/my_task --dataset.episode=0
+```
+
+**4.9 Train** (default: ACT — fastest, lowest memory). Apple silicon: `--policy.device=mps`. See §6/§7 for policy and duration.
+
+```bash
+lerobot-train \
+  --dataset.repo_id=${HF_USER}/my_task \
+  --policy.type=act \
+  --policy.device=cuda \
+  --output_dir=outputs/train/act_my_task \
+  --job_name=act_my_task \
+  --batch_size=8 \
+  --wandb.enable=true \
+  --policy.repo_id=${HF_USER}/act_my_task
+```
+
+**4.10 Evaluate on the real robot** — compare success rate to a teleoperated baseline.
+
+```bash
+lerobot-record \
+  --robot.type=so101_follower --robot.port=<FOLLOWER_PORT> --robot.id=my_follower \
+  --robot.cameras="{ front: {type: opencv, index_or_path: 0, width: 640, height: 480, fps: 30}}" \
+  --dataset.repo_id=${HF_USER}/eval_my_task \
+  --dataset.single_task="<same task description as training>" \
+  --dataset.num_episodes=10 \
+  --policy.path=${HF_USER}/act_my_task
+```
+
+---
+
+## 5. Data collection tips (beginner → reliable policy)
+
+Good data beats clever models. Adopt these defaults and deviate only with evidence.
+
+### 5.1 Setup & ergonomics
+
+- **Fix the rig and cameras** before touching the software. If the rig vibrates or the operator gets frustrated, fix that first — more bad data won't help.
+- **Lighting matters more than resolution.** Diffuse, consistent light. Avoid moving shadows.
+- **"Can you do the task from the camera view alone?"** If no, your cameras are wrong. Fix before recording.
+- Enable **action interpolation** for rollouts when available for smoother trajectories.
+
+### 5.2 Practice before you record
+
+- Do 5–10 demos without recording. Build a deliberate, repeatable strategy.
+- Hesitant or inconsistent demos teach the model hesitation.
+
+### 5.3 Quality over speed
+
+Deliberate, high-quality execution beats fast sloppy runs. Optimize for speed only **after** strategy is dialed in — never trade quality for it.
+
+### 5.4 Consistency within and across episodes
+
+Same grasp, approach vector, and timing. Coherent strategies are much easier to learn than wildly varying movements.
+
+### 5.5 Start small, then extend (the golden rule)
+
+- **First 50 episodes = constrained version** of the task: one object, fixed position, fixed camera setup, one operator.
+- Train a quick ACT model. See what fails.
+- **Then add diversity** along one axis at a time: more positions → more lighting → more objects → more operators.
+- Don't try to collect the "perfect dataset" on day one. Iterate.
+
+### 5.6 Policy choice for beginners
+
+- **Laptop / first time / want results fast → ACT.** Works surprisingly well, trains fast even on a laptop GPU.
+- **Bigger GPU / language-conditioned / multi-task → SmolVLA.** Unfreezing the vision encoder (see §7) is a big win here.
+- Defer π0 / π0.5 / Wall-X / X-VLA until you have a proven ACT baseline and a 20+ GB GPU.
+
+### 5.7 Recommended defaults for your first task
+
+| Setting          | Value                                                                                                                                                 |
+| ---------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------- |
+| Episodes         | **50** to start, scale to 100–300 after first training                                                                                                |
+| Episode length   | 20–45 s (shorter is fine for grasp/place)                                                                                                             |
+| Reset time       | 10 s                                                                                                                                                  |
+| FPS              | 30                                                                                                                                                    |
+| Cameras          | **2 cameras recommended**: 1 fixed front + 1 wrist. Multi-view often outperforms single-view. A single fixed camera also works to keep things simple. |
+| Task description | Short, specific, action-phrased sentence                                                                                                              |
+
+### 5.8 Troubleshooting signal
+
+- Policy fails at one specific stage → record 10–20 more episodes **targeting that stage**.
+- Policy flaps / oscillates → likely inconsistent demos, or need more training; re-record worst episodes (use **←** to redo).
+- Policy ignores the object → camera framing or lighting issue, not a model issue.
+
+See also: [What makes a good dataset](https://huggingface.co/blog/lerobot-datasets#what-makes-a-good-dataset).
+
+---
+
+## 6. Which policy should I train?
+
+Match the policy to the user's **GPU memory** and **time budget**. Numbers below come from an internal profiling run (one training update per policy). They are **indicative only** — see caveats.
+
+### 6.1 Profiling snapshot (indicative)
+
+All policies typically train for **5–10 epochs** (see §7).
+
+| Policy      | Batch | Update (ms) | Peak GPU mem (GB) | Best for                                                                                         |
+| ----------- | ----: | ----------: | ----------------: | ------------------------------------------------------------------------------------------------ |
+| `act`       |     4 |    **83.9** |          **0.94** | First-time users, laptops, single-task. Fast and reliable.                                       |
+| `diffusion` |     4 |       168.6 |              4.94 | Multi-modal action distributions; needs mid-range GPU.                                           |
+| `smolvla`   |     1 |       357.8 |              3.93 | Language-conditioned, multi-task, small VLA. **Unfreeze vision encoder for big gains** (see §7). |
+| `xvla`      |     1 |       731.6 |             15.52 | Large VLA, multi-task.                                                                           |
+| `wall_x`    |     1 |       716.5 |             15.95 | Large VLA with world-model objective.                                                            |
+| `pi0`       |     1 |       940.3 |             15.50 | Strong large VLA baseline (Physical Intelligence).                                               |
+| `pi05`      |     1 |      1055.8 |             16.35 | Newer π policy; similar footprint to `pi0`.                                                      |
+
+**Critical caveats:**
+
+- **Optimizer:** measured with **SGD**. LeRobot's default is **AdamW**, which keeps extra optimizer state → **peak memory will be noticeably higher** with the default, especially for `pi0`, `pi05`, `wall_x`, `xvla`.
+- **Batch size:** the large policies were profiled at batch 1. In practice use a **larger batch** for stable training (see §7.4). Memory scales roughly linearly with batch.
+
+### 6.2 Decision rules
+
+- **< 8 GB VRAM (laptop, 3060, M-series Mac):** → `act`. Maybe `diffusion` if you have ~6–8 GB free.
+- **12–16 GB VRAM (4070/4080, A4000):** → `smolvla` with defaults, or `act`/`diffusion` with larger batch. `pi0`/`pi05`/`wall_x`/`xvla` feasible only with small batch + gradient accumulation.
+- **24+ GB VRAM (3090/4090/A5000):** → any policy. Prefer `smolvla` (unfrozen) for multi-task; `act` for single-task grasp-and-place (still often the best ROI). Could experiment with `pi0` or `pi05` or `xvla`
+- **80 GB (A100/H100):** → any, with healthy batch. `pi05`, `xvla`, `wall_x` become comfortable.
+- **CPU only:** → don't train here. Use Google Colab (see [`docs/source/notebooks.mdx`](./docs/source/notebooks.mdx)) or a rented GPU.
+
+---
+
+## 7. How long should I train?
+
+Robotics imitation learning usually converges in a **few epochs over the dataset**, not hundreds of thousands of raw steps. Think **epochs first**, then translate to steps.
+
+### 7.1 Rule of thumb
+
+- **Typical total: 5–10 epochs.** Start at 5, eval, then decide if more helps.
+- Very small datasets (< 30 episodes) may want slightly more epochs — but first, **collect more data**.
+- VLAs with a pretrained vision backbone typically need **fewer** epochs than training from scratch.
+
+### 7.2 Steps ↔ epochs conversion
+
+```
+total_frames     = sum of frames over all episodes      # e.g. 50 eps × 30 fps × 30 s ≈ 45,000
+steps_per_epoch  = ceil(total_frames / batch_size)
+total_steps      = epochs × steps_per_epoch
+```
+
+Examples for `--batch_size=8`:
+
+| Dataset size            |  Frames | Steps / epoch | 5 epochs | 10 epochs |
+| ----------------------- | ------: | ------------: | -------: | --------: |
+| 50 eps × 30 s @ 30 fps  |  45,000 |        ~5,625 |      28k |       56k |
+| 100 eps × 30 s @ 30 fps |  90,000 |       ~11,250 |      56k |      113k |
+| 300 eps × 30 s @ 30 fps | 270,000 |       ~33,750 |     169k |      338k |
+
+Pass the resulting total with `--steps=<N>`; eval at intermediate checkpoints (`outputs/train/.../checkpoints/`).
+
+### 7.3 Per-policy starting points (single-task, ~50 episodes)
+
+| Policy         | Batch | Steps (first run) | Notes                                                             |
+| -------------- | ----: | ----------------: | ----------------------------------------------------------------- |
+| `act`          |  8–16 |           30k–80k | Usually converges under 50k for single-task.                      |
+| `diffusion`    |  8–16 |          80k–150k | Benefits from longer training than ACT.                           |
+| `smolvla`      |   4–8 |           30k–80k | Pretrained VLM → converges fast.                                  |
+| `pi0` / `pi05` |   1–4 |           30k–80k | Memory-bound; use gradient accumulation for effective batch ≥ 16! |
+
+### 7.4 Batch size guidance
+
+- **Bigger batch is preferable** for stable gradients on teleop data.
+- If GPU memory is the bottleneck, use **gradient accumulation** to raise _effective_ batch without raising peak memory.
+- Scale **learning rate** gently with batch; most LeRobot defaults work fine for a 2–4× batch change.
+
+### 7.5 Scale LR schedule & checkpoints with `--steps`
+
+LeRobot's default schedulers (e.g. SmolVLA's cosine decay) use `scheduler_decay_steps=30_000`, which is sized for long training runs. When you shorten training (e.g. 5k–10k steps on a small dataset), **scale the scheduler down to match** — otherwise the LR stays near the peak and never decays. Same for checkpoint frequency.
+
+```bash
+lerobot-train ... \
+  --steps=5000 \
+  --policy.scheduler_decay_steps=5000 \
+  --save_freq=5000
+```
+
+Rule of thumb: set `scheduler_decay_steps ≈ steps`, and `save_freq` to whatever granularity you want for eval (e.g. every 1k–5k steps). Match `scheduler_warmup_steps` proportionally if your run is very short.
+
+### 7.6 SmolVLA: unfreeze the vision encoder for real gains
+
+SmolVLA ships with `freeze_vision_encoder=True`. Unfreezing usually **improves performance substantially** on specialized tasks, at the cost of more VRAM and slower steps. Enable with:
+
+```bash
+lerobot-train ... --policy.type=smolvla \
+  --policy.freeze_vision_encoder=false \
+  --policy.train_expert_only=false
+```
+
+### 7.7 Signals to stop / keep going
+
+- Train loss plateaus → stop, save a Hub checkpoint.
+- Train loss still dropping and you're under 10 epochs → keep going.
+
+---
+
+## 8. Evaluation & benchmarks
+
+Two flavors of evaluation:
+
+### 8.1 Real-robot eval (SO-101, etc.)
+
+Reuse `lerobot-record` with `--policy.path` to run the trained policy on-robot and save the run as an eval dataset. Convention: prefix the dataset with `eval_`.
+
+```bash
+lerobot-record \
+  --robot.type=so101_follower --robot.port=<FOLLOWER_PORT> --robot.id=my_follower \
+  --robot.cameras="{ front: {type: opencv, index_or_path: 0, width: 640, height: 480, fps: 30}}" \
+  --dataset.repo_id=${HF_USER}/eval_my_task \
+  --dataset.single_task="<same task description used during training>" \
+  --dataset.num_episodes=10 \
+  --policy.path=${HF_USER}/act_my_task
+```
+
+Report success rate across episodes. Compare to a teleoperated baseline and to an earlier checkpoint to catch regressions.
+
+### 8.2 Sim-benchmark eval
+
+For policies trained on sim datasets (PushT, Aloha, LIBERO, MetaWorld, RoboCasa, …) use `lerobot-eval` against the matching `env.type`:
+
+```bash
+lerobot-eval \
+  --policy.path=${HF_USER}/diffusion_pusht \
+  --env.type=pusht \
+  --eval.n_episodes=50 \
+  --eval.batch_size=10 \
+  --policy.device=cuda
+```
+
+- Use `--policy.path=outputs/train/.../checkpoints/<step>/pretrained_model` for local checkpoints.
+- `--eval.n_episodes` should be ≥ 50 for a stable success-rate estimate.
+- Available envs live in `src/lerobot/envs/`. See [`docs/source/libero.mdx`](./docs/source/libero.mdx), [`metaworld.mdx`](./docs/source/metaworld.mdx), [`robocasa.mdx`](./docs/source/robocasa.mdx), [`vlabench.mdx`](./docs/source/vlabench.mdx) for specific benchmarks.
+- To add a new benchmark, see [`docs/source/adding_benchmarks.mdx`](./docs/source/adding_benchmarks.mdx) and [`envhub.mdx`](./docs/source/envhub.mdx).
+
+### 8.2b Dockerfiles for benchmark eval
+
+Benchmark envs have native dependencies that are painful to install locally. The repo ships **pre-baked Dockerfiles** for each supported benchmark — use these to run `lerobot-eval` in a reproducible environment:
+
+| Benchmark   | Dockerfile                                                                             |
+| ----------- | -------------------------------------------------------------------------------------- |
+| LIBERO      | [`docker/Dockerfile.benchmark.libero`](./docker/Dockerfile.benchmark.libero)           |
+| LIBERO+     | [`docker/Dockerfile.benchmark.libero_plus`](./docker/Dockerfile.benchmark.libero_plus) |
+| MetaWorld   | [`docker/Dockerfile.benchmark.metaworld`](./docker/Dockerfile.benchmark.metaworld)     |
+| RoboCasa    | [`docker/Dockerfile.benchmark.robocasa`](./docker/Dockerfile.benchmark.robocasa)       |
+| RoboCerebra | [`docker/Dockerfile.benchmark.robocerebra`](./docker/Dockerfile.benchmark.robocerebra) |
+| RoboMME     | [`docker/Dockerfile.benchmark.robomme`](./docker/Dockerfile.benchmark.robomme)         |
+| RoboTwin    | [`docker/Dockerfile.benchmark.robotwin`](./docker/Dockerfile.benchmark.robotwin)       |
+| VLABench    | [`docker/Dockerfile.benchmark.vlabench`](./docker/Dockerfile.benchmark.vlabench)       |
+
+Build and run (adapt to your benchmark):
+
+```bash
+docker build -f docker/Dockerfile.benchmark.robomme -t lerobot-bench-robomme .
+docker run --gpus all --rm -it \
+  -v $HOME/.cache/huggingface:/root/.cache/huggingface \
+  lerobot-bench-robomme \
+  lerobot-eval --policy.path=<your_policy> --env.type=<env> --eval.n_episodes=50
+```
+
+See [`docker/README.md`](./docker/README.md) for base-image details.
+
+### 8.3 Target success rates
+
+Single-task grasp-and-place with 50 clean episodes: ACT should reach **> 70% success** on the training configuration. Less → data problem (see §5), not model problem. Expect a drop when generalizing to new positions — scale episodes or diversity to recover.
+
+---
+
+## 9. Further reading & resources
+
+- **Getting started:** [`installation.mdx`](./docs/source/installation.mdx) · [`il_robots.mdx`](./docs/source/il_robots.mdx) · [What makes a good dataset](https://huggingface.co/blog/lerobot-datasets)
+- **Per-policy docs:** browse [`docs/source/*.mdx`](./docs/source/) (policies, hardware, benchmarks, advanced training).
+- **Community:** [Discord](https://discord.com/invite/s3KuuzsPFb) · [Hub `LeRobot` tag](https://huggingface.co/datasets?other=LeRobot) · [Dataset visualizer](https://huggingface.co/spaces/lerobot/visualize_dataset)
+
+> Keep this file current. If you learn a rule that would prevent a class of user mistakes, add it here and in [`AGENTS.md`](./AGENTS.md).

docker/Dockerfile.benchmark.robotwin

@@ -56,11 +56,11 @@ RUN uv pip install --no-cache --no-build-isolation \
         "git+https://github.com/facebookresearch/pytorch3d.git@stable"
 
 # CuRobo — NVlabs motion generator; TORCH_CUDA_ARCH_LIST must be set or the
-# build aborts on an empty arch list. Pinned SHA for reproducibility.
-ARG CUROBO_SHA=ca941586c33b8482ed9c0e74d60f23efd64b516a
+# build aborts on an empty arch list. RoboTwin's own installer pins v0.7.8,
+# which still exposes the v1 API (`curobo.types.math`) that RoboTwin imports.
+ARG CUROBO_REF=v0.7.8
 RUN cd ${ROBOTWIN_ROOT}/envs \
-    && git clone https://github.com/NVlabs/curobo.git \
-    && git -C curobo checkout ${CUROBO_SHA} \
+    && git clone --branch ${CUROBO_REF} --depth 1 https://github.com/NVlabs/curobo.git \
     && cd curobo \
     && TORCH_CUDA_ARCH_LIST="7.0;7.5;8.0;8.6;8.9;9.0" \
        uv pip install -e . --no-build-isolation --no-cache
@@ -111,7 +111,23 @@ EOF
 WORKDIR ${ROBOTWIN_ROOT}
 RUN python script/update_embodiment_config_path.py
 
-ENV PYTHONPATH="${ROBOTWIN_ROOT}:${PYTHONPATH}"
+ENV PYTHONPATH="${ROBOTWIN_ROOT}"
+
+# Fail the image build early if the CuRobo package layout regresses. Importing
+# RoboTwin's planner here is too eager because CuRobo constructs CUDA-backed
+# defaults at import time, while Docker builds don't have access to an NVIDIA
+# driver.
+RUN python - <<'EOF'
+from pathlib import Path
+
+from curobo.types.math import Pose
+
+planner_src = (Path("/opt/robotwin/envs/robot/planner.py")).read_text()
+assert "from curobo.types.math import Pose as CuroboPose" in planner_src
+
+print("CuRobo import OK:", Pose.__name__)
+print("RoboTwin planner import references curobo.types.math")
+EOF
 
 # Return to the lerobot source directory (set by base image) before overlaying.
 WORKDIR /lerobot

docs/source/_toctree.yml

@@ -61,6 +61,8 @@
     title: SARM
   title: "Reward Models"
 - sections:
+  - local: inference
+    title: Policy Deployment (lerobot-rollout)
   - local: async
     title: Use Async Inference
   - local: rtc

docs/source/hil_data_collection.mdx

@@ -50,30 +50,30 @@ This process can be repeated iteratively: deploy, collect, fine-tune, repeat. Ea
 
 ### Teleoperator Requirements
 
-The `examples/hil` HIL scripts require **teleoperators with active motors** that can:
+The `lerobot-rollout --strategy.type=dagger` mode requires **teleoperators with active motors** that can:
 
 - Enable/disable torque programmatically
 - Move to target positions (to mirror the robot state when pausing)
 
-**Compatible teleoperators in the current `examples/hil` scripts:**
+**Compatible teleoperators:**
 
 - `openarm_mini` - OpenArm Mini
 - `so_leader` - SO100 / SO101 leader arm
 
 > [!IMPORTANT]
-> The provided `examples/hil` commands default to `bi_openarm_follower` + `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.
 
 ---
 
 ## Script
 
-A single script handles both synchronous and RTC-based inference. Toggle RTC with `--rtc.enabled=true`:
+Use `lerobot-rollout` with `--strategy.type=dagger` for HIL data collection. Select the inference backend with `--inference.type=sync|rtc`:
 
-| Mode                     | Flag                 | Models                |
-| ------------------------ | -------------------- | --------------------- |
-| Standard (default)       | _(no flag needed)_   | ACT, Diffusion Policy |
-| Real-Time Chunking (RTC) | `--rtc.enabled=true` | Pi0, Pi0.5, SmolVLA   |
+| Mode                     | Flag                   | Models                |
+| ------------------------ | ---------------------- | --------------------- |
+| Standard (default)       | _(no flag needed)_     | ACT, Diffusion Policy |
+| Real-Time Chunking (RTC) | `--inference.type=rtc` | Pi0, Pi0.5, SmolVLA   |
 
 ---
 
@@ -97,7 +97,7 @@ python src/lerobot/scripts/lerobot_train.py \
 **Standard inference (ACT, Diffusion Policy):**
 
 ```bash
-python examples/hil/hil_data_collection.py \
+lerobot-rollout --strategy.type=dagger \
     --robot.type=bi_openarm_follower \
     --robot.left_arm_config.port=can1 \
     --robot.left_arm_config.side=left \
@@ -108,11 +108,10 @@ python examples/hil/hil_data_collection.py \
     --teleop.port_left=/dev/ttyACM0 \
     --teleop.port_right=/dev/ttyACM1 \
     --policy.path=outputs/pretrain/checkpoints/last/pretrained_model \
-    --dataset.repo_id=your-username/hil-dataset \
+    --dataset.repo_id=your-username/rollout_hil_dataset \
     --dataset.single_task="Fold the T-shirt properly" \
     --dataset.fps=30 \
-    --dataset.episode_time_s=1000 \
-    --dataset.num_episodes=50 \
+    --strategy.num_episodes=50 \
     --interpolation_multiplier=2
 ```
 
@@ -121,11 +120,11 @@ python examples/hil/hil_data_collection.py \
 For models with high inference latency, enable RTC for smooth execution:
 
 ```bash
-python examples/hil/hil_data_collection.py \
-    --rtc.enabled=true \
-    --rtc.execution_horizon=20 \
-    --rtc.max_guidance_weight=5.0 \
-    --rtc.prefix_attention_schedule=LINEAR \
+lerobot-rollout --strategy.type=dagger \
+    --inference.type=rtc \
+    --inference.rtc.execution_horizon=20 \
+    --inference.rtc.max_guidance_weight=5.0 \
+    --inference.rtc.prefix_attention_schedule=LINEAR \
     --robot.type=bi_openarm_follower \
     --robot.left_arm_config.port=can1 \
     --robot.left_arm_config.side=left \
@@ -136,11 +135,10 @@ python examples/hil/hil_data_collection.py \
     --teleop.port_left=/dev/ttyACM0 \
     --teleop.port_right=/dev/ttyACM1 \
     --policy.path=outputs/pretrain/checkpoints/last/pretrained_model \
-    --dataset.repo_id=your-username/hil-rtc-dataset \
+    --dataset.repo_id=your-username/rollout_hil_rtc_dataset \
     --dataset.single_task="Fold the T-shirt properly" \
     --dataset.fps=30 \
-    --dataset.episode_time_s=1000 \
-    --dataset.num_episodes=50 \
+    --strategy.num_episodes=50 \
     --interpolation_multiplier=3
 ```
 
@@ -235,7 +233,7 @@ This HIL data collection approach builds on ideas from interactive imitation lea
 
 - **HG-DAgger** (Kelly et al., 2019) made this practical for robotics: a human expert monitors the robot and only intervenes when needed, rather than labeling every state. The gating between autonomous and human control is exactly the pause → takeover → return-to-policy loop used in the scripts here.
 
-- **RaC** (Hu et al., 2025) scales this loop to long-horizon tasks by explicitly decomposing interventions into **recovery** (teleoperating back to a good state) and **correction** (demonstrating the right behavior from there). This decomposition is the protocol followed by the HIL scripts in `examples/hil`.
+- **RaC** (Hu et al., 2025) scales this loop to long-horizon tasks by explicitly decomposing interventions into **recovery** (teleoperating back to a good state) and **correction** (demonstrating the right behavior from there). This decomposition is the protocol followed by the DAgger strategy in `lerobot-rollout`.
 
 - **π0.6/RECAP** (Physical Intelligence, 2025) applies the same iterative collect-and-finetune loop at scale with VLA models, showing that even large pretrained policies benefit substantially from targeted human corrections on their own failure modes. π0.6 is trained using RECAP.
 

docs/source/il_robots.mdx

@@ -509,121 +509,42 @@ hf upload ${HF_USER}/act_so101_test${CKPT} \
 
 ## Run inference and evaluate your policy
 
-You can use the `record` script from [`lerobot-record`](https://github.com/huggingface/lerobot/blob/main/src/lerobot/scripts/lerobot_record.py) with a policy checkpoint as input, to run inference and evaluate your policy. For instance, run this command or API example to run inference and record 10 evaluation episodes:
+Use `lerobot-rollout` to deploy a trained policy on your robot. You can choose different strategies depending on your needs:
 
 <hfoptions id="eval">
-<hfoption id="Command">
+<hfoption id="Base mode (no recording)">
 ```bash
-lerobot-record  \
+lerobot-rollout \
+  --strategy.type=base \
+  --policy.path=${HF_USER}/my_policy \
   --robot.type=so100_follower \
   --robot.port=/dev/ttyACM1 \
   --robot.cameras="{ up: {type: opencv, index_or_path: /dev/video10, width: 640, height: 480, fps: 30}, side: {type: intelrealsense, serial_number_or_name: 233522074606, width: 640, height: 480, fps: 30}}" \
-  --robot.id=my_awesome_follower_arm \
-  --display_data=false \
-  --dataset.repo_id=${HF_USER}/eval_so100 \
-  --dataset.single_task="Put lego brick into the transparent box" \
-  --dataset.streaming_encoding=true \
-  --dataset.encoder_threads=2 \
-  # --dataset.vcodec=auto \
-  # <- Teleop optional if you want to teleoperate in between episodes \
-  # --teleop.type=so100_leader \
-  # --teleop.port=/dev/ttyACM0 \
-  # --teleop.id=my_awesome_leader_arm \
-  --policy.path=${HF_USER}/my_policy
+  --task="Put lego brick into the transparent box" \
+  --duration=60
 ```
 </hfoption>
-<hfoption id="API example">
-
-<!-- prettier-ignore-start -->
-```python
-from lerobot.cameras.opencv import OpenCVCameraConfig
-from lerobot.datasets import LeRobotDataset
-from lerobot.utils.feature_utils import hw_to_dataset_features
-from lerobot.policies.act import ACTPolicy
-from lerobot.policies import make_pre_post_processors
-from lerobot.robots.so_follower import SO100Follower, SO100FollowerConfig
-from lerobot.scripts.lerobot_record import record_loop
-from lerobot.common.control_utils import init_keyboard_listener
-from lerobot.utils.utils import log_say
-from lerobot.utils.visualization_utils import init_rerun
-
-
-NUM_EPISODES = 5
-FPS = 30
-EPISODE_TIME_SEC = 60
-TASK_DESCRIPTION = "My task description"
-HF_MODEL_ID = "<hf_username>/<model_repo_id>"
-HF_DATASET_ID = "<hf_username>/<eval_dataset_repo_id>"
-
-# Create the robot configuration
-camera_config = {"front": OpenCVCameraConfig(index_or_path=0, width=640, height=480, fps=FPS)}
-robot_config = SO100FollowerConfig(
-    port="/dev/tty.usbmodem58760434471", id="my_awesome_follower_arm", cameras=camera_config
-)
-
-# Initialize the robot
-robot = SO100Follower(robot_config)
-
-# Initialize the policy
-policy = ACTPolicy.from_pretrained(HF_MODEL_ID)
-
-# Configure the dataset features
-action_features = hw_to_dataset_features(robot.action_features, "action")
-obs_features = hw_to_dataset_features(robot.observation_features, "observation")
-dataset_features = {**action_features, **obs_features}
-
-# Create the dataset
-dataset = LeRobotDataset.create(
-    repo_id=HF_DATASET_ID,
-    fps=FPS,
-    features=dataset_features,
-    robot_type=robot.name,
-    use_videos=True,
-    image_writer_threads=4,
-)
-
-# Initialize the keyboard listener and rerun visualization
-_, events = init_keyboard_listener()
-init_rerun(session_name="recording")
-
-# Connect the robot
-robot.connect()
-
-preprocessor, postprocessor = make_pre_post_processors(
-    policy_cfg=policy,
-    pretrained_path=HF_MODEL_ID,
-    dataset_stats=dataset.meta.stats,
-)
-
-for episode_idx in range(NUM_EPISODES):
-    log_say(f"Running inference, recording eval episode {episode_idx + 1} of {NUM_EPISODES}")
-
-    # Run the policy inference loop
-    record_loop(
-        robot=robot,
-        events=events,
-        fps=FPS,
-        policy=policy,
-        preprocessor=preprocessor,
-        postprocessor=postprocessor,
-        dataset=dataset,
-        control_time_s=EPISODE_TIME_SEC,
-        single_task=TASK_DESCRIPTION,
-        display_data=True,
-    )
-
-    dataset.save_episode()
-
-# Clean up
-robot.disconnect()
-dataset.push_to_hub()
+<hfoption id="Sentry mode (with recording)">
+```bash
+lerobot-rollout \
+  --strategy.type=sentry \
+  --strategy.upload_every_n_episodes=5 \
+  --policy.path=${HF_USER}/my_policy \
+  --robot.type=so100_follower \
+  --robot.port=/dev/ttyACM1 \
+  --robot.cameras="{ up: {type: opencv, index_or_path: /dev/video10, width: 640, height: 480, fps: 30}, side: {type: intelrealsense, serial_number_or_name: 233522074606, width: 640, height: 480, fps: 30}}" \
+  --dataset.repo_id=${HF_USER}/eval_so100 \
+  --dataset.single_task="Put lego brick into the transparent box" \
+  --duration=600
 ```
-<!-- prettier-ignore-end -->
-
 </hfoption>
 </hfoptions>
 
-As you can see, it's almost the same command as previously used to record your training dataset. Two things changed:
+The `--strategy.type` flag selects the execution mode:
 
-1. There is an additional `--control.policy.path` argument which indicates the path to your policy checkpoint with (e.g. `outputs/train/eval_act_so101_test/checkpoints/last/pretrained_model`). You can also use the model repository if you uploaded a model checkpoint to the hub (e.g. `${HF_USER}/act_so101_test`).
-2. The name of dataset begins by `eval` to reflect that you are running inference (e.g. `${HF_USER}/eval_act_so101_test`).
+- `base`: Autonomous rollout with no data recording (useful for quick evaluation)
+- `sentry`: Continuous recording with auto-upload (useful for large-scale evaluation)
+- `highlight`: Ring buffer recording with keystroke save (useful for capturing interesting events)
+- `dagger`: Human-in-the-loop data collection (see [HIL Data Collection](./hil_data_collection))
+
+All strategies support `--inference.type=rtc` for smooth execution with slow VLA models (Pi0, Pi0.5, SmolVLA).

docs/source/inference.mdx

@@ -0,0 +1,261 @@
+# Policy Deployment (lerobot-rollout)
+
+`lerobot-rollout` is the single CLI for deploying trained policies on real robots. It supports multiple execution strategies and inference backends, from quick evaluation to continuous recording and human-in-the-loop data collection.
+
+## Quick Start
+
+No extra dependencies are needed beyond your robot and policy extras.
+
+```bash
+lerobot-rollout \
+    --strategy.type=base \
+    --policy.path=lerobot/act_koch_real \
+    --robot.type=koch_follower \
+    --robot.port=/dev/ttyACM0 \
+    --task="pick up cube" \
+    --duration=30
+```
+
+This runs the policy for 30 seconds with no recording.
+
+---
+
+## Strategies
+
+Select a strategy with `--strategy.type=<name>`. Each strategy defines a different control loop with its own recording and interaction semantics.
+
+### Base (`--strategy.type=base`)
+
+Autonomous policy execution with no data recording. Use this for quick evaluation, demos, or when you only need to observe the robot.
+
+```bash
+lerobot-rollout \
+    --strategy.type=base \
+    --policy.path=${HF_USER}/my_policy \
+    --robot.type=so100_follower \
+    --robot.port=/dev/ttyACM0 \
+    --robot.cameras="{ front: {type: opencv, index_or_path: 0, width: 640, height: 480, fps: 30}}" \
+    --task="Put lego brick into the box" \
+    --duration=60
+```
+
+| Flag             | Description                                            |
+| ---------------- | ------------------------------------------------------ |
+| `--duration`     | Run time in seconds (0 = infinite)                     |
+| `--task`         | Task description passed to the policy                  |
+| `--display_data` | Stream observations/actions to Rerun for visualization |
+
+### Sentry (`--strategy.type=sentry`)
+
+Continuous autonomous recording with periodic upload to the Hugging Face Hub. Episode boundaries are auto-computed from camera resolution and FPS so each saved episode produces a complete video file, keeping uploads efficient.
+
+Policy state (hidden state, RTC queue) persists across episode boundaries: the robot does not reset between episodes.
+
+```bash
+lerobot-rollout \
+    --strategy.type=sentry \
+    --strategy.upload_every_n_episodes=5 \
+    --policy.path=${HF_USER}/my_policy \
+    --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=${HF_USER}/rollout_eval_data \
+    --dataset.single_task="Put lego brick into the box" \
+    --duration=3600
+```
+
+| Flag                                   | Description                                                 |
+| -------------------------------------- | ----------------------------------------------------------- |
+| `--strategy.upload_every_n_episodes`   | Push to Hub every N episodes (default: 5)                   |
+| `--strategy.target_video_file_size_mb` | Target video file size for episode rotation (default: auto) |
+| `--dataset.repo_id`                    | **Required.** Hub repository for the recorded dataset       |
+| `--dataset.push_to_hub`                | Whether to push to Hub on teardown (default: true)          |
+
+### Highlight (`--strategy.type=highlight`)
+
+Autonomous rollout with on-demand recording via a memory-bounded ring buffer. The robot runs continuously while the buffer captures the last N seconds of telemetry. Press the save key to flush the buffer and start live recording; press it again to save the episode.
+
+```bash
+lerobot-rollout \
+    --strategy.type=highlight \
+    --strategy.ring_buffer_seconds=30 \
+    --strategy.save_key=s \
+    --strategy.push_key=h \
+    --policy.path=${HF_USER}/my_policy \
+    --robot.type=koch_follower \
+    --robot.port=/dev/ttyACM0 \
+    --dataset.repo_id=${HF_USER}/rollout_highlight_data \
+    --dataset.single_task="Pick up the red cube"
+```
+
+**Keyboard controls:**
+
+| Key                | Action                                                   |
+| ------------------ | -------------------------------------------------------- |
+| `s` (configurable) | Start recording (flushes buffer) / stop and save episode |
+| `h` (configurable) | Push dataset to Hub                                      |
+| `ESC`              | Stop the session                                         |
+
+| Flag                                   | Description                                    |
+| -------------------------------------- | ---------------------------------------------- |
+| `--strategy.ring_buffer_seconds`       | Duration of buffered telemetry (default: 30)   |
+| `--strategy.ring_buffer_max_memory_mb` | Memory cap for the ring buffer (default: 2048) |
+| `--strategy.save_key`                  | Key to toggle recording (default: `s`)         |
+| `--strategy.push_key`                  | Key to push to Hub (default: `h`)              |
+
+### DAgger (`--strategy.type=dagger`)
+
+Human-in-the-loop data collection. Alternates between autonomous policy execution and human intervention via a teleoperator. Intervention frames are tagged with `intervention=True`. Requires a teleoperator (`--teleop.type`).
+
+See the [Human-In-the-Loop Data Collection](./hil_data_collection) guide for a detailed walkthrough.
+
+**Corrections-only mode** (default): Only human correction windows are recorded. Each correction becomes one episode.
+
+```bash
+lerobot-rollout \
+    --strategy.type=dagger \
+    --strategy.num_episodes=20 \
+    --policy.path=outputs/pretrain/checkpoints/last/pretrained_model \
+    --robot.type=bi_openarm_follower \
+    --teleop.type=openarm_mini \
+    --dataset.repo_id=${HF_USER}/rollout_hil_data \
+    --dataset.single_task="Fold the T-shirt"
+```
+
+**Continuous recording mode** (`--strategy.record_autonomous=true`): Both autonomous and correction frames are recorded with time-based episode rotation (same as Sentry).
+
+```bash
+lerobot-rollout \
+    --strategy.type=dagger \
+    --strategy.record_autonomous=true \
+    --strategy.num_episodes=50 \
+    --policy.path=${HF_USER}/my_policy \
+    --robot.type=so100_follower \
+    --robot.port=/dev/ttyACM0 \
+    --teleop.type=so101_leader \
+    --teleop.port=/dev/ttyACM1 \
+    --dataset.repo_id=${HF_USER}/rollout_dagger_data \
+    --dataset.single_task="Grasp the block"
+```
+
+**Keyboard controls** (default input device):
+
+| Key     | Action                                      |
+| ------- | ------------------------------------------- |
+| `Space` | Pause / resume policy execution             |
+| `Tab`   | Start / stop human correction               |
+| `Enter` | Push dataset to Hub (corrections-only mode) |
+| `ESC`   | Stop the session                            |
+
+Foot pedal input is also supported via `--strategy.input_device=pedal`. Configure pedal codes with `--strategy.pedal.*` flags.
+
+| Flag                                 | Description                                             |
+| ------------------------------------ | ------------------------------------------------------- |
+| `--strategy.num_episodes`            | Number of correction episodes to record (default: 10)   |
+| `--strategy.record_autonomous`       | Record autonomous frames too (default: false)           |
+| `--strategy.upload_every_n_episodes` | Push to Hub every N episodes (default: 5)               |
+| `--strategy.input_device`            | Input device: `keyboard` or `pedal` (default: keyboard) |
+| `--teleop.type`                      | **Required.** Teleoperator type                         |
+
+---
+
+## Inference Backends
+
+Select a backend with `--inference.type=<name>`. All strategies work with both backends.
+
+### Sync (default)
+
+One policy call per control tick. The main loop blocks until the action is computed.
+
+Works with all policies. No extra flags needed.
+
+### Real-Time Chunking (`--inference.type=rtc`)
+
+A background thread produces action chunks asynchronously. The main control loop polls for the next ready action while the policy computes the next chunk in parallel.
+
+Use RTC with large, slow VLA models (Pi0, Pi0.5, SmolVLA) for smooth, continuous motion despite high inference latency.
+
+```bash
+lerobot-rollout \
+    --strategy.type=base \
+    --inference.type=rtc \
+    --inference.rtc.execution_horizon=10 \
+    --inference.rtc.max_guidance_weight=10.0 \
+    --policy.path=${HF_USER}/pi0_policy \
+    --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 \
+    --device=cuda
+```
+
+| Flag                                        | Description                                                    |
+| ------------------------------------------- | -------------------------------------------------------------- |
+| `--inference.rtc.execution_horizon`         | Steps to blend with previous chunk (default: varies by policy) |
+| `--inference.rtc.max_guidance_weight`       | Consistency enforcement strength (default: varies by policy)   |
+| `--inference.rtc.prefix_attention_schedule` | Blend schedule: `LINEAR`, `EXP`, `ONES`, `ZEROS`               |
+| `--inference.queue_threshold`               | Max queue size before backpressure (default: 30)               |
+
+See the [Real-Time Chunking](./rtc) guide for details on tuning RTC parameters.
+
+---
+
+## Common Flags
+
+| Flag                              | Description                                                       | Default |
+| --------------------------------- | ----------------------------------------------------------------- | ------- |
+| `--policy.path`                   | **Required.** HF Hub model ID or local checkpoint path            | --      |
+| `--robot.type`                    | **Required.** Robot type (e.g. `so100_follower`, `koch_follower`) | --      |
+| `--robot.port`                    | Serial port for the robot                                         | --      |
+| `--robot.cameras`                 | Camera configuration (JSON dict)                                  | --      |
+| `--fps`                           | Control loop frequency                                            | 30      |
+| `--duration`                      | Run time in seconds (0 = infinite)                                | 0       |
+| `--device`                        | Torch device (`cpu`, `cuda`, `mps`)                               | auto    |
+| `--task`                          | Task description (used when no dataset is provided)               | --      |
+| `--display_data`                  | Stream telemetry to Rerun visualization                           | false   |
+| `--display_ip` / `--display_port` | Remote Rerun server address                                       | --      |
+| `--interpolation_multiplier`      | Action interpolation factor                                       | 1       |
+| `--use_torch_compile`             | Enable `torch.compile` for inference                              | false   |
+| `--resume`                        | Resume a previous recording session                               | false   |
+| `--play_sounds`                   | Vocal synthesis for events                                        | true    |
+
+---
+
+## Programmatic Usage
+
+For custom deployments (e.g. with kinematics processors), use the rollout module API directly:
+
+```python
+from lerobot.rollout import BaseStrategyConfig, RolloutConfig, build_rollout_context
+from lerobot.rollout.inference import SyncInferenceConfig
+from lerobot.rollout.strategies import BaseStrategy
+from lerobot.utils.process import ProcessSignalHandler
+
+cfg = RolloutConfig(
+    robot=my_robot_config,
+    policy=my_policy_config,
+    strategy=BaseStrategyConfig(),
+    inference=SyncInferenceConfig(),
+    fps=30,
+    duration=60,
+    task="my task",
+)
+
+signal_handler = ProcessSignalHandler(use_threads=True)
+ctx = build_rollout_context(
+    cfg,
+    signal_handler.shutdown_event,
+    robot_action_processor=my_custom_action_processor,       # optional
+    robot_observation_processor=my_custom_obs_processor,     # optional
+)
+
+strategy = BaseStrategy(cfg.strategy)
+try:
+    strategy.setup(ctx)
+    strategy.run(ctx)
+finally:
+    strategy.teardown(ctx)
+```
+
+See `examples/so100_to_so100_EE/rollout.py` and `examples/phone_to_so100/rollout.py` for full examples with kinematics processors.

docs/source/rename_map.mdx

@@ -61,17 +61,6 @@ lerobot-eval \
   --rename_map='{"observation.images.image": "observation.images.base_0_rgb", "observation.images.image2": "observation.images.left_wrist_0_rgb"}'
 ```
 
-### Recording
-
-`lerobot-record` also supports rename maps, nested under the dataset config:
-
-```bash
-lerobot-record \ # When running inference
-  --policy.path="<user>/smolVLA_finetuned" \
-  ... \
-  --dataset.rename_map='{"observation.images.glove2": "observation.images.image"}'
-```
-
 ## Alternative: edit the policy config directly
 
 If you always use the same dataset or environment, you can **edit the policy's `config.json`** so its observation keys match your data source. Then no rename map is needed.
@@ -105,10 +94,10 @@ XVLA-base has three visual inputs and `empty_cameras=0` by default. Your dataset
 
 ## Quick reference
 
-| Goal                                      | What to do                                                                  |
-| ----------------------------------------- | --------------------------------------------------------------------------- |
-| Dataset keys ≠ policy keys                | `--rename_map='{"dataset_key": "policy_key", ...}'`                         |
-| Env keys ≠ policy keys (eval)             | `--rename_map='{"env_key": "policy_key", ...}'`                             |
-| Recording with different keys (inference) | `--dataset.rename_map='{"source_key": "policy_key", ...}'`.                 |
-| Fewer cameras than policy expects         | `--policy.empty_cameras=N` (supported by PI0, PI05, PI0Fast, SmolVLA, XVLA) |
-| Avoid passing a rename map                | Edit the policy's `config.json` so its keys match your data source          |
+| Goal                                    | What to do                                                                  |
+| --------------------------------------- | --------------------------------------------------------------------------- |
+| Dataset keys ≠ policy keys              | `--rename_map='{"dataset_key": "policy_key", ...}'`                         |
+| Env keys ≠ policy keys (eval)           | `--rename_map='{"env_key": "policy_key", ...}'`                             |
+| Rollout with different keys (inference) | `--rename_map='{"source_key": "policy_key", ...}'`.                         |
+| Fewer cameras than policy expects       | `--policy.empty_cameras=N` (supported by PI0, PI05, PI0Fast, SmolVLA, XVLA) |
+| Avoid passing a rename map              | Edit the policy's `config.json` so its keys match your data source          |

docs/source/rtc.mdx

@@ -34,7 +34,7 @@ pip install -e ".[smolvla]"
 
 ### Using RTC with Pi0
 
-You can find a complete reference implementation in [eval_with_real_robot.py](examples/rtc/eval_with_real_robot.py).
+You can use `lerobot-rollout --strategy.type=base --inference.type=rtc` for RTC deployment on real robots.
 The snippet below provides a simplified pseudo-example of how RTC operates with Pi0 in your pipeline:
 
 ```python
@@ -137,8 +137,12 @@ The script generates a visualization of the denoising process, comparing standar
 ## Testing RTC with a Real Robot
 
 ```bash
-python examples/rtc/eval_with_real_robot.py \
+lerobot-rollout \
+    --strategy.type=base \
     --policy.path=${HF_USERNAME}/policy_repo_id \
+    --inference.type=rtc \
+    --inference.rtc.execution_horizon=10 \
+    --inference.rtc.max_guidance_weight=10.0 \
     --robot.type=so100_follower \
     --robot.port=/dev/tty.usbmodem58FA0834591 \
     --robot.cameras="{ gripper: {type: opencv, index_or_path: 1, width: 640, height: 480, fps: 30}, front: {type: opencv, index_or_path: 0, width: 640, height: 480, fps: 30}}" \
@@ -178,7 +182,7 @@ visualizer = RTCDebugVisualizer()
 # ... create plots
 ```
 
-See `examples/rtc/eval_dataset.py` for a complete example of visualization.
+See `examples/rtc/eval_dataset.py` for a complete example of offline RTC visualization.
 
 ## References
 

docs/source/unitree_g1.mdx

@@ -274,7 +274,8 @@ python src/lerobot/scripts/lerobot_train.py \
 Once trained, we recommend deploying policies using inference-time RTC:
 
 ```bash
-python examples/rtc/eval_with_real_robot.py \
+lerobot-rollout \
+  --strategy.type=base \
   --policy.path=your-username/your-repo-id \
   --policy.device=cuda \
   --robot.type=unitree_g1 \
@@ -284,7 +285,7 @@ python examples/rtc/eval_with_real_robot.py \
   --task="task_description" \
   --duration=1000 \
   --fps=30 \
-  --rtc.enabled=true
+  --inference.type=rtc
 ```
 
 ---

examples/hil/hil_utils.py

@@ -1,226 +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.
-
-"""Shared utilities for Human-in-the-Loop data collection scripts."""
-
-import logging
-import time
-from dataclasses import dataclass, field
-from pathlib import Path
-
-from lerobot.common.control_utils import is_headless
-from lerobot.processor import (
-    IdentityProcessorStep,
-    RobotAction,
-    RobotObservation,
-    RobotProcessorPipeline,
-    observation_to_transition,
-    robot_action_observation_to_transition,
-    transition_to_observation,
-    transition_to_robot_action,
-)
-from lerobot.robots import Robot
-from lerobot.teleoperators import Teleoperator
-from lerobot.utils.robot_utils import precise_sleep
-
-logger = logging.getLogger(__name__)
-
-
-@dataclass
-class HILDatasetConfig:
-    repo_id: str
-    single_task: str
-    root: str | Path | None = None
-    fps: int = 30
-    episode_time_s: float = 120
-    num_episodes: int = 50
-    video: bool = True
-    push_to_hub: bool = True
-    private: bool = False
-    tags: list[str] | None = None
-    num_image_writer_processes: int = 0
-    num_image_writer_threads_per_camera: int = 4
-    video_encoding_batch_size: int = 1
-    vcodec: str = "auto"
-    streaming_encoding: bool = True
-    encoder_queue_maxsize: int = 30
-    encoder_threads: int | None = None
-    rename_map: dict[str, str] = field(default_factory=dict)
-
-
-def teleop_has_motor_control(teleop: Teleoperator) -> bool:
-    """Check if teleoperator has motor control capabilities."""
-    return all(hasattr(teleop, attr) for attr in ("enable_torque", "disable_torque", "write_goal_positions"))
-
-
-def teleop_disable_torque(teleop: Teleoperator) -> None:
-    """Disable teleop torque if supported."""
-    if hasattr(teleop, "disable_torque"):
-        teleop.disable_torque()
-
-
-def teleop_enable_torque(teleop: Teleoperator) -> None:
-    """Enable teleop torque if supported."""
-    if hasattr(teleop, "enable_torque"):
-        teleop.enable_torque()
-
-
-def teleop_smooth_move_to(teleop: Teleoperator, target_pos: dict, duration_s: float = 2.0, fps: int = 50):
-    """Smoothly move teleop to target position if motor control is available."""
-    if not teleop_has_motor_control(teleop):
-        logger.warning("Teleop does not support motor control - cannot mirror robot position")
-        return
-
-    teleop_enable_torque(teleop)
-    current = teleop.get_action()
-    steps = max(int(duration_s * fps), 1)
-
-    for step in range(steps + 1):
-        t = step / steps
-        interp = {}
-        for k in current:
-            if k in target_pos:
-                interp[k] = current[k] * (1 - t) + target_pos[k] * t
-            else:
-                interp[k] = current[k]
-        teleop.write_goal_positions(interp)
-        time.sleep(1 / fps)
-
-
-def init_keyboard_listener():
-    """Initialize keyboard listener with HIL controls."""
-    events = {
-        "exit_early": False,
-        "rerecord_episode": False,
-        "stop_recording": False,
-        "policy_paused": False,
-        "correction_active": False,
-        "resume_policy": False,
-        "in_reset": False,
-        "start_next_episode": False,
-    }
-
-    if is_headless():
-        logger.warning("Headless environment - keyboard controls unavailable")
-        return None, events
-
-    from pynput import keyboard
-
-    def on_press(key):
-        try:
-            if events["in_reset"]:
-                if key in [keyboard.Key.space, keyboard.Key.right]:
-                    logger.info("[HIL] Starting next episode...")
-                    events["start_next_episode"] = True
-                elif hasattr(key, "char") and key.char == "c":
-                    events["start_next_episode"] = True
-                elif key == keyboard.Key.esc:
-                    logger.info("[HIL] ESC - Stop recording, pushing to hub...")
-                    events["stop_recording"] = True
-                    events["start_next_episode"] = True
-            else:
-                if key == keyboard.Key.space:
-                    if not events["policy_paused"] and not events["correction_active"]:
-                        logger.info("[HIL] PAUSED - Press 'c' to take control or 'p' to resume policy")
-                        events["policy_paused"] = True
-                elif hasattr(key, "char") and key.char == "c":
-                    if events["policy_paused"] and not events["correction_active"]:
-                        logger.info("[HIL] Taking control...")
-                        events["start_next_episode"] = True
-                elif hasattr(key, "char") and key.char == "p":
-                    if events["policy_paused"] or events["correction_active"]:
-                        logger.info("[HIL] Resuming policy...")
-                        events["resume_policy"] = True
-                elif key == keyboard.Key.right:
-                    logger.info("[HIL] End episode")
-                    events["exit_early"] = True
-                elif key == keyboard.Key.left:
-                    logger.info("[HIL] Re-record episode")
-                    events["rerecord_episode"] = True
-                    events["exit_early"] = True
-                elif key == keyboard.Key.esc:
-                    logger.info("[HIL] ESC - Stop recording...")
-                    events["stop_recording"] = True
-                    events["exit_early"] = True
-        except Exception as e:
-            logger.info(f"Key error: {e}")
-
-    listener = keyboard.Listener(on_press=on_press)
-    listener.start()
-    return listener, events
-
-
-def make_identity_processors():
-    """Create identity processors for recording."""
-    teleop_proc = RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction](
-        steps=[IdentityProcessorStep()],
-        to_transition=robot_action_observation_to_transition,
-        to_output=transition_to_robot_action,
-    )
-    obs_proc = RobotProcessorPipeline[RobotObservation, RobotObservation](
-        steps=[IdentityProcessorStep()],
-        to_transition=observation_to_transition,
-        to_output=transition_to_observation,
-    )
-    return teleop_proc, obs_proc
-
-
-def reset_loop(robot: Robot, teleop: Teleoperator, events: dict, fps: int):
-    """Reset period where human repositions environment."""
-    logger.info("[HIL] RESET")
-
-    events["in_reset"] = True
-    events["start_next_episode"] = False
-
-    obs = robot.get_observation()
-    robot_pos = {k: v for k, v in obs.items() if k.endswith(".pos") and k in robot.observation_features}
-    teleop_smooth_move_to(teleop, robot_pos, duration_s=2.0, fps=50)
-
-    logger.info("Press any key to enable teleoperation")
-    while not events["start_next_episode"] and not events["stop_recording"]:
-        precise_sleep(0.05)
-
-    if events["stop_recording"]:
-        return
-
-    events["start_next_episode"] = False
-    teleop_disable_torque(teleop)
-    logger.info("Teleop enabled - press any key to start episode")
-
-    while not events["start_next_episode"] and not events["stop_recording"]:
-        loop_start = time.perf_counter()
-        action = teleop.get_action()
-        robot.send_action(action)
-        precise_sleep(1 / fps - (time.perf_counter() - loop_start))
-
-    events["in_reset"] = False
-    events["start_next_episode"] = False
-    events["exit_early"] = False
-    events["policy_paused"] = False
-    events["correction_active"] = False
-    events["resume_policy"] = False
-
-
-def print_controls(rtc: bool = False):
-    """Print control instructions."""
-    mode = "Human-in-the-Loop Data Collection" + (" (RTC)" if rtc else "")
-    logger.info(
-        "%s\n  Controls:\n"
-        "    SPACE  - Pause policy\n"
-        "    c      - Take control\n"
-        "    p      - Resume policy after pause/correction\n"
-        "    →      - End episode\n"
-        "    ESC    - Stop and push to hub",
-        mode,
-    )

examples/lekiwi/evaluate.py

@@ -14,17 +14,21 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from lerobot.common.control_utils import init_keyboard_listener
+import logging
+import time
+
+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
+from lerobot.policies.utils import make_robot_action
 from lerobot.processor import make_default_processors
 from lerobot.robots.lekiwi import LeKiwiClient, LeKiwiClientConfig
-from lerobot.scripts.lerobot_record import record_loop
 from lerobot.utils.constants import ACTION, OBS_STR
-from lerobot.utils.feature_utils import hw_to_dataset_features
+from lerobot.utils.feature_utils import build_dataset_frame, hw_to_dataset_features
+from lerobot.utils.robot_utils import precise_sleep
 from lerobot.utils.utils import log_say
-from lerobot.utils.visualization_utils import init_rerun
+from lerobot.utils.visualization_utils import init_rerun, log_rerun_data
 
 NUM_EPISODES = 2
 FPS = 30
@@ -35,6 +39,9 @@ HF_DATASET_ID = "<hf_username>/<eval_dataset_repo_id>"
 
 
 def main():
+    # NOTE: For production policy deployment, use `lerobot-rollout` CLI instead.
+    # This script provides a self-contained example for educational purposes.
+
     # Create the robot configuration & robot
     robot_config = LeKiwiClientConfig(remote_ip="172.18.134.136", id="lekiwi")
 
@@ -83,43 +90,67 @@ def main():
             raise ValueError("Robot is not connected!")
 
         print("Starting evaluate loop...")
+        control_interval = 1 / FPS
         recorded_episodes = 0
         while recorded_episodes < NUM_EPISODES and not events["stop_recording"]:
             log_say(f"Running inference, recording eval episode {recorded_episodes} of {NUM_EPISODES}")
 
-            # Main record loop
-            record_loop(
-                robot=robot,
-                events=events,
-                fps=FPS,
-                policy=policy,
-                preprocessor=preprocessor,  # Pass the pre and post policy processors
-                postprocessor=postprocessor,
-                dataset=dataset,
-                control_time_s=EPISODE_TIME_SEC,
-                single_task=TASK_DESCRIPTION,
-                display_data=True,
-                teleop_action_processor=teleop_action_processor,
-                robot_action_processor=robot_action_processor,
-                robot_observation_processor=robot_observation_processor,
-            )
+            # Inline evaluation loop: predict actions and send to robot
+            timestamp = 0
+            start_episode_t = time.perf_counter()
+            while timestamp < EPISODE_TIME_SEC:
+                start_loop_t = time.perf_counter()
+
+                if events["exit_early"]:
+                    events["exit_early"] = False
+                    break
+
+                # Get robot observation
+                obs = robot.get_observation()
+                obs_processed = robot_observation_processor(obs)
+                observation_frame = build_dataset_frame(dataset.features, obs_processed, prefix=OBS_STR)
+
+                # Predict action using the policy
+                action_tensor = predict_action(
+                    observation=observation_frame,
+                    policy=policy,
+                    device=policy.config.device,
+                    preprocessor=preprocessor,
+                    postprocessor=postprocessor,
+                    use_amp=policy.config.device.type == "cuda",
+                    task=TASK_DESCRIPTION,
+                    robot_type=robot.name,
+                )
+
+                # Convert policy output to robot action dict
+                action_values = make_robot_action(action_tensor, dataset.features)
+
+                # Process and send action to robot
+                robot_action_to_send = robot_action_processor((action_values, obs))
+                robot.send_action(robot_action_to_send)
+
+                # Write to dataset
+                action_frame = build_dataset_frame(dataset.features, action_values, prefix=ACTION)
+                frame = {**observation_frame, **action_frame, "task": TASK_DESCRIPTION}
+                dataset.add_frame(frame)
+
+                log_rerun_data(observation=obs_processed, action=action_values)
+
+                dt_s = time.perf_counter() - start_loop_t
+                sleep_time_s = control_interval - dt_s
+                if sleep_time_s < 0:
+                    logging.warning(
+                        f"Evaluate loop is running slower ({1 / dt_s:.1f} Hz) than the target FPS ({FPS} Hz)."
+                    )
+                precise_sleep(max(sleep_time_s, 0.0))
+                timestamp = time.perf_counter() - start_episode_t
 
             # Reset the environment if not stopping or re-recording
             if not events["stop_recording"] and (
                 (recorded_episodes < NUM_EPISODES - 1) or events["rerecord_episode"]
             ):
                 log_say("Reset the environment")
-                record_loop(
-                    robot=robot,
-                    events=events,
-                    fps=FPS,
-                    control_time_s=EPISODE_TIME_SEC,
-                    single_task=TASK_DESCRIPTION,
-                    display_data=True,
-                    teleop_action_processor=teleop_action_processor,
-                    robot_action_processor=robot_action_processor,
-                    robot_observation_processor=robot_observation_processor,
-                )
+                log_say("Waiting for environment reset, press right arrow key when ready...")
 
             if events["rerecord_episode"]:
                 log_say("Re-record episode")

examples/lekiwi/record.py

@@ -45,9 +45,6 @@ def main():
     leader_arm = SO100Leader(leader_arm_config)
     keyboard = KeyboardTeleop(keyboard_config)
 
-    # TODO(Steven): Update this example to use pipelines
-    teleop_action_processor, robot_action_processor, robot_observation_processor = make_default_processors()
-
     # Configure the dataset features
     action_features = hw_to_dataset_features(robot.action_features, ACTION)
     obs_features = hw_to_dataset_features(robot.observation_features, OBS_STR)
@@ -77,6 +74,10 @@ def main():
         if not robot.is_connected or not leader_arm.is_connected or not keyboard.is_connected:
             raise ValueError("Robot or teleop is not connected!")
 
+        teleop_action_processor, robot_action_processor, robot_observation_processor = (
+            make_default_processors()
+        )
+
         print("Starting record loop...")
         recorded_episodes = 0
         while recorded_episodes < NUM_EPISODES and not events["stop_recording"]:
@@ -87,14 +88,14 @@ def main():
                 robot=robot,
                 events=events,
                 fps=FPS,
+                teleop_action_processor=teleop_action_processor,
+                robot_action_processor=robot_action_processor,
+                robot_observation_processor=robot_observation_processor,
                 dataset=dataset,
                 teleop=[leader_arm, keyboard],
                 control_time_s=EPISODE_TIME_SEC,
                 single_task=TASK_DESCRIPTION,
                 display_data=True,
-                teleop_action_processor=teleop_action_processor,
-                robot_action_processor=robot_action_processor,
-                robot_observation_processor=robot_observation_processor,
             )
 
             # Reset the environment if not stopping or re-recording
@@ -106,13 +107,13 @@ def main():
                     robot=robot,
                     events=events,
                     fps=FPS,
+                    teleop_action_processor=teleop_action_processor,
+                    robot_action_processor=robot_action_processor,
+                    robot_observation_processor=robot_observation_processor,
                     teleop=[leader_arm, keyboard],
                     control_time_s=RESET_TIME_SEC,
                     single_task=TASK_DESCRIPTION,
                     display_data=True,
-                    teleop_action_processor=teleop_action_processor,
-                    robot_action_processor=robot_action_processor,
-                    robot_observation_processor=robot_observation_processor,
                 )
 
             if events["rerecord_episode"]:

examples/lekiwi/rollout.py

@@ -0,0 +1,77 @@
+# !/usr/bin/env python
+
+# 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.
+
+"""Run a trained policy on LeKiwi without recording (base rollout).
+
+Uses the rollout engine's :class:`BaseStrategy` (autonomous execution,
+no dataset) with :class:`SyncInferenceConfig` (inline policy call per
+control tick).  For a CLI entry point with the same capabilities plus
+recording, upload, and human-in-the-loop variants, see ``lerobot-rollout``.
+"""
+
+from lerobot.configs import PreTrainedConfig
+from lerobot.robots.lekiwi import LeKiwiClientConfig
+from lerobot.rollout import BaseStrategyConfig, RolloutConfig, build_rollout_context
+from lerobot.rollout.inference import SyncInferenceConfig
+from lerobot.rollout.strategies import BaseStrategy
+from lerobot.utils.process import ProcessSignalHandler
+from lerobot.utils.utils import init_logging
+
+FPS = 30
+DURATION_SEC = 60
+TASK_DESCRIPTION = "My task description"
+HF_MODEL_ID = "<hf_username>/<model_repo_id>"
+
+
+def main():
+    init_logging()
+
+    # Robot: LeKiwi client — make sure lekiwi_host is already running on the robot.
+    robot_config = LeKiwiClientConfig(remote_ip="172.18.134.136", id="lekiwi")
+
+    # Policy: load the pretrained config.  ``pretrained_path`` is read downstream
+    # by ``build_rollout_context`` to reload the full model.
+    policy_config = PreTrainedConfig.from_pretrained(HF_MODEL_ID)
+    policy_config.pretrained_path = HF_MODEL_ID
+
+    # Assemble the rollout config: base strategy (no recording) + sync inference.
+    cfg = RolloutConfig(
+        robot=robot_config,
+        policy=policy_config,
+        strategy=BaseStrategyConfig(),
+        inference=SyncInferenceConfig(),
+        fps=FPS,
+        duration=DURATION_SEC,
+        task=TASK_DESCRIPTION,
+    )
+
+    # Graceful Ctrl-C: the strategy loop exits when shutdown_event is set.
+    signal_handler = ProcessSignalHandler(use_threads=True)
+
+    # Build the context (connects robot, loads policy, wires the inference strategy).
+    # No custom processors here — LeKiwi runs on raw joint features.
+    ctx = build_rollout_context(cfg, signal_handler.shutdown_event)
+
+    strategy = BaseStrategy(cfg.strategy)
+    try:
+        strategy.setup(ctx)
+        strategy.run(ctx)
+    finally:
+        strategy.teardown(ctx)
+
+
+if __name__ == "__main__":
+    main()

examples/phone_to_so100/evaluate.py

@@ -14,13 +14,17 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import logging
+import time
+
 from lerobot.cameras.opencv import OpenCVCameraConfig
-from lerobot.common.control_utils import init_keyboard_listener
+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
 from lerobot.policies import make_pre_post_processors
 from lerobot.policies.act import ACTPolicy
+from lerobot.policies.utils import make_robot_action
 from lerobot.processor import (
     RobotProcessorPipeline,
     make_default_teleop_action_processor,
@@ -34,11 +38,12 @@ from lerobot.robots.so_follower.robot_kinematic_processor import (
     ForwardKinematicsJointsToEE,
     InverseKinematicsEEToJoints,
 )
-from lerobot.scripts.lerobot_record import record_loop
 from lerobot.types import RobotAction, RobotObservation
-from lerobot.utils.feature_utils import combine_feature_dicts
+from lerobot.utils.constants import ACTION, OBS_STR
+from lerobot.utils.feature_utils import build_dataset_frame, combine_feature_dicts
+from lerobot.utils.robot_utils import precise_sleep
 from lerobot.utils.utils import log_say
-from lerobot.utils.visualization_utils import init_rerun
+from lerobot.utils.visualization_utils import init_rerun, log_rerun_data
 
 NUM_EPISODES = 5
 FPS = 30
@@ -49,6 +54,9 @@ HF_DATASET_ID = "<hf_username>/<dataset_repo_id>"
 
 
 def main():
+    # NOTE: For production policy deployment, use `lerobot-rollout` CLI instead.
+    # This script provides a self-contained example for educational purposes.
+
     # Create the robot configuration & robot
     camera_config = {"front": OpenCVCameraConfig(index_or_path=0, width=640, height=480, fps=FPS)}
     robot_config = SO100FollowerConfig(
@@ -143,43 +151,67 @@ def main():
             raise ValueError("Robot is not connected!")
 
         print("Starting evaluate loop...")
+        control_interval = 1 / FPS
         episode_idx = 0
         for episode_idx in range(NUM_EPISODES):
             log_say(f"Running inference, recording eval episode {episode_idx + 1} of {NUM_EPISODES}")
 
-            # Main record loop
-            record_loop(
-                robot=robot,
-                events=events,
-                fps=FPS,
-                policy=policy,
-                preprocessor=preprocessor,  # Pass the pre and post policy processors
-                postprocessor=postprocessor,
-                dataset=dataset,
-                control_time_s=EPISODE_TIME_SEC,
-                single_task=TASK_DESCRIPTION,
-                display_data=True,
-                teleop_action_processor=make_default_teleop_action_processor(),
-                robot_action_processor=robot_ee_to_joints_processor,
-                robot_observation_processor=robot_joints_to_ee_pose_processor,
-            )
+            # Inline evaluation loop: predict actions and send to robot
+            timestamp = 0
+            start_episode_t = time.perf_counter()
+            while timestamp < EPISODE_TIME_SEC:
+                start_loop_t = time.perf_counter()
+
+                if events["exit_early"]:
+                    events["exit_early"] = False
+                    break
+
+                # Get robot observation
+                obs = robot.get_observation()
+                obs_processed = robot_joints_to_ee_pose_processor(obs)
+                observation_frame = build_dataset_frame(dataset.features, obs_processed, prefix=OBS_STR)
+
+                # Predict action using the policy
+                action_tensor = predict_action(
+                    observation=observation_frame,
+                    policy=policy,
+                    device=policy.config.device,
+                    preprocessor=preprocessor,
+                    postprocessor=postprocessor,
+                    use_amp=policy.config.device.type == "cuda",
+                    task=TASK_DESCRIPTION,
+                    robot_type=robot.name,
+                )
+
+                # Convert policy output to robot action dict
+                action_values = make_robot_action(action_tensor, dataset.features)
+
+                # Process and send action to robot (EE -> joints via IK)
+                robot_action_to_send = robot_ee_to_joints_processor((action_values, obs))
+                robot.send_action(robot_action_to_send)
+
+                # Write to dataset
+                action_frame = build_dataset_frame(dataset.features, action_values, prefix=ACTION)
+                frame = {**observation_frame, **action_frame, "task": TASK_DESCRIPTION}
+                dataset.add_frame(frame)
+
+                log_rerun_data(observation=obs_processed, action=action_values)
+
+                dt_s = time.perf_counter() - start_loop_t
+                sleep_time_s = control_interval - dt_s
+                if sleep_time_s < 0:
+                    logging.warning(
+                        f"Evaluate loop is running slower ({1 / dt_s:.1f} Hz) than the target FPS ({FPS} Hz)."
+                    )
+                precise_sleep(max(sleep_time_s, 0.0))
+                timestamp = time.perf_counter() - start_episode_t
 
             # Reset the environment if not stopping or re-recording
             if not events["stop_recording"] and (
                 (episode_idx < NUM_EPISODES - 1) or events["rerecord_episode"]
             ):
                 log_say("Reset the environment")
-                record_loop(
-                    robot=robot,
-                    events=events,
-                    fps=FPS,
-                    control_time_s=EPISODE_TIME_SEC,
-                    single_task=TASK_DESCRIPTION,
-                    display_data=True,
-                    teleop_action_processor=make_default_teleop_action_processor(),
-                    robot_action_processor=robot_ee_to_joints_processor,
-                    robot_observation_processor=robot_joints_to_ee_pose_processor,
-                )
+                log_say("Waiting for environment reset, press right arrow key when ready...")
 
             if events["rerecord_episode"]:
                 log_say("Re-record episode")
@@ -190,7 +222,6 @@ def main():
 
             # Save episode
             dataset.save_episode()
-            episode_idx += 1
     finally:
         # Clean up
         log_say("Stop recording")

examples/phone_to_so100/record.py

@@ -65,14 +65,15 @@ def main():
     robot = SO100Follower(robot_config)
     phone = Phone(teleop_config)
 
-    # NOTE: It is highly recommended to use the urdf in the SO-ARM100 repo: https://github.com/TheRobotStudio/SO-ARM100/blob/main/Simulation/SO101/so101_new_calib.urdf
+    # NOTE: It is highly recommended to use the urdf in the SO-ARM100 repo:
+    #   https://github.com/TheRobotStudio/SO-ARM100/blob/main/Simulation/SO101/so101_new_calib.urdf
     kinematics_solver = RobotKinematics(
         urdf_path="./SO101/so101_new_calib.urdf",
         target_frame_name="gripper_frame_link",
         joint_names=list(robot.bus.motors.keys()),
     )
 
-    # Build pipeline to convert phone action to EE action
+    # Build pipeline to convert phone action to EE action (with gripper velocity mapped to joint).
     phone_to_robot_ee_pose_processor = RobotProcessorPipeline[
         tuple[RobotAction, RobotObservation], RobotAction
     ](
@@ -94,7 +95,7 @@ def main():
         to_output=transition_to_robot_action,
     )
 
-    # Build pipeline to convert EE action to joints action
+    # Build pipeline to convert EE action to joints action (IK).
     robot_ee_to_joints_processor = RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction](
         steps=[
             InverseKinematicsEEToJoints(
@@ -107,7 +108,7 @@ def main():
         to_output=transition_to_robot_action,
     )
 
-    # Build pipeline to convert joint observation to EE observation
+    # Build pipeline to convert joint observation to EE observation (FK).
     robot_joints_to_ee_pose = RobotProcessorPipeline[RobotObservation, RobotObservation](
         steps=[
             ForwardKinematicsJointsToEE(
@@ -118,13 +119,12 @@ def main():
         to_output=transition_to_observation,
     )
 
-    # Create the dataset
+    # Create the dataset, deriving features from the pipelines so the on-disk schema
+    # matches exactly what the pipelines produce at runtime.
     dataset = LeRobotDataset.create(
         repo_id=HF_REPO_ID,
         fps=FPS,
         features=combine_feature_dicts(
-            # Run the feature contract of the pipelines
-            # This tells you how the features would look like after the pipeline steps
             aggregate_pipeline_dataset_features(
                 pipeline=phone_to_robot_ee_pose_processor,
                 initial_features=create_initial_features(action=phone.action_features),
@@ -163,14 +163,14 @@ def main():
                 robot=robot,
                 events=events,
                 fps=FPS,
+                teleop_action_processor=phone_to_robot_ee_pose_processor,
+                robot_action_processor=robot_ee_to_joints_processor,
+                robot_observation_processor=robot_joints_to_ee_pose,
                 teleop=phone,
                 dataset=dataset,
                 control_time_s=EPISODE_TIME_SEC,
                 single_task=TASK_DESCRIPTION,
                 display_data=True,
-                teleop_action_processor=phone_to_robot_ee_pose_processor,
-                robot_action_processor=robot_ee_to_joints_processor,
-                robot_observation_processor=robot_joints_to_ee_pose,
             )
 
             # Reset the environment if not stopping or re-recording
@@ -182,13 +182,13 @@ def main():
                     robot=robot,
                     events=events,
                     fps=FPS,
+                    teleop_action_processor=phone_to_robot_ee_pose_processor,
+                    robot_action_processor=robot_ee_to_joints_processor,
+                    robot_observation_processor=robot_joints_to_ee_pose,
                     teleop=phone,
                     control_time_s=RESET_TIME_SEC,
                     single_task=TASK_DESCRIPTION,
                     display_data=True,
-                    teleop_action_processor=phone_to_robot_ee_pose_processor,
-                    robot_action_processor=robot_ee_to_joints_processor,
-                    robot_observation_processor=robot_joints_to_ee_pose,
                 )
 
             if events["rerecord_episode"]:

examples/phone_to_so100/rollout.py

@@ -0,0 +1,126 @@
+# !/usr/bin/env python
+
+# 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.
+
+"""Run a trained EE-space policy on SO100 (phone-trained) without recording.
+
+Mirrors ``examples/so100_to_so100_EE/rollout.py`` — the model was trained
+with phone teleoperation in EE space, so at deployment we only need the
+joint↔EE conversion on the robot side; the phone is not used.
+
+Uses :class:`BaseStrategy` (no recording) + :class:`SyncInferenceConfig`
+(inline policy call).  For recording during rollout, switch to Sentry,
+Highlight, or DAgger via ``lerobot-rollout --strategy.type=...``.
+"""
+
+from lerobot.cameras.opencv import OpenCVCameraConfig
+from lerobot.configs import PreTrainedConfig
+from lerobot.model.kinematics import RobotKinematics
+from lerobot.processor import (
+    RobotProcessorPipeline,
+    observation_to_transition,
+    robot_action_observation_to_transition,
+    transition_to_observation,
+    transition_to_robot_action,
+)
+from lerobot.robots.so_follower import SO100Follower, SO100FollowerConfig
+from lerobot.robots.so_follower.robot_kinematic_processor import (
+    ForwardKinematicsJointsToEE,
+    InverseKinematicsEEToJoints,
+)
+from lerobot.rollout import BaseStrategyConfig, RolloutConfig, build_rollout_context
+from lerobot.rollout.inference import SyncInferenceConfig
+from lerobot.rollout.strategies import BaseStrategy
+from lerobot.types import RobotAction, RobotObservation
+from lerobot.utils.process import ProcessSignalHandler
+from lerobot.utils.utils import init_logging
+
+FPS = 30
+DURATION_SEC = 60
+TASK_DESCRIPTION = "My task description"
+HF_MODEL_ID = "<hf_username>/<model_repo_id>"
+
+
+def main():
+    init_logging()
+
+    camera_config = {"front": OpenCVCameraConfig(index_or_path=0, width=640, height=480, fps=FPS)}
+    robot_config = SO100FollowerConfig(
+        port="/dev/tty.usbmodem58760434471",
+        id="my_awesome_follower_arm",
+        cameras=camera_config,
+        use_degrees=True,
+    )
+
+    # Peek at motor names once to build the kinematic solver.
+    temp_robot = SO100Follower(robot_config)
+    motor_names = list(temp_robot.bus.motors.keys())
+
+    kinematics_solver = RobotKinematics(
+        urdf_path="./SO101/so101_new_calib.urdf",
+        target_frame_name="gripper_frame_link",
+        joint_names=motor_names,
+    )
+
+    robot_joints_to_ee_pose_processor = RobotProcessorPipeline[RobotObservation, RobotObservation](
+        steps=[ForwardKinematicsJointsToEE(kinematics=kinematics_solver, motor_names=motor_names)],
+        to_transition=observation_to_transition,
+        to_output=transition_to_observation,
+    )
+
+    robot_ee_to_joints_processor = RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction](
+        steps=[
+            InverseKinematicsEEToJoints(
+                kinematics=kinematics_solver,
+                motor_names=motor_names,
+                initial_guess_current_joints=True,
+            ),
+        ],
+        to_transition=robot_action_observation_to_transition,
+        to_output=transition_to_robot_action,
+    )
+
+    policy_config = PreTrainedConfig.from_pretrained(HF_MODEL_ID)
+    policy_config.pretrained_path = HF_MODEL_ID
+
+    cfg = RolloutConfig(
+        robot=robot_config,
+        policy=policy_config,
+        strategy=BaseStrategyConfig(),
+        inference=SyncInferenceConfig(),
+        fps=FPS,
+        duration=DURATION_SEC,
+        task=TASK_DESCRIPTION,
+    )
+
+    signal_handler = ProcessSignalHandler(use_threads=True)
+
+    ctx = build_rollout_context(
+        cfg,
+        signal_handler.shutdown_event,
+        robot_action_processor=robot_ee_to_joints_processor,
+        robot_observation_processor=robot_joints_to_ee_pose_processor,
+    )
+
+    strategy = BaseStrategy(cfg.strategy)
+    try:
+        strategy.setup(ctx)
+        strategy.run(ctx)
+    finally:
+        strategy.teardown(ctx)
+
+
+if __name__ == "__main__":
+    main()

examples/rtc/eval_with_real_robot.py

@@ -1,673 +0,0 @@
-#!/usr/bin/env python
-
-# 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.
-
-"""
-Demo script showing how to use Real-Time Chunking (RTC) with action chunking policies on real robots.
-
-This script demonstrates:
-1. Creating a robot and policy (SmolVLA, Pi0, etc.) with RTC
-2. Consuming actions from the policy while the robot executes
-3. Periodically requesting new action chunks in the background using threads
-4. Managing action buffers and timing for real-time operation
-
-For simulation environments, see eval_with_simulation.py
-
-Usage:
-    # Run RTC with Real robot with RTC
-    uv run examples/rtc/eval_with_real_robot.py \
-        --policy.path=<USER>/smolvla_check_rtc_last3 \
-        --policy.device=mps \
-        --rtc.enabled=true \
-        --rtc.execution_horizon=20 \
-        --robot.type=so100_follower \
-        --robot.port=/dev/tty.usbmodem58FA0834591 \
-        --robot.id=so100_follower \
-        --robot.cameras="{ gripper: {type: opencv, index_or_path: 1, width: 640, height: 480, fps: 30}, front: {type: opencv, index_or_path: 0, width: 640, height: 480, fps: 30}}" \
-        --task="Move green small object into the purple platform" \
-        --duration=120
-
-    # Run RTC with Real robot without RTC
-    uv run examples/rtc/eval_with_real_robot.py \
-        --policy.path=<USER>/smolvla_check_rtc_last3 \
-        --policy.device=mps \
-        --rtc.enabled=false \
-        --robot.type=so100_follower \
-        --robot.port=/dev/tty.usbmodem58FA0834591 \
-        --robot.id=so100_follower \
-        --robot.cameras="{ gripper: {type: opencv, index_or_path: 1, width: 640, height: 480, fps: 30}, front: {type: opencv, index_or_path: 0, width: 640, height: 480, fps: 30}}" \
-        --task="Move green small object into the purple platform" \
-        --duration=120
-
-    # Run RTC with Real robot with pi0.5 policy
-    uv run examples/rtc/eval_with_real_robot.py \
-        --policy.path=<USER>/pi05_check_rtc \
-        --policy.device=mps \
-        --rtc.enabled=true \
-        --rtc.execution_horizon=20 \
-        --robot.type=so100_follower \
-        --robot.port=/dev/tty.usbmodem58FA0834591 \
-        --robot.id=so100_follower \
-        --robot.cameras="{ gripper: {type: opencv, index_or_path: 0, width: 640, height: 480, fps: 30}, front: {type: opencv, index_or_path: 1, width: 640, height: 480, fps: 30}}" \
-        --task="Move green small object into the purple platform" \
-        --duration=120
-
-    # Run RTC with bi_openarm_follower (dual-arm OpenArms) and pi0.5 policy
-    python examples/rtc/eval_with_real_robot.py \
-        --policy.path=lerobot-data-collection/folding_final \
-        --robot.type=bi_openarm_follower \
-        --robot.cameras='{left_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}, right_wrist: {type: opencv, index_or_path: "/dev/video0", width: 1280, height: 720, fps: 30}}' \
-        --robot.left_arm_config.port=can0 \
-        --robot.left_arm_config.side=left \
-        --robot.left_arm_config.can_interface=socketcan \
-        --robot.left_arm_config.disable_torque_on_disconnect=true \
-        --robot.left_arm_config.max_relative_target=8.0 \
-        --robot.right_arm_config.port=can1 \
-        --robot.right_arm_config.side=right \
-        --robot.right_arm_config.can_interface=socketcan \
-        --robot.right_arm_config.disable_torque_on_disconnect=true \
-        --robot.right_arm_config.max_relative_target=8.0 \
-        --task="Fold the T-shirt properly" \
-        --fps=30 \
-        --duration=2000 \
-        --interpolation_multiplier=3 \
-        --rtc.enabled=true \
-        --rtc.execution_horizon=20 \
-        --rtc.max_guidance_weight=5.0 \
-        --rtc.prefix_attention_schedule=LINEAR \
-        --device=cuda
-"""
-
-import logging
-import math
-import sys
-import time
-import traceback
-from dataclasses import dataclass, field
-from threading import Event, Lock, Thread
-
-import torch
-from torch import Tensor
-
-from lerobot.cameras.opencv import OpenCVCameraConfig  # noqa: F401
-from lerobot.cameras.realsense import RealSenseCameraConfig  # noqa: F401
-from lerobot.cameras.zmq import ZMQCameraConfig  # noqa: F401
-from lerobot.configs import PreTrainedConfig, RTCAttentionSchedule, parser
-from lerobot.policies import get_policy_class, make_pre_post_processors
-from lerobot.policies.rtc import ActionInterpolator, ActionQueue, LatencyTracker, RTCConfig
-from lerobot.processor import (
-    NormalizerProcessorStep,
-    RelativeActionsProcessorStep,
-    TransitionKey,
-    create_transition,
-    make_default_robot_action_processor,
-    make_default_robot_observation_processor,
-    to_relative_actions,
-)
-from lerobot.rl.process import ProcessSignalHandler
-from lerobot.robots import (  # noqa: F401
-    Robot,
-    RobotConfig,
-    bi_openarm_follower,
-    bi_so_follower,
-    koch_follower,
-    so_follower,
-    unitree_g1,
-)
-from lerobot.robots.utils import make_robot_from_config
-from lerobot.utils.constants import OBS_IMAGES, OBS_STATE
-from lerobot.utils.feature_utils import build_dataset_frame, hw_to_dataset_features
-from lerobot.utils.hub import HubMixin
-from lerobot.utils.utils import init_logging
-
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-
-class RobotWrapper:
-    def __init__(self, robot: Robot):
-        self.robot = robot
-        self.lock = Lock()
-
-    def get_observation(self) -> dict[str, Tensor]:
-        with self.lock:
-            return self.robot.get_observation()
-
-    def send_action(self, action: Tensor):
-        with self.lock:
-            self.robot.send_action(action)
-
-    def observation_features(self) -> list[str]:
-        with self.lock:
-            return self.robot.observation_features
-
-    def action_features(self) -> list[str]:
-        with self.lock:
-            return self.robot.action_features
-
-
-@dataclass
-class RTCDemoConfig(HubMixin):
-    """Configuration for RTC demo with action chunking policies and real robots."""
-
-    # Policy configuration
-    policy: PreTrainedConfig | None = None
-
-    # Robot configuration
-    robot: RobotConfig | None = None
-
-    # RTC configuration
-    rtc: RTCConfig = field(
-        default_factory=lambda: RTCConfig(
-            execution_horizon=10,
-            max_guidance_weight=1.0,
-            prefix_attention_schedule=RTCAttentionSchedule.EXP,
-        )
-    )
-
-    # Demo parameters
-    duration: float = 30.0  # Duration to run the demo (seconds)
-    fps: float = 10.0  # Action execution frequency (Hz)
-    interpolation_multiplier: int = 1  # Control rate multiplier (1=off, 2=2x, 3=3x)
-
-    # Compute device
-    device: str | None = None  # Device to run on (cuda, cpu, auto)
-
-    # Get new actions horizon. The amount of executed steps after which will be requested new actions.
-    # It should be higher than inference delay + execution horizon.
-    action_queue_size_to_get_new_actions: int = 30
-
-    # Task to execute
-    task: str = field(default="", metadata={"help": "Task to execute"})
-
-    # Torch compile configuration
-    use_torch_compile: bool = field(
-        default=False,
-        metadata={"help": "Use torch.compile for faster inference (PyTorch 2.0+)"},
-    )
-
-    torch_compile_backend: str = field(
-        default="inductor",
-        metadata={"help": "Backend for torch.compile (inductor, aot_eager, cudagraphs)"},
-    )
-
-    torch_compile_mode: str = field(
-        default="default",
-        metadata={"help": "Compilation mode (default, reduce-overhead, max-autotune)"},
-    )
-
-    torch_compile_disable_cudagraphs: bool = field(
-        default=True,
-        metadata={
-            "help": "Disable CUDA graphs in torch.compile. Required due to in-place tensor "
-            "operations in denoising loop (x_t += dt * v_t) which cause tensor aliasing issues."
-        },
-    )
-
-    def __post_init__(self):
-        # HACK: We parse again the cli args here to get the pretrained path if there was one.
-        policy_path = parser.get_path_arg("policy")
-        if policy_path:
-            cli_overrides = parser.get_cli_overrides("policy")
-            self.policy = PreTrainedConfig.from_pretrained(policy_path, cli_overrides=cli_overrides)
-            self.policy.pretrained_path = policy_path
-        else:
-            raise ValueError("Policy path is required")
-
-        # Validate that robot configuration is provided
-        if self.robot is None:
-            raise ValueError("Robot configuration must be provided")
-
-    @classmethod
-    def __get_path_fields__(cls) -> list[str]:
-        """This enables the parser to load config from the policy using `--policy.path=local/dir`"""
-        return ["policy"]
-
-
-def is_image_key(k: str) -> bool:
-    return k.startswith(OBS_IMAGES)
-
-
-def _reanchor_relative_rtc_prefix(
-    prev_actions_absolute: Tensor,
-    current_state: Tensor,
-    relative_step: RelativeActionsProcessorStep,
-    normalizer_step: NormalizerProcessorStep | None,
-    policy_device: torch.device | str,
-) -> Tensor:
-    """Convert absolute leftovers into model-space for relative-action RTC policies.
-
-    When a policy uses relative actions, the RTC prefix (leftover actions from
-    the previous chunk) is stored in absolute space. Before feeding it back to
-    the policy we need to re-express it relative to the *current* robot state
-    and then re-normalize.
-    """
-    state = current_state.detach().cpu()
-    if state.dim() == 1:
-        state = state.unsqueeze(0)
-
-    action_cpu = prev_actions_absolute.detach().cpu()
-    mask = relative_step._build_mask(action_cpu.shape[-1])
-    relative_actions = to_relative_actions(action_cpu, state, mask)
-
-    transition = create_transition(action=relative_actions)
-    if normalizer_step is not None:
-        transition = normalizer_step(transition)
-
-    return transition[TransitionKey.ACTION].to(policy_device)
-
-
-def get_actions(
-    policy,
-    robot: RobotWrapper,
-    robot_observation_processor,
-    action_queue: ActionQueue,
-    shutdown_event: Event,
-    cfg: RTCDemoConfig,
-):
-    """Thread function to request action chunks from the policy.
-
-    Args:
-        policy: The policy instance (SmolVLA, Pi0, etc.)
-        robot: The robot instance for getting observations
-        robot_observation_processor: Processor for raw robot observations
-        action_queue: Queue to put new action chunks
-        shutdown_event: Event to signal shutdown
-        cfg: Demo configuration
-    """
-    try:
-        logger.info("[GET_ACTIONS] Starting get actions thread")
-
-        latency_tracker = LatencyTracker()  # Track latency of action chunks
-        fps = cfg.fps
-        time_per_chunk = 1.0 / fps
-
-        # Only keep .pos joints + camera streams if the policy was trained on positions,
-        # not the full pos/vel/torque state the robot exposes.
-        observation_features_hw = {
-            key: value
-            for key, value in robot.observation_features().items()
-            if key.endswith(".pos") or isinstance(value, tuple)
-        }
-
-        dataset_features = hw_to_dataset_features(observation_features_hw, "observation")
-        policy_device = policy.config.device
-
-        # Load preprocessor and postprocessor from pretrained files
-        # The stats are embedded in the processor .safetensors files
-        logger.info(f"[GET_ACTIONS] Loading preprocessor/postprocessor from {cfg.policy.pretrained_path}")
-
-        preprocessor, postprocessor = make_pre_post_processors(
-            policy_cfg=cfg.policy,
-            pretrained_path=cfg.policy.pretrained_path,
-            dataset_stats=None,  # Will load from pretrained processor files
-            preprocessor_overrides={
-                "device_processor": {"device": cfg.policy.device},
-            },
-        )
-
-        logger.info("[GET_ACTIONS] Preprocessor/postprocessor loaded successfully with embedded stats")
-
-        relative_step = next(
-            (s for s in preprocessor.steps if isinstance(s, RelativeActionsProcessorStep) and s.enabled),
-            None,
-        )
-        normalizer_step = next(
-            (s for s in preprocessor.steps if isinstance(s, NormalizerProcessorStep)),
-            None,
-        )
-        if relative_step is not None:
-            if relative_step.action_names is None:
-                cfg_names = getattr(cfg.policy, "action_feature_names", None)
-                if cfg_names:
-                    relative_step.action_names = list(cfg_names)
-                else:
-                    relative_step.action_names = [
-                        k for k in robot.robot.action_features if k.endswith(".pos")
-                    ]
-            logger.info("[GET_ACTIONS] Relative actions enabled: will re-anchor RTC prefix")
-
-        get_actions_threshold = cfg.action_queue_size_to_get_new_actions
-
-        if not cfg.rtc.enabled:
-            get_actions_threshold = 0
-
-        while not shutdown_event.is_set():
-            if action_queue.qsize() <= get_actions_threshold:
-                current_time = time.perf_counter()
-                action_index_before_inference = action_queue.get_action_index()
-                prev_actions = action_queue.get_left_over()
-
-                inference_latency = latency_tracker.max()
-                inference_delay = math.ceil(inference_latency / time_per_chunk)
-
-                obs = robot.get_observation()
-
-                # Apply robot observation processor
-                obs_processed = robot_observation_processor(obs)
-
-                obs_with_policy_features = build_dataset_frame(
-                    dataset_features, obs_processed, prefix="observation"
-                )
-
-                for name in obs_with_policy_features:
-                    obs_with_policy_features[name] = torch.from_numpy(obs_with_policy_features[name])
-                    if "image" in name:
-                        obs_with_policy_features[name] = (
-                            obs_with_policy_features[name].type(torch.float32) / 255
-                        )
-                        obs_with_policy_features[name] = (
-                            obs_with_policy_features[name].permute(2, 0, 1).contiguous()
-                        )
-                    obs_with_policy_features[name] = obs_with_policy_features[name].unsqueeze(0)
-                    obs_with_policy_features[name] = obs_with_policy_features[name].to(policy_device)
-
-                obs_with_policy_features["task"] = [cfg.task]  # Task should be a list, not a string!
-                obs_with_policy_features["robot_type"] = (
-                    robot.robot.name if hasattr(robot.robot, "name") else ""
-                )
-
-                preproceseded_obs = preprocessor(obs_with_policy_features)
-
-                # Re-anchor leftover actions for relative-action policies.
-                # We need the *postprocessed* (absolute) leftover, not the original
-                # (normalized/relative) one that get_left_over() returns.
-                if (
-                    prev_actions is not None
-                    and relative_step is not None
-                    and OBS_STATE in obs_with_policy_features
-                ):
-                    with action_queue.lock:
-                        if action_queue.queue is not None:
-                            prev_actions_abs = action_queue.queue[action_queue.last_index :].clone()
-                        else:
-                            prev_actions_abs = None
-                    if prev_actions_abs is not None and prev_actions_abs.numel() > 0:
-                        prev_actions = _reanchor_relative_rtc_prefix(
-                            prev_actions_absolute=prev_actions_abs,
-                            current_state=obs_with_policy_features[OBS_STATE],
-                            relative_step=relative_step,
-                            normalizer_step=normalizer_step,
-                            policy_device=policy_device,
-                        )
-
-                # Generate actions WITH RTC
-                actions = policy.predict_action_chunk(
-                    preproceseded_obs,
-                    inference_delay=inference_delay,
-                    prev_chunk_left_over=prev_actions,
-                )
-
-                # Store original actions (before postprocessing) for RTC
-                original_actions = actions.squeeze(0).clone()
-
-                postprocessed_actions = postprocessor(actions)
-
-                postprocessed_actions = postprocessed_actions.squeeze(0)
-
-                new_latency = time.perf_counter() - current_time
-                new_delay = math.ceil(new_latency / time_per_chunk)
-                latency_tracker.add(new_latency)
-
-                if cfg.action_queue_size_to_get_new_actions < cfg.rtc.execution_horizon + new_delay:
-                    logger.warning(
-                        "[GET_ACTIONS] cfg.action_queue_size_to_get_new_actions Too small, It should be higher than inference delay + execution horizon."
-                    )
-
-                action_queue.merge(
-                    original_actions, postprocessed_actions, new_delay, action_index_before_inference
-                )
-            else:
-                # Small sleep to prevent busy waiting
-                time.sleep(0.1)
-
-        logger.info("[GET_ACTIONS] get actions thread shutting down")
-    except Exception as e:
-        logger.error(f"[GET_ACTIONS] Fatal exception in get_actions thread: {e}")
-        logger.error(traceback.format_exc())
-        sys.exit(1)
-
-
-def actor_control(
-    robot: RobotWrapper,
-    robot_action_processor,
-    action_queue: ActionQueue,
-    shutdown_event: Event,
-    cfg: RTCDemoConfig,
-):
-    """Thread function to execute actions on the robot.
-
-    Args:
-        robot: The robot instance
-        action_queue: Queue to get actions from
-        shutdown_event: Event to signal shutdown
-        cfg: Demo configuration
-    """
-    try:
-        logger.info("[ACTOR] Starting actor thread")
-
-        action_keys = [k for k in robot.action_features() if k.endswith(".pos")]
-
-        action_count = 0
-        interpolator = ActionInterpolator(multiplier=cfg.interpolation_multiplier)
-        action_interval = interpolator.get_control_interval(cfg.fps)
-
-        while not shutdown_event.is_set():
-            start_time = time.perf_counter()
-
-            if interpolator.needs_new_action():
-                new_action = action_queue.get()
-                if new_action is not None:
-                    interpolator.add(new_action.cpu())
-
-            action = interpolator.get()
-            if action is not None:
-                action = action.cpu()
-                action_dict = {key: action[i].item() for i, key in enumerate(action_keys)}
-                action_processed = robot_action_processor((action_dict, None))
-                robot.send_action(action_processed)
-                action_count += 1
-
-            dt_s = time.perf_counter() - start_time
-            time.sleep(max(0, (action_interval - dt_s) - 0.001))
-
-        logger.info(f"[ACTOR] Actor thread shutting down. Total actions executed: {action_count}")
-    except Exception as e:
-        logger.error(f"[ACTOR] Fatal exception in actor_control thread: {e}")
-        logger.error(traceback.format_exc())
-        sys.exit(1)
-
-
-def _apply_torch_compile(policy, cfg: RTCDemoConfig):
-    """Apply torch.compile to the policy's predict_action_chunk method.
-
-    Args:
-        policy: Policy instance to compile
-        cfg: Configuration containing torch compile settings
-
-    Returns:
-        Policy with compiled predict_action_chunk method
-    """
-
-    # PI models handle their own compilation
-    if policy.type == "pi05" or policy.type == "pi0":
-        return policy
-
-    try:
-        # Check if torch.compile is available (PyTorch 2.0+)
-        if not hasattr(torch, "compile"):
-            logger.warning(
-                f"torch.compile is not available. Requires PyTorch 2.0+. "
-                f"Current version: {torch.__version__}. Skipping compilation."
-            )
-            return policy
-
-        logger.info("Applying torch.compile to predict_action_chunk...")
-        logger.info(f"  Backend: {cfg.torch_compile_backend}")
-        logger.info(f"  Mode: {cfg.torch_compile_mode}")
-        logger.info(f"  Disable CUDA graphs: {cfg.torch_compile_disable_cudagraphs}")
-
-        # Compile the predict_action_chunk method
-        # - CUDA graphs disabled to prevent tensor aliasing from in-place ops (x_t += dt * v_t)
-        compile_kwargs = {
-            "backend": cfg.torch_compile_backend,
-            "mode": cfg.torch_compile_mode,
-        }
-
-        # Disable CUDA graphs if requested (prevents tensor aliasing issues)
-        if cfg.torch_compile_disable_cudagraphs:
-            compile_kwargs["options"] = {"triton.cudagraphs": False}
-
-        original_method = policy.predict_action_chunk
-        compiled_method = torch.compile(original_method, **compile_kwargs)
-        policy.predict_action_chunk = compiled_method
-        logger.info("✓ Successfully compiled predict_action_chunk")
-
-    except Exception as e:
-        logger.error(f"Failed to apply torch.compile: {e}")
-        logger.warning("Continuing without torch.compile")
-
-    return policy
-
-
-@parser.wrap()
-def demo_cli(cfg: RTCDemoConfig):
-    """Main entry point for RTC demo with draccus configuration."""
-
-    # Initialize logging
-    init_logging()
-
-    logger.info(f"Using device: {cfg.device}")
-
-    # Setup signal handler for graceful shutdown
-    signal_handler = ProcessSignalHandler(use_threads=True, display_pid=False)
-    shutdown_event = signal_handler.shutdown_event
-
-    policy = None
-    robot = None
-    get_actions_thread = None
-    actor_thread = None
-
-    policy_class = get_policy_class(cfg.policy.type)
-
-    # Load config and set compile_model for pi0/pi05 models
-    config = PreTrainedConfig.from_pretrained(cfg.policy.pretrained_path)
-
-    if cfg.policy.type == "pi05" or cfg.policy.type == "pi0":
-        config.compile_model = cfg.use_torch_compile
-
-    if config.use_peft:
-        from peft import PeftConfig, PeftModel
-
-        peft_pretrained_path = cfg.policy.pretrained_path
-        peft_config = PeftConfig.from_pretrained(peft_pretrained_path)
-
-        policy = policy_class.from_pretrained(
-            pretrained_name_or_path=peft_config.base_model_name_or_path, config=config
-        )
-        policy = PeftModel.from_pretrained(policy, peft_pretrained_path, config=peft_config)
-    else:
-        policy = policy_class.from_pretrained(cfg.policy.pretrained_path, config=config)
-
-    # Turn on RTC
-    policy.config.rtc_config = cfg.rtc
-
-    # Init RTC processort, as by default if RTC disabled in the config
-    # The processor won't be created
-    policy.init_rtc_processor()
-
-    assert policy.name in ["smolvla", "pi05", "pi0"], "Only smolvla, pi05, and pi0 are supported for RTC"
-
-    policy = policy.to(cfg.device)
-    policy.eval()
-
-    # Apply torch.compile to predict_action_chunk method if enabled
-    if cfg.use_torch_compile:
-        policy = _apply_torch_compile(policy, cfg)
-
-    # Create robot
-    logger.info(f"Initializing robot: {cfg.robot.type}")
-    robot = make_robot_from_config(cfg.robot)
-    robot.connect()
-    robot_wrapper = RobotWrapper(robot)
-
-    # Create robot observation processor
-    robot_observation_processor = make_default_robot_observation_processor()
-    robot_action_processor = make_default_robot_action_processor()
-
-    # Create action queue for communication between threads
-    action_queue = ActionQueue(cfg.rtc)
-
-    # Start chunk requester thread
-    get_actions_thread = Thread(
-        target=get_actions,
-        args=(policy, robot_wrapper, robot_observation_processor, action_queue, shutdown_event, cfg),
-        daemon=True,
-        name="GetActions",
-    )
-    get_actions_thread.start()
-    logger.info("Started get actions thread")
-
-    # Start action executor thread
-    actor_thread = Thread(
-        target=actor_control,
-        args=(robot_wrapper, robot_action_processor, action_queue, shutdown_event, cfg),
-        daemon=True,
-        name="Actor",
-    )
-    actor_thread.start()
-    logger.info("Started actor thread")
-
-    logger.info("Started stop by duration thread")
-
-    # Main thread monitors for duration or shutdown
-    logger.info(f"Running demo for {cfg.duration} seconds...")
-    start_time = time.time()
-
-    while not shutdown_event.is_set() and (time.time() - start_time) < cfg.duration:
-        time.sleep(10)
-
-        # Log queue status periodically
-        if int(time.time() - start_time) % 5 == 0:
-            logger.info(f"[MAIN] Action queue size: {action_queue.qsize()}")
-
-        if time.time() - start_time > cfg.duration:
-            break
-
-    logger.info("Demo duration reached or shutdown requested")
-
-    # Signal shutdown
-    shutdown_event.set()
-
-    # Wait for threads to finish
-    if get_actions_thread and get_actions_thread.is_alive():
-        logger.info("Waiting for chunk requester thread to finish...")
-        get_actions_thread.join()
-
-    if actor_thread and actor_thread.is_alive():
-        logger.info("Waiting for action executor thread to finish...")
-        actor_thread.join()
-
-    # Cleanup robot
-    if robot:
-        robot.disconnect()
-        logger.info("Robot disconnected")
-
-    logger.info("Cleanup completed")
-
-
-if __name__ == "__main__":
-    demo_cli()
-    logging.info("RTC demo finished")

examples/so100_to_so100_EE/evaluate.py

@@ -14,13 +14,17 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import logging
+import time
+
 from lerobot.cameras.opencv import OpenCVCameraConfig
-from lerobot.common.control_utils import init_keyboard_listener
+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
 from lerobot.policies import make_pre_post_processors
 from lerobot.policies.act import ACTPolicy
+from lerobot.policies.utils import make_robot_action
 from lerobot.processor import (
     RobotProcessorPipeline,
     make_default_teleop_action_processor,
@@ -34,11 +38,12 @@ from lerobot.robots.so_follower.robot_kinematic_processor import (
     ForwardKinematicsJointsToEE,
     InverseKinematicsEEToJoints,
 )
-from lerobot.scripts.lerobot_record import record_loop
 from lerobot.types import RobotAction, RobotObservation
-from lerobot.utils.feature_utils import combine_feature_dicts
+from lerobot.utils.constants import ACTION, OBS_STR
+from lerobot.utils.feature_utils import build_dataset_frame, combine_feature_dicts
+from lerobot.utils.robot_utils import precise_sleep
 from lerobot.utils.utils import log_say
-from lerobot.utils.visualization_utils import init_rerun
+from lerobot.utils.visualization_utils import init_rerun, log_rerun_data
 
 NUM_EPISODES = 5
 FPS = 30
@@ -49,6 +54,9 @@ HF_DATASET_ID = "<hf_username>/<dataset_repo_id>"
 
 
 def main():
+    # NOTE: For production policy deployment, use `lerobot-rollout` CLI instead.
+    # This script provides a self-contained example for educational purposes.
+
     # Create the robot configuration & robot
     camera_config = {"front": OpenCVCameraConfig(index_or_path=0, width=640, height=480, fps=FPS)}
     robot_config = SO100FollowerConfig(
@@ -143,43 +151,67 @@ def main():
             raise ValueError("Robot is not connected!")
 
         print("Starting evaluate loop...")
+        control_interval = 1 / FPS
         episode_idx = 0
         for episode_idx in range(NUM_EPISODES):
             log_say(f"Running inference, recording eval episode {episode_idx + 1} of {NUM_EPISODES}")
 
-            # Main record loop
-            record_loop(
-                robot=robot,
-                events=events,
-                fps=FPS,
-                policy=policy,
-                preprocessor=preprocessor,  # Pass the pre and post policy processors
-                postprocessor=postprocessor,
-                dataset=dataset,
-                control_time_s=EPISODE_TIME_SEC,
-                single_task=TASK_DESCRIPTION,
-                display_data=True,
-                teleop_action_processor=make_default_teleop_action_processor(),
-                robot_action_processor=robot_ee_to_joints_processor,
-                robot_observation_processor=robot_joints_to_ee_pose_processor,
-            )
+            # Inline evaluation loop: predict actions and send to robot
+            timestamp = 0
+            start_episode_t = time.perf_counter()
+            while timestamp < EPISODE_TIME_SEC:
+                start_loop_t = time.perf_counter()
+
+                if events["exit_early"]:
+                    events["exit_early"] = False
+                    break
+
+                # Get robot observation
+                obs = robot.get_observation()
+                obs_processed = robot_joints_to_ee_pose_processor(obs)
+                observation_frame = build_dataset_frame(dataset.features, obs_processed, prefix=OBS_STR)
+
+                # Predict action using the policy
+                action_tensor = predict_action(
+                    observation=observation_frame,
+                    policy=policy,
+                    device=policy.config.device,
+                    preprocessor=preprocessor,
+                    postprocessor=postprocessor,
+                    use_amp=policy.config.device.type == "cuda",
+                    task=TASK_DESCRIPTION,
+                    robot_type=robot.name,
+                )
+
+                # Convert policy output to robot action dict
+                action_values = make_robot_action(action_tensor, dataset.features)
+
+                # Process and send action to robot (EE -> joints via IK)
+                robot_action_to_send = robot_ee_to_joints_processor((action_values, obs))
+                robot.send_action(robot_action_to_send)
+
+                # Write to dataset
+                action_frame = build_dataset_frame(dataset.features, action_values, prefix=ACTION)
+                frame = {**observation_frame, **action_frame, "task": TASK_DESCRIPTION}
+                dataset.add_frame(frame)
+
+                log_rerun_data(observation=obs_processed, action=action_values)
+
+                dt_s = time.perf_counter() - start_loop_t
+                sleep_time_s = control_interval - dt_s
+                if sleep_time_s < 0:
+                    logging.warning(
+                        f"Evaluate loop is running slower ({1 / dt_s:.1f} Hz) than the target FPS ({FPS} Hz)."
+                    )
+                precise_sleep(max(sleep_time_s, 0.0))
+                timestamp = time.perf_counter() - start_episode_t
 
             # Reset the environment if not stopping or re-recording
             if not events["stop_recording"] and (
                 (episode_idx < NUM_EPISODES - 1) or events["rerecord_episode"]
             ):
                 log_say("Reset the environment")
-                record_loop(
-                    robot=robot,
-                    events=events,
-                    fps=FPS,
-                    control_time_s=EPISODE_TIME_SEC,
-                    single_task=TASK_DESCRIPTION,
-                    display_data=True,
-                    teleop_action_processor=make_default_teleop_action_processor(),
-                    robot_action_processor=robot_ee_to_joints_processor,
-                    robot_observation_processor=robot_joints_to_ee_pose_processor,
-                )
+                log_say("Waiting for environment reset, press right arrow key when ready...")
 
             if events["rerecord_episode"]:
                 log_say("Re-record episode")
@@ -190,7 +222,6 @@ def main():
 
             # Save episode
             dataset.save_episode()
-            episode_idx += 1
     finally:
         # Clean up
         log_say("Stop recording")

examples/so100_to_so100_EE/record.py

@@ -62,21 +62,20 @@ def main():
     follower = SO100Follower(follower_config)
     leader = SO100Leader(leader_config)
 
-    # NOTE: It is highly recommended to use the urdf in the SO-ARM100 repo: https://github.com/TheRobotStudio/SO-ARM100/blob/main/Simulation/SO101/so101_new_calib.urdf
+    # NOTE: It is highly recommended to use the urdf in the SO-ARM100 repo:
+    #   https://github.com/TheRobotStudio/SO-ARM100/blob/main/Simulation/SO101/so101_new_calib.urdf
     follower_kinematics_solver = RobotKinematics(
         urdf_path="./SO101/so101_new_calib.urdf",
         target_frame_name="gripper_frame_link",
         joint_names=list(follower.bus.motors.keys()),
     )
-
-    # NOTE: It is highly recommended to use the urdf in the SO-ARM100 repo: https://github.com/TheRobotStudio/SO-ARM100/blob/main/Simulation/SO101/so101_new_calib.urdf
     leader_kinematics_solver = RobotKinematics(
         urdf_path="./SO101/so101_new_calib.urdf",
         target_frame_name="gripper_frame_link",
         joint_names=list(leader.bus.motors.keys()),
     )
 
-    # Build pipeline to convert follower joints to EE observation
+    # Build pipeline to convert follower joints to EE observation.
     follower_joints_to_ee = RobotProcessorPipeline[RobotObservation, RobotObservation](
         steps=[
             ForwardKinematicsJointsToEE(
@@ -87,7 +86,7 @@ def main():
         to_output=transition_to_observation,
     )
 
-    # Build pipeline to convert leader joints to EE action
+    # Build pipeline to convert leader joints to EE action.
     leader_joints_to_ee = RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction](
         steps=[
             ForwardKinematicsJointsToEE(
@@ -98,9 +97,9 @@ def main():
         to_output=transition_to_robot_action,
     )
 
-    # Build pipeline to convert EE action to follower joints
+    # Build pipeline to convert EE action to follower joints (with safety bounds).
     ee_to_follower_joints = RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction](
-        [
+        steps=[
             EEBoundsAndSafety(
                 end_effector_bounds={"min": [-1.0, -1.0, -1.0], "max": [1.0, 1.0, 1.0]},
                 max_ee_step_m=0.10,
@@ -115,13 +114,12 @@ def main():
         to_output=transition_to_robot_action,
     )
 
-    # Create the dataset
+    # Create the dataset, deriving features from the pipelines so the on-disk schema
+    # matches exactly what the pipelines produce at runtime.
     dataset = LeRobotDataset.create(
         repo_id=HF_REPO_ID,
         fps=FPS,
         features=combine_feature_dicts(
-            # Run the feature contract of the pipelines
-            # This tells you how the features would look like after the pipeline steps
             aggregate_pipeline_dataset_features(
                 pipeline=leader_joints_to_ee,
                 initial_features=create_initial_features(action=leader.action_features),
@@ -144,7 +142,7 @@ def main():
 
     # Initialize the keyboard listener and rerun visualization
     listener, events = init_keyboard_listener()
-    init_rerun(session_name="recording_phone")
+    init_rerun(session_name="recording_so100_ee")
 
     try:
         if not leader.is_connected or not follower.is_connected:
@@ -160,14 +158,14 @@ def main():
                 robot=follower,
                 events=events,
                 fps=FPS,
+                teleop_action_processor=leader_joints_to_ee,
+                robot_action_processor=ee_to_follower_joints,
+                robot_observation_processor=follower_joints_to_ee,
                 teleop=leader,
                 dataset=dataset,
                 control_time_s=EPISODE_TIME_SEC,
                 single_task=TASK_DESCRIPTION,
                 display_data=True,
-                teleop_action_processor=leader_joints_to_ee,
-                robot_action_processor=ee_to_follower_joints,
-                robot_observation_processor=follower_joints_to_ee,
             )
 
             # Reset the environment if not stopping or re-recording
@@ -179,13 +177,13 @@ def main():
                     robot=follower,
                     events=events,
                     fps=FPS,
+                    teleop_action_processor=leader_joints_to_ee,
+                    robot_action_processor=ee_to_follower_joints,
+                    robot_observation_processor=follower_joints_to_ee,
                     teleop=leader,
                     control_time_s=RESET_TIME_SEC,
                     single_task=TASK_DESCRIPTION,
                     display_data=True,
-                    teleop_action_processor=leader_joints_to_ee,
-                    robot_action_processor=ee_to_follower_joints,
-                    robot_observation_processor=follower_joints_to_ee,
                 )
 
             if events["rerecord_episode"]:

examples/so100_to_so100_EE/rollout.py

@@ -0,0 +1,134 @@
+# !/usr/bin/env python
+
+# 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.
+
+"""Run a trained EE-space policy on SO100 without recording (base rollout).
+
+Uses the rollout engine's :class:`BaseStrategy` (autonomous execution,
+no dataset) with :class:`SyncInferenceConfig` (inline policy call per
+control tick).  The custom observation/action processors convert between
+joint space (robot hardware) and end-effector space (policy I/O) via
+forward/inverse kinematics.
+"""
+
+from lerobot.cameras.opencv import OpenCVCameraConfig
+from lerobot.configs import PreTrainedConfig
+from lerobot.model.kinematics import RobotKinematics
+from lerobot.processor import (
+    RobotProcessorPipeline,
+    observation_to_transition,
+    robot_action_observation_to_transition,
+    transition_to_observation,
+    transition_to_robot_action,
+)
+from lerobot.robots.so_follower import SO100Follower, SO100FollowerConfig
+from lerobot.robots.so_follower.robot_kinematic_processor import (
+    ForwardKinematicsJointsToEE,
+    InverseKinematicsEEToJoints,
+)
+from lerobot.rollout import BaseStrategyConfig, RolloutConfig, build_rollout_context
+from lerobot.rollout.inference import SyncInferenceConfig
+from lerobot.rollout.strategies import BaseStrategy
+from lerobot.types import RobotAction, RobotObservation
+from lerobot.utils.process import ProcessSignalHandler
+from lerobot.utils.utils import init_logging
+
+FPS = 30
+DURATION_SEC = 60
+TASK_DESCRIPTION = "My task description"
+HF_MODEL_ID = "<hf_username>/<model_repo_id>"
+
+
+def main():
+    init_logging()
+
+    # Robot configuration — the rollout engine will connect it inside build_rollout_context.
+    camera_config = {"front": OpenCVCameraConfig(index_or_path=0, width=640, height=480, fps=FPS)}
+    robot_config = SO100FollowerConfig(
+        port="/dev/tty.usbmodem5A460814411",
+        id="my_awesome_follower_arm",
+        cameras=camera_config,
+        use_degrees=True,
+    )
+
+    # Kinematic solver: we need the motor-name list, so peek at the robot once.
+    # (The rollout engine owns the connected instance; we only use this for introspection.)
+    temp_robot = SO100Follower(robot_config)
+    motor_names = list(temp_robot.bus.motors.keys())
+
+    # NOTE: It is highly recommended to use the urdf in the SO-ARM100 repo:
+    #   https://github.com/TheRobotStudio/SO-ARM100/blob/main/Simulation/SO101/so101_new_calib.urdf
+    kinematics_solver = RobotKinematics(
+        urdf_path="./SO101/so101_new_calib.urdf",
+        target_frame_name="gripper_frame_link",
+        joint_names=motor_names,
+    )
+
+    # Joint-space observation → EE-space observation (consumed by the policy).
+    robot_joints_to_ee_pose_processor = RobotProcessorPipeline[RobotObservation, RobotObservation](
+        steps=[ForwardKinematicsJointsToEE(kinematics=kinematics_solver, motor_names=motor_names)],
+        to_transition=observation_to_transition,
+        to_output=transition_to_observation,
+    )
+
+    # EE-space action (produced by the policy) → joint-space action (sent to robot).
+    robot_ee_to_joints_processor = RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction](
+        steps=[
+            InverseKinematicsEEToJoints(
+                kinematics=kinematics_solver,
+                motor_names=motor_names,
+                initial_guess_current_joints=True,
+            ),
+        ],
+        to_transition=robot_action_observation_to_transition,
+        to_output=transition_to_robot_action,
+    )
+
+    # Policy config (full model is loaded inside build_rollout_context).
+    policy_config = PreTrainedConfig.from_pretrained(HF_MODEL_ID)
+    policy_config.pretrained_path = HF_MODEL_ID
+
+    cfg = RolloutConfig(
+        robot=robot_config,
+        policy=policy_config,
+        strategy=BaseStrategyConfig(),
+        inference=SyncInferenceConfig(),
+        fps=FPS,
+        duration=DURATION_SEC,
+        task=TASK_DESCRIPTION,
+    )
+
+    signal_handler = ProcessSignalHandler(use_threads=True)
+
+    # Pass the EE kinematic processors via kwargs; the defaults (identity) would
+    # otherwise skip the joint↔EE conversion and the policy would receive the
+    # wrong observation/action space.
+    ctx = build_rollout_context(
+        cfg,
+        signal_handler.shutdown_event,
+        robot_action_processor=robot_ee_to_joints_processor,
+        robot_observation_processor=robot_joints_to_ee_pose_processor,
+    )
+
+    strategy = BaseStrategy(cfg.strategy)
+    try:
+        strategy.setup(ctx)
+        strategy.run(ctx)
+    finally:
+        strategy.teardown(ctx)
+
+
+if __name__ == "__main__":
+    main()

pyproject.toml

@@ -289,6 +289,7 @@ 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-rollout="lerobot.scripts.lerobot_rollout:main"
 
 # ---------------- Tool Configurations ----------------
 [tool.setuptools.package-data]

src/lerobot/configs/__init__.py

@@ -21,6 +21,7 @@ are intentionally NOT re-exported here to avoid circular dependencies
 Import them directly: ``from lerobot.configs.train import TrainPipelineConfig``
 """
 
+from .dataset import DatasetRecordConfig
 from .default import DatasetConfig, EvalConfig, PeftConfig, WandBConfig
 from .policies import PreTrainedConfig
 from .types import (
@@ -39,6 +40,7 @@ __all__ = [
     "PolicyFeature",
     "RTCAttentionSchedule",
     # Config classes
+    "DatasetRecordConfig",
     "DatasetConfig",
     "EvalConfig",
     "PeftConfig",

src/lerobot/configs/dataset.py

@@ -0,0 +1,80 @@
+# 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.
+
+"""Shared dataset recording configuration used by both ``lerobot-record`` and ``lerobot-rollout``."""
+
+from dataclasses import dataclass
+from datetime import datetime
+from pathlib import Path
+
+
+@dataclass
+class DatasetRecordConfig:
+    # Dataset identifier. By convention it should match '{hf_username}/{dataset_name}' (e.g. `lerobot/test`).
+    repo_id: str = ""
+    # A short but accurate description of the task performed during the recording (e.g. "Pick the Lego block and drop it in the box on the right.")
+    single_task: str = ""
+    # Root directory where the dataset will be stored (e.g. 'dataset/path'). If None, defaults to $HF_LEROBOT_HOME/repo_id.
+    root: str | Path | None = None
+    # Limit the frames per second.
+    fps: int = 30
+    # Number of seconds for data recording for each episode.
+    episode_time_s: int | float = 60
+    # Number of seconds for resetting the environment after each episode.
+    reset_time_s: int | float = 60
+    # Number of episodes to record.
+    num_episodes: int = 50
+    # Encode frames in the dataset into video
+    video: bool = True
+    # Upload dataset to Hugging Face hub.
+    push_to_hub: bool = True
+    # Upload on private repository on the Hugging Face hub.
+    private: bool = False
+    # Add tags to your dataset on the hub.
+    tags: list[str] | None = None
+    # Number of subprocesses handling the saving of frames as PNG. Set to 0 to use threads only;
+    # set to ≥1 to use subprocesses, each using threads to write images. The best number of processes
+    # and threads depends on your system. We recommend 4 threads per camera with 0 processes.
+    # If fps is unstable, adjust the thread count. If still unstable, try using 1 or more subprocesses.
+    num_image_writer_processes: int = 0
+    # Number of threads writing the frames as png images on disk, per camera.
+    # Too many threads might cause unstable teleoperation fps due to main thread being blocked.
+    # Not enough threads might cause low camera fps.
+    num_image_writer_threads_per_camera: int = 4
+    # Number of episodes to record before batch encoding videos
+    # Set to 1 for immediate encoding (default behavior), or higher for batched encoding
+    video_encoding_batch_size: int = 1
+    # Video codec for encoding videos. Options: 'h264', 'hevc', 'libsvtav1', 'auto',
+    # or hardware-specific: 'h264_videotoolbox', 'h264_nvenc', 'h264_vaapi', 'h264_qsv'.
+    # Use 'auto' to auto-detect the best available hardware encoder.
+    vcodec: str = "libsvtav1"
+    # 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
+    # Maximum number of frames to buffer per camera when using streaming encoding.
+    # ~1s buffer at 30fps. Provides backpressure if the encoder can't keep up.
+    encoder_queue_maxsize: int = 30
+    # Number of threads per encoder instance. None = auto (codec default).
+    # Lower values reduce CPU usage, maps to 'lp' (via svtav1-params) for libsvtav1 and 'threads' for h264/hevc..
+    encoder_threads: int | None = None
+
+    def stamp_repo_id(self) -> None:
+        """Append a date-time tag to ``repo_id`` so each recording session gets a unique name.
+
+        Must be called explicitly at dataset *creation* time — not on resume,
+        where the existing ``repo_id`` (already stamped) must be preserved.
+        """
+        if self.repo_id:
+            timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+            self.repo_id = f"{self.repo_id}_{timestamp}"

src/lerobot/datasets/utils.py

@@ -71,8 +71,8 @@ class ForwardCompatibilityError(CompatibilityError):
 
 
 DEFAULT_CHUNK_SIZE = 1000  # Max number of files per chunk
-DEFAULT_DATA_FILE_SIZE_IN_MB = 100  # Max size per file
-DEFAULT_VIDEO_FILE_SIZE_IN_MB = 200  # Max size per file
+DEFAULT_DATA_FILE_SIZE_IN_MB = 50  # Max size per file
+DEFAULT_VIDEO_FILE_SIZE_IN_MB = 100  # Max size per file
 
 INFO_PATH = "meta/info.json"
 STATS_PATH = "meta/stats.json"

src/lerobot/policies/__init__.py

@@ -12,6 +12,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from lerobot.utils.action_interpolator import ActionInterpolator as ActionInterpolator
+
 from .act.configuration_act import ACTConfig as ACTConfig
 from .diffusion.configuration_diffusion import DiffusionConfig as DiffusionConfig
 from .factory import get_policy_class, make_policy, make_policy_config, make_pre_post_processors
@@ -21,7 +23,6 @@ from .pi0.configuration_pi0 import PI0Config as PI0Config
 from .pi0_fast.configuration_pi0_fast import PI0FastConfig as PI0FastConfig
 from .pi05.configuration_pi05 import PI05Config as PI05Config
 from .pretrained import PreTrainedPolicy as PreTrainedPolicy
-from .rtc import ActionInterpolator as ActionInterpolator
 from .sac.configuration_sac import SACConfig as SACConfig
 from .sac.reward_model.configuration_classifier import RewardClassifierConfig as RewardClassifierConfig
 from .sarm.configuration_sarm import SARMConfig as SARMConfig

src/lerobot/policies/rtc/action_interpolator.py

@@ -1,116 +1,4 @@
-# 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.
+# Moved to lerobot.utils.action_interpolator — re-exported for backwards compatibility.
+from lerobot.utils.action_interpolator import ActionInterpolator
 
-"""Action interpolation for smoother robot control.
-
-Provides configurable Nx control rate by interpolating between consecutive actions.
-Useful with RTC and action-chunking policies to reduce jerkiness.
-"""
-
-from torch import Tensor
-
-
-class ActionInterpolator:
-    """Interpolates between consecutive actions for smoother control.
-
-    When enabled with multiplier N, produces N actions per policy action
-    by linearly interpolating between the previous and current action.
-
-    Example with multiplier=3:
-        prev_action -> [1/3 interpolated, 2/3 interpolated, current_action]
-
-    This effectively multiplies the control rate for smoother motion.
-
-    Usage:
-        interpolator = ActionInterpolator(multiplier=2)  # 2x control rate
-
-        # In control loop:
-        if interpolator.needs_new_action():
-            new_action = queue.get()
-            if new_action:
-                interpolator.add(new_action.cpu())
-
-        action = interpolator.get()
-        if action:
-            robot.send_action(action)
-    """
-
-    def __init__(self, multiplier: int = 1):
-        """Initialize the interpolator.
-
-        Args:
-            multiplier: Control rate multiplier (1 = no interpolation, 2 = 2x, 3 = 3x, etc.)
-        """
-        if multiplier < 1:
-            raise ValueError(f"multiplier must be >= 1, got {multiplier}")
-        self.multiplier = multiplier
-        self._prev: Tensor | None = None
-        self._buffer: list[Tensor] = []
-        self._idx = 0
-
-    @property
-    def enabled(self) -> bool:
-        """Whether interpolation is active (multiplier > 1)."""
-        return self.multiplier > 1
-
-    def reset(self):
-        """Reset interpolation state (call between episodes)."""
-        self._prev = None
-        self._buffer = []
-        self._idx = 0
-
-    def needs_new_action(self) -> bool:
-        """Check if a new action is needed from the queue."""
-        return self._idx >= len(self._buffer)
-
-    def add(self, action: Tensor) -> None:
-        """Add a new action and compute interpolated sequence.
-
-        Args:
-            action: New action tensor from policy/queue (already on CPU).
-        """
-        if self.multiplier > 1 and self._prev is not None:
-            self._buffer = []
-            for i in range(1, self.multiplier + 1):
-                t = i / self.multiplier
-                interp = self._prev + t * (action - self._prev)
-                self._buffer.append(interp)
-        else:
-            # First step: no previous action yet, so run at base FPS without interpolation.
-            self._buffer = [action.clone()]
-        self._prev = action.clone()
-        self._idx = 0
-
-    def get(self) -> Tensor | None:
-        """Get the next interpolated action.
-
-        Returns:
-            Next action tensor, or None if buffer is exhausted.
-        """
-        if self._idx >= len(self._buffer):
-            return None
-        action = self._buffer[self._idx]
-        self._idx += 1
-        return action
-
-    def get_control_interval(self, fps: float) -> float:
-        """Get the control interval based on interpolation multiplier.
-
-        Args:
-            fps: Base frames per second.
-
-        Returns:
-            Control interval in seconds (divided by multiplier).
-        """
-        return 1.0 / (fps * self.multiplier)
+__all__ = ["ActionInterpolator"]

src/lerobot/policies/rtc/action_queue.py

@@ -92,10 +92,10 @@ class ActionQueue:
         Returns:
             int: Number of unconsumed actions.
         """
-        if self.queue is None:
-            return 0
-        length = len(self.queue)
-        return length - self.last_index
+        with self.lock:
+            if self.queue is None:
+                return 0
+            return len(self.queue) - self.last_index
 
     def empty(self) -> bool:
         """Check if the queue is empty.
@@ -103,11 +103,10 @@ class ActionQueue:
         Returns:
             bool: True if no actions remain, False otherwise.
         """
-        if self.queue is None:
-            return True
-
-        length = len(self.queue)
-        return length - self.last_index <= 0
+        with self.lock:
+            if self.queue is None:
+                return True
+            return len(self.queue) - self.last_index <= 0
 
     def get_action_index(self) -> int:
         """Get the current action consumption index.
@@ -115,7 +114,8 @@ class ActionQueue:
         Returns:
             int: Index of the next action to be consumed.
         """
-        return self.last_index
+        with self.lock:
+            return self.last_index
 
     def get_left_over(self) -> Tensor | None:
         """Get leftover original actions for RTC prev_chunk_left_over.

src/lerobot/policies/rtc/configuration_rtc.py

@@ -35,7 +35,7 @@ class RTCConfig:
     """
 
     # Infrastructure
-    enabled: bool = False
+    enabled: bool = True
 
     # Core RTC settings
     # Todo change to exp

src/lerobot/rl/actor.py

@@ -76,6 +76,7 @@ from lerobot.transport.utils import (
 )
 from lerobot.types import TransitionKey
 from lerobot.utils.device_utils import get_safe_torch_device
+from lerobot.utils.process import ProcessSignalHandler
 from lerobot.utils.random_utils import set_seed
 from lerobot.utils.robot_utils import precise_sleep
 from lerobot.utils.transition import (
@@ -94,7 +95,6 @@ from .gym_manipulator import (
     make_robot_env,
     step_env_and_process_transition,
 )
-from .process import ProcessSignalHandler
 from .queue import get_last_item_from_queue
 
 # Main entry point

src/lerobot/rl/learner.py

@@ -90,6 +90,7 @@ from lerobot.utils.constants import (
     TRAINING_STATE_DIR,
 )
 from lerobot.utils.device_utils import get_safe_torch_device
+from lerobot.utils.process import ProcessSignalHandler
 from lerobot.utils.random_utils import set_seed
 from lerobot.utils.transition import move_state_dict_to_device, move_transition_to_device
 from lerobot.utils.utils import (
@@ -99,7 +100,6 @@ from lerobot.utils.utils import (
 
 from .buffer import ReplayBuffer, concatenate_batch_transitions
 from .learner_service import MAX_WORKERS, SHUTDOWN_TIMEOUT, LearnerService
-from .process import ProcessSignalHandler
 
 
 @parser.wrap()

src/lerobot/rollout/__init__.py

@@ -0,0 +1,87 @@
+# 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.
+
+"""Policy deployment engine with pluggable rollout strategies."""
+
+from lerobot.utils.import_utils import require_package
+
+require_package("datasets", extra="dataset")
+
+from .configs import (
+    BaseStrategyConfig,
+    DAggerKeyboardConfig,
+    DAggerPedalConfig,
+    DAggerStrategyConfig,
+    HighlightStrategyConfig,
+    RolloutConfig,
+    RolloutStrategyConfig,
+    SentryStrategyConfig,
+)
+from .context import (
+    DatasetContext,
+    HardwareContext,
+    PolicyContext,
+    ProcessorContext,
+    RolloutContext,
+    RuntimeContext,
+    build_rollout_context,
+)
+from .inference import (
+    InferenceEngine,
+    InferenceEngineConfig,
+    RTCInferenceConfig,
+    RTCInferenceEngine,
+    SyncInferenceConfig,
+    SyncInferenceEngine,
+    create_inference_engine,
+)
+from .strategies import (
+    BaseStrategy,
+    DAggerStrategy,
+    HighlightStrategy,
+    RolloutStrategy,
+    SentryStrategy,
+    create_strategy,
+)
+
+__all__ = [
+    "BaseStrategy",
+    "BaseStrategyConfig",
+    "DAggerKeyboardConfig",
+    "DAggerPedalConfig",
+    "DAggerStrategy",
+    "DAggerStrategyConfig",
+    "DatasetContext",
+    "HardwareContext",
+    "HighlightStrategy",
+    "HighlightStrategyConfig",
+    "InferenceEngine",
+    "InferenceEngineConfig",
+    "PolicyContext",
+    "ProcessorContext",
+    "RTCInferenceConfig",
+    "RTCInferenceEngine",
+    "RolloutConfig",
+    "RolloutContext",
+    "RolloutStrategy",
+    "RolloutStrategyConfig",
+    "RuntimeContext",
+    "SentryStrategy",
+    "SentryStrategyConfig",
+    "SyncInferenceConfig",
+    "SyncInferenceEngine",
+    "build_rollout_context",
+    "create_inference_engine",
+    "create_strategy",
+]

src/lerobot/rollout/configs.py

@@ -0,0 +1,317 @@
+# 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.
+
+"""Configuration dataclasses for the rollout deployment engine."""
+
+from __future__ import annotations
+
+import abc
+import logging
+from dataclasses import dataclass, field
+
+import draccus
+
+from lerobot.configs import PreTrainedConfig, parser
+from lerobot.configs.dataset import DatasetRecordConfig
+from lerobot.robots.config import RobotConfig
+from lerobot.teleoperators.config import TeleoperatorConfig
+from lerobot.utils.device_utils import auto_select_torch_device, is_torch_device_available
+
+from .inference import InferenceEngineConfig, SyncInferenceConfig
+
+logger = logging.getLogger(__name__)
+
+
+# ---------------------------------------------------------------------------
+# Strategy configs (polymorphic dispatch via draccus ChoiceRegistry)
+# ---------------------------------------------------------------------------
+
+
+@dataclass
+class RolloutStrategyConfig(draccus.ChoiceRegistry, abc.ABC):
+    """Abstract base for rollout strategy configurations.
+
+    Use ``--strategy.type=<name>`` on the CLI to select a strategy.
+    """
+
+    @property
+    def type(self) -> str:
+        return self.get_choice_name(self.__class__)
+
+
+@RolloutStrategyConfig.register_subclass("base")
+@dataclass
+class BaseStrategyConfig(RolloutStrategyConfig):
+    """Autonomous rollout with no data recording."""
+
+    pass
+
+
+@RolloutStrategyConfig.register_subclass("sentry")
+@dataclass
+class SentryStrategyConfig(RolloutStrategyConfig):
+    """Continuous autonomous rollout with always-on recording.
+
+    Episode duration is derived from camera resolution, FPS, and
+    ``target_video_file_size_mb`` so that each saved episode produces a
+    video file that has crossed the target size.  This aligns episode
+    boundaries with the dataset's video file chunking, so each
+    ``push_to_hub`` call uploads complete video files rather than
+    re-uploading a growing file that hasn't crossed the chunk boundary.
+    """
+
+    upload_every_n_episodes: int = 5
+    # Target video file size in MB for episode rotation.  Episodes are
+    # saved once the estimated video duration would exceed this limit.
+    # Defaults to DEFAULT_VIDEO_FILE_SIZE_IN_MB when set to None.
+    target_video_file_size_mb: float | None = None
+
+
+@RolloutStrategyConfig.register_subclass("highlight")
+@dataclass
+class HighlightStrategyConfig(RolloutStrategyConfig):
+    """Autonomous rollout with on-demand recording via ring buffer.
+
+    A memory-bounded ring buffer continuously captures telemetry.  When
+    the user presses the save key, the buffer contents are flushed to
+    the dataset and live recording continues until the key is pressed
+    again.
+    """
+
+    ring_buffer_seconds: float = 10.0
+    ring_buffer_max_memory_mb: float = 1024.0
+    save_key: str = "s"
+    push_key: str = "h"
+
+
+@dataclass
+class DAggerKeyboardConfig:
+    """Keyboard key bindings for DAgger controls.
+
+    Keys are specified as single characters (e.g. ``"c"``, ``"h"``) or
+    special key names (``"space"``).
+    """
+
+    pause_resume: str = "space"
+    correction: str = "tab"
+    upload: str = "enter"
+
+
+@dataclass
+class DAggerPedalConfig:
+    """Foot pedal configuration for DAgger controls.
+
+    Pedal codes are evdev key code strings (e.g. ``"KEY_A"``).
+    """
+
+    device_path: str = "/dev/input/by-id/usb-PCsensor_FootSwitch-event-kbd"
+    pause_resume: str = "KEY_A"
+    correction: str = "KEY_B"
+    upload: str = "KEY_C"
+
+
+@RolloutStrategyConfig.register_subclass("dagger")
+@dataclass
+class DAggerStrategyConfig(RolloutStrategyConfig):
+    """Human-in-the-loop data collection (DAgger / RaC).
+
+    Alternates between autonomous policy execution and human intervention.
+    Intervention frames are tagged with ``intervention=True``.
+
+    Input is controlled via either a keyboard or foot pedal, selected by
+    ``input_device``.  Each device exposes three actions:
+
+    1. **pause_resume** — toggle policy execution on/off.
+    2. **correction** — toggle human correction recording.
+    3. **upload** — push dataset to hub on demand (corrections-only mode).
+
+    When ``record_autonomous=False`` (default) only human-correction windows
+    are recorded — each correction becomes its own episode.  Set to ``True``
+    to record both autonomous and correction frames with size-based episode
+    rotation (same as Sentry) and background uploading.  ``push_to_hub`` is
+    blocked while a correction is in progress.
+    """
+
+    # Number of correction episodes to collect (corrections-only mode).
+    # When None, falls back to ``--dataset.num_episodes``.
+    num_episodes: int | None = None
+    record_autonomous: bool = False
+    upload_every_n_episodes: int = 5
+    # Target video file size in MB for episode rotation (record_autonomous
+    # mode only).  Defaults to DEFAULT_VIDEO_FILE_SIZE_IN_MB when None.
+    target_video_file_size_mb: float | None = None
+    input_device: str = "keyboard"
+    keyboard: DAggerKeyboardConfig = field(default_factory=DAggerKeyboardConfig)
+    pedal: DAggerPedalConfig = field(default_factory=DAggerPedalConfig)
+
+    def __post_init__(self):
+        if self.input_device not in ("keyboard", "pedal"):
+            raise ValueError(f"DAgger input_device must be 'keyboard' or 'pedal', got '{self.input_device}'")
+
+
+# ---------------------------------------------------------------------------
+# Top-level rollout config
+# ---------------------------------------------------------------------------
+
+
+@dataclass
+class RolloutConfig:
+    """Top-level configuration for the ``lerobot-rollout`` CLI.
+
+    Combines hardware, policy, strategy, and runtime settings.  The
+    ``__post_init__`` method performs fail-fast validation to reject
+    invalid flag combinations early.
+    """
+
+    # Hardware
+    robot: RobotConfig | None = None
+    teleop: TeleoperatorConfig | None = None
+
+    # Policy (loaded from --policy.path via __post_init__)
+    policy: PreTrainedConfig | None = None
+
+    # Strategy (polymorphic: --strategy.type=base|sentry|highlight|dagger)
+    strategy: RolloutStrategyConfig = field(default_factory=BaseStrategyConfig)
+
+    # Inference backend (polymorphic: --inference.type=sync|rtc)
+    inference: InferenceEngineConfig = field(default_factory=SyncInferenceConfig)
+
+    # Dataset (required for sentry, highlight, dagger; None for base)
+    dataset: DatasetRecordConfig | None = None
+
+    # Runtime
+    fps: float = 30.0
+    duration: float = 0.0  # 0 = infinite (24/7 mode)
+    interpolation_multiplier: int = 1
+    device: str | None = None
+    task: str = ""
+    display_data: bool = False
+    # Display data on a remote Rerun server
+    display_ip: str | None = None
+    # Port of the remote Rerun server
+    display_port: int | None = None
+    # Whether to display compressed images in Rerun
+    display_compressed_images: bool = False
+    # Use vocal synthesis to read events
+    play_sounds: bool = True
+    resume: bool = False
+    # Rename map for mapping robot/dataset observation keys to policy keys
+    rename_map: dict[str, str] = field(default_factory=dict)
+
+    # Torch compile
+    use_torch_compile: bool = False
+    torch_compile_backend: str = "inductor"
+    torch_compile_mode: str = "default"
+    compile_warmup_inferences: int = 2
+
+    def __post_init__(self):
+        """Validate config invariants and load the policy config from ``--policy.path``."""
+        # --- Strategy-specific validation ---
+        if isinstance(self.strategy, DAggerStrategyConfig) and self.teleop is None:
+            raise ValueError("DAgger strategy requires --teleop.type to be set")
+
+        # TODO(Steven): DAgger shouldn't require a dataset (user may want to just rollout+intervene without recording), but for now we require it to simplify the implementation.
+        needs_dataset = isinstance(
+            self.strategy, (SentryStrategyConfig, HighlightStrategyConfig, DAggerStrategyConfig)
+        )
+        if needs_dataset and (self.dataset is None or not self.dataset.repo_id):
+            raise ValueError(f"{self.strategy.type} strategy requires --dataset.repo_id to be set")
+
+        if isinstance(self.strategy, BaseStrategyConfig) and self.dataset is not None:
+            raise ValueError(
+                "Base strategy does not record data. Use sentry, highlight, or dagger for recording."
+            )
+
+        # Sentry MUST use streaming encoding to avoid disk I/O blocking the control loop
+        if (
+            isinstance(self.strategy, SentryStrategyConfig)
+            and self.dataset is not None
+            and not self.dataset.streaming_encoding
+        ):
+            logger.warning("Sentry mode forces streaming_encoding=True")
+            self.dataset.streaming_encoding = True
+
+        # Highlight writes frames while the policy is still running, so streaming is mandatory.
+        if (
+            isinstance(self.strategy, HighlightStrategyConfig)
+            and self.dataset is not None
+            and not self.dataset.streaming_encoding
+        ):
+            logger.warning("Highlight mode forces streaming_encoding=True")
+            self.dataset.streaming_encoding = True
+
+        # DAgger: streaming is mandatory only when the autonomous phase is also recorded.
+        if isinstance(self.strategy, DAggerStrategyConfig) and self.dataset is not None:
+            if self.strategy.record_autonomous and not self.dataset.streaming_encoding:
+                logger.warning("DAgger with record_autonomous=True forces streaming_encoding=True")
+                self.dataset.streaming_encoding = True
+            elif not self.strategy.record_autonomous and not self.dataset.streaming_encoding:
+                logger.info(
+                    "Streaming encoding is disabled for DAgger corrections-only mode. "
+                    "Consider enabling it for faster episode saving: "
+                    "--dataset.streaming_encoding=true --dataset.encoder_threads=2"
+                )
+
+        # DAgger: resolve num_episodes from dataset config when not explicitly set.
+        if isinstance(self.strategy, DAggerStrategyConfig) and self.strategy.num_episodes is None:
+            if self.dataset is not None:
+                self.strategy.num_episodes = self.dataset.num_episodes
+                logger.info(
+                    "DAgger num_episodes not set — using --dataset.num_episodes=%d",
+                    self.strategy.num_episodes,
+                )
+            else:
+                raise ValueError(
+                    "DAgger num_episodes must be set either via --strategy.num_episodes or --dataset.num_episodes"
+                )
+
+        # --- Policy loading ---
+        if self.robot is None:
+            raise ValueError("--robot.type is required for rollout")
+
+        policy_path = parser.get_path_arg("policy")
+        if policy_path:
+            cli_overrides = parser.get_cli_overrides("policy")
+            self.policy = PreTrainedConfig.from_pretrained(policy_path, cli_overrides=cli_overrides)
+            self.policy.pretrained_path = policy_path
+        if self.policy is None:
+            raise ValueError("--policy.path is required for rollout")
+
+        # --- Task resolution ---
+        # When any --dataset.* flag is passed, draccus creates a DatasetRecordConfig with single_task="".
+        # If the user set the task via the top-level --task flag, propagate it so that all
+        # downstream consumers (inference engine, dataset frame builders) see it.
+        if self.dataset is not None and not self.dataset.single_task and self.task:
+            logger.info("Propagating top-level task '%s' to dataset config", self.task)
+            self.dataset.single_task = self.task
+        elif self.dataset is not None and self.dataset.single_task and not self.task:
+            logger.info("Propagating dataset single_task '%s' to top-level task", self.dataset.single_task)
+            self.task = self.dataset.single_task
+
+        # --- Device resolution ---
+        # Resolve device from the policy config when not explicitly set so all
+        # components (policy.to, preprocessor, inference engine) use the same
+        # device string instead of inconsistent fallbacks.
+        if self.device is None or not is_torch_device_available(self.device):
+            resolved = self.policy.device
+            if resolved:
+                self.device = resolved
+                logger.info("Resolved device from policy config: %s", self.device)
+            else:
+                self.device = auto_select_torch_device().type
+                logger.info("No policy config to resolve device from; auto-selected device: %s", self.device)
+
+    @classmethod
+    def __get_path_fields__(cls) -> list[str]:
+        return ["policy"]

src/lerobot/rollout/context.py

@@ -0,0 +1,441 @@
+# 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.
+
+"""Rollout context: shared state created once before strategy dispatch.
+
+Grouped into five topical sub-contexts — :class:`RuntimeContext`,
+:class:`HardwareContext`, :class:`PolicyContext`, :class:`ProcessorContext`,
+and :class:`DatasetContext` — assembled into :class:`RolloutContext`.
+"""
+
+from __future__ import annotations
+
+import logging
+from dataclasses import dataclass, field
+from threading import Event
+
+import torch
+
+from lerobot.configs import FeatureType, PreTrainedConfig
+from lerobot.datasets import (
+    LeRobotDataset,
+    aggregate_pipeline_dataset_features,
+    create_initial_features,
+)
+from lerobot.policies import get_policy_class, make_pre_post_processors
+from lerobot.policies.pretrained import PreTrainedPolicy
+from lerobot.processor import (
+    PolicyProcessorPipeline,
+    RobotAction,
+    RobotObservation,
+    RobotProcessorPipeline,
+    make_default_processors,
+    rename_stats,
+)
+from lerobot.robots import make_robot_from_config
+from lerobot.teleoperators import Teleoperator, make_teleoperator_from_config
+from lerobot.utils.feature_utils import combine_feature_dicts, hw_to_dataset_features
+
+from .configs import BaseStrategyConfig, DAggerStrategyConfig, RolloutConfig
+from .inference import (
+    InferenceEngine,
+    RTCInferenceConfig,
+    create_inference_engine,
+)
+from .robot_wrapper import ThreadSafeRobot
+
+logger = logging.getLogger(__name__)
+
+
+def _resolve_action_key_order(
+    policy_action_names: list[str] | None, dataset_action_names: list[str]
+) -> list[str]:
+    """Choose action name ordering for mapping policy tensor outputs to robot action dicts."""
+    if not policy_action_names:
+        return dataset_action_names
+    policy_action_names = list(policy_action_names)
+    if len(policy_action_names) != len(dataset_action_names):
+        logger.warning(
+            "policy.action_feature_names length (%d) != dataset action dim (%d); using dataset order",
+            len(policy_action_names),
+            len(dataset_action_names),
+        )
+        return dataset_action_names
+    if set(dataset_action_names) != set(policy_action_names):
+        logger.warning("policy.action_feature_names keys don't match dataset; using dataset order")
+        return dataset_action_names
+    return policy_action_names
+
+
+# ---------------------------------------------------------------------------
+# Sub-contexts
+# ---------------------------------------------------------------------------
+
+
+@dataclass
+class RuntimeContext:
+    """Runtime knobs shared with every strategy."""
+
+    cfg: RolloutConfig
+    shutdown_event: Event
+
+
+@dataclass
+class HardwareContext:
+    """Connected hardware.
+
+    The raw robot is available via ``robot_wrapper.inner`` when needed
+    (e.g. for disconnect); strategies should otherwise go through the
+    thread-safe wrapper.
+
+    ``initial_position`` stores the robot's joint positions at connect
+    time.  Strategies use it to return the robot to a safe pose before
+    shutting down.
+    """
+
+    robot_wrapper: ThreadSafeRobot
+    teleop: Teleoperator | None
+    initial_position: dict | None = None
+
+
+@dataclass
+class PolicyContext:
+    """Loaded policy and its inference engine."""
+
+    policy: PreTrainedPolicy
+    preprocessor: PolicyProcessorPipeline
+    postprocessor: PolicyProcessorPipeline
+    inference: InferenceEngine
+
+
+@dataclass
+class ProcessorContext:
+    """Robot-side pipelines (run outside the policy)."""
+
+    teleop_action_processor: RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction]
+    robot_action_processor: RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction]
+    robot_observation_processor: RobotProcessorPipeline[RobotObservation, RobotObservation]
+
+
+@dataclass
+class DatasetContext:
+    """Dataset and feature bookkeeping."""
+
+    dataset: LeRobotDataset | None
+    dataset_features: dict = field(default_factory=dict)
+    hw_features: dict = field(default_factory=dict)
+    ordered_action_keys: list[str] = field(default_factory=list)
+
+
+@dataclass
+class RolloutContext:
+    """Bundle of sub-contexts passed to every rollout strategy.
+
+    Built once by :func:`build_rollout_context` before strategy dispatch.
+    """
+
+    runtime: RuntimeContext
+    hardware: HardwareContext
+    policy: PolicyContext
+    processors: ProcessorContext
+    data: DatasetContext
+
+
+# ---------------------------------------------------------------------------
+# Build
+# ---------------------------------------------------------------------------
+
+
+def build_rollout_context(
+    cfg: RolloutConfig,
+    shutdown_event: Event,
+    teleop_action_processor: RobotProcessorPipeline | None = None,
+    robot_action_processor: RobotProcessorPipeline | None = None,
+    robot_observation_processor: RobotProcessorPipeline | None = None,
+) -> RolloutContext:
+    """Wire up policy, processors, hardware, dataset, and inference engine.
+
+    The order is policy-first / hardware-last so a bad ``--policy.path``
+    fails fast without touching the robot.
+    """
+    is_rtc = isinstance(cfg.inference, RTCInferenceConfig)
+
+    # --- 1. Policy (heavy I/O, but no hardware yet) -------------------
+    logger.info("Loading policy from '%s'...", cfg.policy.pretrained_path)
+    policy_config = cfg.policy
+    policy_class = get_policy_class(policy_config.type)
+
+    full_config = PreTrainedConfig.from_pretrained(cfg.policy.pretrained_path)
+    for attr in ("device", "use_amp"):
+        if hasattr(cfg.policy, attr) and hasattr(full_config, attr):
+            cli_val = getattr(cfg.policy, attr)
+            if cli_val is not None:
+                setattr(full_config, attr, cli_val)
+
+    if hasattr(full_config, "compile_model"):
+        full_config.compile_model = cfg.use_torch_compile
+
+    if full_config.type == "vqbet" and cfg.device == "mps":
+        raise NotImplementedError(
+            "Current implementation of VQBeT does not support `mps` backend. "
+            "Please use `cpu` or `cuda` backend."
+        )
+
+    if full_config.use_peft:
+        from peft import PeftConfig, PeftModel
+
+        peft_path = cfg.policy.pretrained_path
+        peft_config = PeftConfig.from_pretrained(peft_path)
+        policy = policy_class.from_pretrained(
+            pretrained_name_or_path=peft_config.base_model_name_or_path, config=full_config
+        )
+        policy = PeftModel.from_pretrained(policy, peft_path, config=peft_config)
+    else:
+        policy = policy_class.from_pretrained(cfg.policy.pretrained_path, config=full_config)
+
+    if is_rtc:
+        policy.config.rtc_config = cfg.inference.rtc
+        if hasattr(policy, "init_rtc_processor"):
+            policy.init_rtc_processor()
+
+    policy = policy.to(cfg.device)
+    policy.eval()
+    logger.info("Policy loaded: type=%s, device=%s", policy_config.type, cfg.device)
+
+    if cfg.use_torch_compile and policy.type not in ("pi0", "pi05"):
+        try:
+            if hasattr(torch, "compile"):
+                compile_kwargs = {
+                    "backend": cfg.torch_compile_backend,
+                    "mode": cfg.torch_compile_mode,
+                    "options": {"triton.cudagraphs": False},
+                }
+                policy.predict_action_chunk = torch.compile(policy.predict_action_chunk, **compile_kwargs)
+                logger.info("torch.compile applied to predict_action_chunk")
+        except Exception as e:
+            logger.warning("Failed to apply torch.compile: %s", e)
+
+    # --- 2. Robot-side processors (user-supplied or defaults) --------
+    if (
+        teleop_action_processor is None
+        or robot_action_processor is None
+        or robot_observation_processor is None
+    ):
+        _t, _r, _o = make_default_processors()
+        teleop_action_processor = teleop_action_processor or _t
+        robot_action_processor = robot_action_processor or _r
+        robot_observation_processor = robot_observation_processor or _o
+
+    # --- 3. Hardware (heaviest side-effect, deferred) -----------------
+    logger.info("Connecting robot (%s)...", cfg.robot.type if cfg.robot else "?")
+    robot = make_robot_from_config(cfg.robot)
+    robot.connect()
+    logger.info("Robot connected: %s", robot.name)
+
+    # Store the initial joint positions so we can return to a safe pose on shutdown.
+    initial_obs = robot.get_observation()
+    initial_position = {k: v for k, v in initial_obs.items() if k.endswith(".pos")}
+    logger.info("Captured initial robot position (%d keys)", len(initial_position))
+
+    robot_wrapper = ThreadSafeRobot(robot)
+
+    teleop = None
+    if cfg.teleop is not None:
+        logger.info("Connecting teleoperator (%s)...", cfg.teleop.type if cfg.teleop else "?")
+        teleop = make_teleoperator_from_config(cfg.teleop)
+        teleop.connect()
+        logger.info("Teleoperator connected")
+
+    # DAgger requires teleop with motor control capabilities (enable_torque,
+    # disable_torque, write_goal_positions).
+    # TODO(Steven): either enforce this (meaning all teleop must implement these methods) or
+    # user is responsible for moving the teleop to the same position as the robot when starting the correction.
+    # if isinstance(cfg.strategy, DAggerStrategyConfig) and teleop is not None:
+    #     required_teleop_methods = ("enable_torque", "disable_torque", "write_goal_positions")
+    #     missing = [m for m in required_teleop_methods if not callable(getattr(teleop, m, None))]
+    #     if missing:
+    #         teleop.disconnect()
+    #         raise ValueError(
+    #             f"DAgger strategy requires a teleoperator with motor control methods "
+    #             f"{required_teleop_methods}. '{type(teleop).__name__}' is missing: {missing}"
+    #         )
+
+    # --- 4. Features + action-key reconciliation ---------------------
+    # TODO(Steven): Only `.pos` joint features are used for policy inference — velocity and
+    # torque channels are observation-only and must be excluded from the state
+    # and action tensors that the policy sees.
+    all_obs_features = robot.observation_features
+    observation_features_hw = {
+        k: v
+        for k, v in all_obs_features.items()
+        if isinstance(v, tuple) or (v is float and k.endswith(".pos"))
+    }
+    action_features_hw = {k: v for k, v in robot.action_features.items() if k.endswith(".pos")}
+
+    # The action side is always needed: sync inference reads action names from
+    # ``dataset_features[ACTION]`` to map policy tensors back to robot actions.
+    action_dataset_features = aggregate_pipeline_dataset_features(
+        pipeline=teleop_action_processor,
+        initial_features=create_initial_features(action=action_features_hw),
+        use_videos=cfg.dataset.video if cfg.dataset else True,
+    )
+    # Observation-side aggregation is needed because of build_dataset_frame
+    observation_dataset_features = aggregate_pipeline_dataset_features(
+        pipeline=robot_observation_processor,
+        initial_features=create_initial_features(observation=observation_features_hw),
+        use_videos=cfg.dataset.video if cfg.dataset else True,
+    )
+    dataset_features = combine_feature_dicts(action_dataset_features, observation_dataset_features)
+    hw_features = hw_to_dataset_features(observation_features_hw, "observation")
+    raw_action_keys = list(action_features_hw.keys())
+    policy_action_names = getattr(policy_config, "action_feature_names", None)
+    ordered_action_keys = _resolve_action_key_order(
+        list(policy_action_names) if policy_action_names else None,
+        raw_action_keys,
+    )
+
+    # Validate visual features if no rename_map is active
+    rename_map = cfg.rename_map
+    if not rename_map:
+        expected_visuals = {k for k, v in full_config.input_features.items() if v.type == FeatureType.VISUAL}
+        provided_visuals = {
+            f"observation.images.{k}" for k, v in robot.observation_features.items() if isinstance(v, tuple)
+        }
+        policy_subset = expected_visuals.issubset(provided_visuals)
+        hw_subset = provided_visuals.issubset(expected_visuals)
+        if not (policy_subset or hw_subset):
+            raise ValueError(
+                f"Visual feature mismatch between policy and robot hardware.\n"
+                f"Policy expects: {expected_visuals}\n"
+                f"Robot provides: {provided_visuals}"
+            )
+
+    # --- 5. Dataset -------------
+    dataset = None
+    if cfg.dataset is not None and not isinstance(cfg.strategy, BaseStrategyConfig):
+        logger.info("Setting up dataset (repo_id=%s)...", cfg.dataset.repo_id)
+        if cfg.resume:
+            dataset = LeRobotDataset.resume(
+                cfg.dataset.repo_id,
+                root=cfg.dataset.root,
+                batch_encoding_size=cfg.dataset.video_encoding_batch_size,
+                vcodec=cfg.dataset.vcodec,
+                streaming_encoding=cfg.dataset.streaming_encoding,
+                encoder_queue_maxsize=cfg.dataset.encoder_queue_maxsize,
+                encoder_threads=cfg.dataset.encoder_threads,
+                image_writer_processes=cfg.dataset.num_image_writer_processes,
+                image_writer_threads=cfg.dataset.num_image_writer_threads_per_camera
+                * len(robot.cameras if hasattr(robot, "cameras") else []),
+            )
+        else:
+            if isinstance(cfg.strategy, DAggerStrategyConfig):
+                dataset_features["intervention"] = {
+                    "dtype": "bool",
+                    "shape": (1,),
+                    "names": None,
+                }
+
+            repo_name = cfg.dataset.repo_id.split("/", 1)[-1]
+            if not repo_name.startswith("rollout_"):
+                raise ValueError(
+                    "Dataset names for rollout must start with 'rollout_'. "
+                    "Use --dataset.repo_id=<user>/rollout_<name> for policy deployment datasets."
+                )
+            cfg.dataset.stamp_repo_id()
+            dataset = LeRobotDataset.create(
+                cfg.dataset.repo_id,
+                cfg.dataset.fps,
+                root=cfg.dataset.root,
+                robot_type=robot.name,
+                features=dataset_features,
+                use_videos=cfg.dataset.video,
+                image_writer_processes=cfg.dataset.num_image_writer_processes,
+                image_writer_threads=cfg.dataset.num_image_writer_threads_per_camera
+                * len(robot.cameras if hasattr(robot, "cameras") else []),
+                batch_encoding_size=cfg.dataset.video_encoding_batch_size,
+                vcodec=cfg.dataset.vcodec,
+                streaming_encoding=cfg.dataset.streaming_encoding,
+                encoder_queue_maxsize=cfg.dataset.encoder_queue_maxsize,
+                encoder_threads=cfg.dataset.encoder_threads,
+            )
+
+    if dataset is not None:
+        logger.info("Dataset ready: %s (%d existing episodes)", dataset.repo_id, dataset.num_episodes)
+
+    # --- 6. Policy pre/post processors (needs dataset stats if any) ---
+    dataset_stats = None
+    if dataset is not None:
+        dataset_stats = rename_stats(
+            dataset.meta.stats,
+            cfg.rename_map,
+        )
+
+    preprocessor, postprocessor = make_pre_post_processors(
+        policy_cfg=policy_config,
+        pretrained_path=cfg.policy.pretrained_path,
+        dataset_stats=dataset_stats,
+        preprocessor_overrides={
+            "device_processor": {"device": cfg.device},
+            "rename_observations_processor": {"rename_map": cfg.rename_map},
+        },
+    )
+
+    # --- 7. Inference strategy (needs policy + pre/post + hardware) --
+    logger.info(
+        "Creating inference engine (type=%s)...",
+        cfg.inference.type if hasattr(cfg.inference, "type") else "sync",
+    )
+    task_str = cfg.dataset.single_task if cfg.dataset else cfg.task
+    inference_strategy = create_inference_engine(
+        cfg.inference,
+        policy=policy,
+        preprocessor=preprocessor,
+        postprocessor=postprocessor,
+        robot_wrapper=robot_wrapper,
+        hw_features=hw_features,
+        dataset_features=dataset_features,
+        ordered_action_keys=ordered_action_keys,
+        task=task_str,
+        fps=cfg.fps,
+        device=cfg.device,
+        use_torch_compile=cfg.use_torch_compile,
+        compile_warmup_inferences=cfg.compile_warmup_inferences,
+        shutdown_event=shutdown_event,
+    )
+
+    # --- 8. Assemble ---------------------------------------------------
+    logger.info("Rollout context assembled successfully")
+    return RolloutContext(
+        runtime=RuntimeContext(cfg=cfg, shutdown_event=shutdown_event),
+        hardware=HardwareContext(
+            robot_wrapper=robot_wrapper, teleop=teleop, initial_position=initial_position
+        ),
+        policy=PolicyContext(
+            policy=policy,
+            preprocessor=preprocessor,
+            postprocessor=postprocessor,
+            inference=inference_strategy,
+        ),
+        processors=ProcessorContext(
+            teleop_action_processor=teleop_action_processor,
+            robot_action_processor=robot_action_processor,
+            robot_observation_processor=robot_observation_processor,
+        ),
+        data=DatasetContext(
+            dataset=dataset,
+            dataset_features=dataset_features,
+            hw_features=hw_features,
+            ordered_action_keys=ordered_action_keys,
+        ),
+    )

src/lerobot/rollout/inference/__init__.py

@@ -0,0 +1,39 @@
+# 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.
+
+"""Inference engine package — backend-agnostic action production.
+
+Concrete strategies (sync, RTC, …) expose the same small interface so
+rollout strategies never branch on the inference backend.
+"""
+
+from .base import InferenceEngine
+from .factory import (
+    InferenceEngineConfig,
+    RTCInferenceConfig,
+    SyncInferenceConfig,
+    create_inference_engine,
+)
+from .rtc import RTCInferenceEngine
+from .sync import SyncInferenceEngine
+
+__all__ = [
+    "InferenceEngine",
+    "InferenceEngineConfig",
+    "RTCInferenceConfig",
+    "RTCInferenceEngine",
+    "SyncInferenceConfig",
+    "SyncInferenceEngine",
+    "create_inference_engine",
+]

src/lerobot/rollout/inference/base.py

@@ -0,0 +1,88 @@
+# 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.
+
+"""Inference engine ABC.
+
+Rollout strategies consume actions through this small interface so they
+do not need to know whether the inference engine is synchronous, runs in
+a background thread (RTC), or comes from an external source.
+"""
+
+from __future__ import annotations
+
+import abc
+
+import torch
+
+
+class InferenceEngine(abc.ABC):
+    """Abstract backend for producing actions during rollout.
+
+    Subclasses decide whether inference happens inline, in a background
+    thread, or externally. The contract is minimal so new backends can
+    be added without touching rollout strategies.
+
+    Lifecycle
+    ---------
+    ``start`` — prepare the backend (e.g. launch a background thread).
+    ``stop`` — shut the backend down cleanly.
+    ``reset`` — clear episode-scoped state (policy hidden state, queues…).
+
+    Action production
+    -----------------
+    ``get_action(obs_frame)`` — return the next action tensor, or
+    ``None`` if none is available (e.g. async queue empty).  Sync
+    backends always compute from ``obs_frame``; async backends may
+    ignore it (they get observations via ``notify_observation``).
+
+    Optional hooks
+    --------------
+    ``notify_observation`` / ``pause`` / ``resume`` have a no-op default
+    so rollout strategies can invoke them unconditionally.
+    """
+
+    @abc.abstractmethod
+    def start(self) -> None:
+        """Initialise the backend."""
+
+    @abc.abstractmethod
+    def stop(self) -> None:
+        """Tear the backend down."""
+
+    @abc.abstractmethod
+    def reset(self) -> None:
+        """Clear episode-scoped state."""
+
+    @abc.abstractmethod
+    def get_action(self, obs_frame: dict | None) -> torch.Tensor | None:
+        """Return the next action tensor, or ``None`` if unavailable."""
+
+    def notify_observation(self, obs: dict) -> None:  # noqa: B027
+        """Publish the latest processed observation.  Default: no-op."""
+
+    def pause(self) -> None:  # noqa: B027
+        """Pause background inference.  Default: no-op."""
+
+    def resume(self) -> None:  # noqa: B027
+        """Resume background inference.  Default: no-op."""
+
+    @property
+    def ready(self) -> bool:
+        """True once the backend can produce actions (e.g. warmup done)."""
+        return True
+
+    @property
+    def failed(self) -> bool:
+        """True if an unrecoverable error occurred in the backend."""
+        return False

src/lerobot/rollout/inference/factory.py

@@ -0,0 +1,129 @@
+# 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.
+
+"""Inference engine configs and factory.
+
+Selection is explicit via ``--inference.type=sync|rtc``.  Adding a new
+backend requires registering its config subclass and dispatching it in
+:func:`create_inference_engine`.
+"""
+
+from __future__ import annotations
+
+import abc
+import logging
+from dataclasses import dataclass, field
+from threading import Event
+
+import draccus
+
+from lerobot.policies.pretrained import PreTrainedPolicy
+from lerobot.policies.rtc.configuration_rtc import RTCConfig
+from lerobot.processor import PolicyProcessorPipeline
+
+from ..robot_wrapper import ThreadSafeRobot
+from .base import InferenceEngine
+from .rtc import RTCInferenceEngine
+from .sync import SyncInferenceEngine
+
+logger = logging.getLogger(__name__)
+
+
+# ---------------------------------------------------------------------------
+# Configs
+# ---------------------------------------------------------------------------
+
+
+@dataclass
+class InferenceEngineConfig(draccus.ChoiceRegistry, abc.ABC):
+    """Abstract base for inference backend configuration.
+
+    Use ``--inference.type=<name>`` on the CLI to select a backend.
+    """
+
+    @property
+    def type(self) -> str:
+        return self.get_choice_name(self.__class__)
+
+
+@InferenceEngineConfig.register_subclass("sync")
+@dataclass
+class SyncInferenceConfig(InferenceEngineConfig):
+    """Inline synchronous inference (one policy call per control tick)."""
+
+
+@InferenceEngineConfig.register_subclass("rtc")
+@dataclass
+class RTCInferenceConfig(InferenceEngineConfig):
+    """Real-Time Chunking: async policy inference in a background thread."""
+
+    # ``RTCConfig`` is a small dataclass with default-only fields, so eagerly
+    # constructing one here costs nothing and keeps draccus' CLI surface flat
+    # (``--inference.rtc.execution_horizon=...`` etc.).  No need to lazy-init.
+    rtc: RTCConfig = field(default_factory=RTCConfig)
+    queue_threshold: int = 30
+
+
+# ---------------------------------------------------------------------------
+# Factory
+# ---------------------------------------------------------------------------
+
+
+def create_inference_engine(
+    config: InferenceEngineConfig,
+    *,
+    policy: PreTrainedPolicy,
+    preprocessor: PolicyProcessorPipeline,
+    postprocessor: PolicyProcessorPipeline,
+    robot_wrapper: ThreadSafeRobot,
+    hw_features: dict,
+    dataset_features: dict,
+    ordered_action_keys: list[str],
+    task: str,
+    fps: float,
+    device: str | None,
+    use_torch_compile: bool = False,
+    compile_warmup_inferences: int = 2,
+    shutdown_event: Event | None = None,
+) -> InferenceEngine:
+    """Instantiate the appropriate inference engine from a config object."""
+    logger.info("Creating inference engine: %s", config.type)
+    if isinstance(config, SyncInferenceConfig):
+        return SyncInferenceEngine(
+            policy=policy,
+            preprocessor=preprocessor,
+            postprocessor=postprocessor,
+            dataset_features=dataset_features,
+            ordered_action_keys=ordered_action_keys,
+            task=task,
+            device=device,
+            robot_type=robot_wrapper.robot_type,
+        )
+    if isinstance(config, RTCInferenceConfig):
+        return RTCInferenceEngine(
+            policy=policy,
+            preprocessor=preprocessor,
+            postprocessor=postprocessor,
+            robot_wrapper=robot_wrapper,
+            rtc_config=config.rtc,
+            hw_features=hw_features,
+            task=task,
+            fps=fps,
+            device=device,
+            use_torch_compile=use_torch_compile,
+            compile_warmup_inferences=compile_warmup_inferences,
+            rtc_queue_threshold=config.queue_threshold,
+            shutdown_event=shutdown_event,
+        )
+    raise ValueError(f"Unknown inference engine type: {type(config).__name__}")

src/lerobot/rollout/inference/rtc.py

@@ -0,0 +1,408 @@
+# 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.
+
+"""Real-Time Chunking inference engine.
+
+A background thread produces action chunks asynchronously via
+:meth:`policy.predict_action_chunk`.  The main control loop polls
+``get_action`` for the next ready action; observations flow the other
+way via ``notify_observation``.
+"""
+
+from __future__ import annotations
+
+import logging
+import math
+import time
+import traceback
+from threading import Event, Lock, Thread
+from typing import Any
+
+import torch
+
+from lerobot.policies.pretrained import PreTrainedPolicy
+from lerobot.policies.rtc import ActionQueue, LatencyTracker
+from lerobot.policies.rtc.configuration_rtc import RTCConfig
+from lerobot.policies.utils import prepare_observation_for_inference
+from lerobot.processor import (
+    NormalizerProcessorStep,
+    PolicyProcessorPipeline,
+    RelativeActionsProcessorStep,
+    TransitionKey,
+    create_transition,
+    to_relative_actions,
+)
+from lerobot.utils.constants import OBS_STATE
+from lerobot.utils.feature_utils import build_dataset_frame
+
+from ..robot_wrapper import ThreadSafeRobot
+from .base import InferenceEngine
+
+logger = logging.getLogger(__name__)
+
+# How long the RTC loop sleeps when paused, idle, or backpressured by a full queue.
+_RTC_IDLE_SLEEP_S: float = 0.01
+# Backoff between transient inference errors (per consecutive failure).
+_RTC_ERROR_RETRY_DELAY_S: float = 0.5
+# Consecutive transient errors tolerated before giving up and propagating shutdown.
+_RTC_MAX_CONSECUTIVE_ERRORS: int = 10
+# Hard timeout for joining the RTC thread on stop().
+_RTC_JOIN_TIMEOUT_S: float = 3.0
+
+
+# ---------------------------------------------------------------------------
+# RTC helpers
+# ---------------------------------------------------------------------------
+
+
+def _reanchor_relative_rtc_prefix(
+    prev_actions_absolute: torch.Tensor,
+    current_state: torch.Tensor,
+    relative_step: RelativeActionsProcessorStep,
+    normalizer_step: NormalizerProcessorStep | None,
+    policy_device: torch.device | str,
+) -> torch.Tensor:
+    """Convert absolute leftover actions into model-space for relative-action RTC policies.
+
+    When using relative actions, the RTC prefix (previous chunk's unexecuted tail)
+    is stored in absolute coordinates. Before feeding it back to the policy, this
+    helper re-expresses those actions relative to the robot's current joint state
+    and optionally normalizes them so the policy receives correctly scaled inputs.
+    """
+    state = current_state.detach().cpu()
+    if state.dim() == 1:
+        state = state.unsqueeze(0)
+
+    action_cpu = prev_actions_absolute.detach().cpu()
+    mask = relative_step._build_mask(action_cpu.shape[-1])
+    relative_actions = to_relative_actions(action_cpu, state, mask)
+
+    transition = create_transition(action=relative_actions)
+    if normalizer_step is not None:
+        transition = normalizer_step(transition)
+
+    return transition[TransitionKey.ACTION].to(policy_device)
+
+
+def _normalize_prev_actions_length(prev_actions: torch.Tensor, target_steps: int) -> torch.Tensor:
+    """Pad or truncate RTC prefix actions to a fixed length for stable compiled inference."""
+    if prev_actions.ndim != 2:
+        raise ValueError(f"Expected 2D [T, A] tensor, got shape={tuple(prev_actions.shape)}")
+    steps, action_dim = prev_actions.shape
+    if steps == target_steps:
+        return prev_actions
+    if steps > target_steps:
+        return prev_actions[:target_steps]
+    padded = torch.zeros((target_steps, action_dim), dtype=prev_actions.dtype, device=prev_actions.device)
+    padded[:steps] = prev_actions
+    return padded
+
+
+def _get_current_raw_state(
+    relative_step: RelativeActionsProcessorStep,
+    fallback_state: torch.Tensor | None,
+) -> torch.Tensor | None:
+    """Return the current raw state cached by the relative-action step.
+
+    ``RelativeActionsProcessorStep`` caches the observation state before any
+    observation normalization. Re-anchoring RTC leftovers must use that raw
+    state rather than the normalized observation that the policy consumes.
+    """
+    if relative_step._last_state is not None:
+        return relative_step._last_state
+    return fallback_state
+
+
+# ---------------------------------------------------------------------------
+# RTCInferenceEngine
+# ---------------------------------------------------------------------------
+
+
+class RTCInferenceEngine(InferenceEngine):
+    """Async RTC inference: a background thread produces action chunks.
+
+    ``get_action`` pops the next action from the shared queue (or
+    returns ``None`` if the queue is empty).  The main loop should call
+    ``notify_observation`` every tick and ``pause``/``resume`` around
+    human-intervention phases.
+    """
+
+    def __init__(
+        self,
+        policy: PreTrainedPolicy,
+        preprocessor: PolicyProcessorPipeline,
+        postprocessor: PolicyProcessorPipeline,
+        robot_wrapper: ThreadSafeRobot,
+        rtc_config: RTCConfig,
+        hw_features: dict,
+        task: str,
+        fps: float,
+        device: str | None,
+        use_torch_compile: bool = False,
+        compile_warmup_inferences: int = 2,
+        rtc_queue_threshold: int = 30,
+        shutdown_event: Event | None = None,
+    ) -> None:
+        self._policy = policy
+        self._preprocessor = preprocessor
+        self._postprocessor = postprocessor
+        self._robot = robot_wrapper
+        self._rtc_config = rtc_config
+        self._hw_features = hw_features
+        self._task = task
+        self._fps = fps
+        self._device = device or "cpu"
+        self._use_torch_compile = use_torch_compile
+        self._compile_warmup_inferences = compile_warmup_inferences
+        self._rtc_queue_threshold = rtc_queue_threshold
+
+        self._action_queue: ActionQueue | None = None
+        self._obs_holder: dict[str, Any] = {}
+        self._obs_lock = Lock()
+        self._policy_active = Event()
+        self._compile_warmup_done = Event()
+        self._shutdown_event = Event()
+        self._rtc_error = Event()
+        self._global_shutdown_event = shutdown_event
+        self._rtc_thread: Thread | None = None
+
+        if not self._use_torch_compile:
+            self._compile_warmup_done.set()
+            logger.info("RTCInferenceEngine initialized (torch.compile disabled, no warmup needed)")
+        else:
+            logger.info(
+                "RTCInferenceEngine initialized (torch.compile enabled, %d warmup inferences)",
+                compile_warmup_inferences,
+            )
+
+        # Processor introspection for relative-action re-anchoring.
+        self._relative_step = next(
+            (s for s in preprocessor.steps if isinstance(s, RelativeActionsProcessorStep) and s.enabled),
+            None,
+        )
+        self._normalizer_step = next(
+            (s for s in preprocessor.steps if isinstance(s, NormalizerProcessorStep)),
+            None,
+        )
+        if self._relative_step is not None:
+            if self._relative_step.action_names is None:
+                cfg_names = getattr(policy.config, "action_feature_names", None)
+                if cfg_names:
+                    self._relative_step.action_names = list(cfg_names)
+                else:
+                    self._relative_step.action_names = [
+                        k for k in robot_wrapper.action_features if k.endswith(".pos")
+                    ]
+            logger.info("Relative actions enabled: RTC prefix will be re-anchored")
+
+    # ------------------------------------------------------------------
+    # Lifecycle
+    # ------------------------------------------------------------------
+
+    @property
+    def ready(self) -> bool:
+        """True once torch.compile warmup is complete (or immediately if compile is disabled)."""
+        return self._compile_warmup_done.is_set()
+
+    @property
+    def failed(self) -> bool:
+        """True if the RTC background thread exited due to an unrecoverable error."""
+        return self._rtc_error.is_set()
+
+    @property
+    def action_queue(self) -> ActionQueue | None:
+        """The shared action queue between the RTC thread and the main loop."""
+        return self._action_queue
+
+    def start(self) -> None:
+        """Launch the RTC background thread."""
+        self._action_queue = ActionQueue(self._rtc_config)
+        self._obs_holder = {
+            "obs": None,
+            "robot_type": self._robot.robot_type,
+        }
+        self._shutdown_event.clear()
+        self._rtc_thread = Thread(
+            target=self._rtc_loop,
+            daemon=True,
+            name="RTCInference",
+        )
+        self._rtc_thread.start()
+        logger.info("RTC inference thread started")
+
+    def stop(self) -> None:
+        """Signal the RTC thread to stop and wait for it."""
+        logger.info("Stopping RTC inference thread...")
+        self._shutdown_event.set()
+        self._policy_active.clear()
+        if self._rtc_thread is not None and self._rtc_thread.is_alive():
+            self._rtc_thread.join(timeout=_RTC_JOIN_TIMEOUT_S)
+            if self._rtc_thread.is_alive():
+                logger.warning("RTC thread did not join within %.1fs", _RTC_JOIN_TIMEOUT_S)
+            else:
+                logger.info("RTC inference thread stopped")
+            self._rtc_thread = None
+
+    def pause(self) -> None:
+        """Pause the RTC background thread."""
+        logger.info("Pausing RTC inference thread")
+        self._policy_active.clear()
+
+    def resume(self) -> None:
+        """Resume the RTC background thread."""
+        logger.info("Resuming RTC inference thread")
+        self._policy_active.set()
+
+    def reset(self) -> None:
+        """Reset the policy, processors, and action queue."""
+        logger.info("Resetting RTC inference state (policy + processors + queue)")
+        self._policy.reset()
+        self._preprocessor.reset()
+        self._postprocessor.reset()
+        if self._action_queue is not None:
+            self._action_queue.clear()
+
+    # ------------------------------------------------------------------
+    # Action production (called from main thread)
+    # ------------------------------------------------------------------
+
+    def get_action(self, obs_frame: dict | None) -> torch.Tensor | None:
+        """Pop the next action from the RTC queue (ignores ``obs_frame``)."""
+        if self._action_queue is None:
+            return None
+        return self._action_queue.get()
+
+    def notify_observation(self, obs: dict) -> None:
+        """Publish the latest observation for the RTC thread to consume."""
+        with self._obs_lock:
+            self._obs_holder["obs"] = obs
+
+    # ------------------------------------------------------------------
+    # RTC: background inference thread
+    # ------------------------------------------------------------------
+
+    def _rtc_loop(self) -> None:
+        """Background thread that generates action chunks via RTC."""
+        try:
+            latency_tracker = LatencyTracker()
+            time_per_chunk = 1.0 / self._fps
+            policy_device = torch.device(self._device)
+
+            warmup_required = max(1, self._compile_warmup_inferences) if self._use_torch_compile else 0
+            inference_count = 0
+            consecutive_errors = 0
+
+            while not self._shutdown_event.is_set():
+                if not self._policy_active.is_set():
+                    time.sleep(_RTC_IDLE_SLEEP_S)
+                    continue
+
+                queue = self._action_queue
+                with self._obs_lock:
+                    obs = self._obs_holder.get("obs")
+                if queue is None or obs is None:
+                    time.sleep(_RTC_IDLE_SLEEP_S)
+                    continue
+
+                if queue.qsize() <= self._rtc_queue_threshold:
+                    try:
+                        current_time = time.perf_counter()
+                        idx_before = queue.get_action_index()
+                        prev_actions = queue.get_left_over()
+
+                        latency = latency_tracker.max()
+                        delay = math.ceil(latency / time_per_chunk) if latency else 0
+
+                        obs_batch = build_dataset_frame(self._hw_features, obs, prefix="observation")
+                        obs_batch = prepare_observation_for_inference(
+                            obs_batch, policy_device, self._task, self._robot.robot_type
+                        )
+                        obs_batch["task"] = [self._task]
+
+                        preprocessed = self._preprocessor(obs_batch)
+
+                        if prev_actions is not None and self._relative_step is not None:
+                            state_tensor = _get_current_raw_state(
+                                self._relative_step, obs_batch.get(OBS_STATE)
+                            )
+                            if state_tensor is not None:
+                                prev_abs = queue.get_processed_left_over()
+                                if prev_abs is not None and prev_abs.numel() > 0:
+                                    prev_actions = _reanchor_relative_rtc_prefix(
+                                        prev_actions_absolute=prev_abs,
+                                        current_state=state_tensor,
+                                        relative_step=self._relative_step,
+                                        normalizer_step=self._normalizer_step,
+                                        policy_device=policy_device,
+                                    )
+
+                        if prev_actions is not None:
+                            prev_actions = _normalize_prev_actions_length(
+                                prev_actions, target_steps=self._rtc_config.execution_horizon
+                            )
+
+                        actions = self._policy.predict_action_chunk(
+                            preprocessed, inference_delay=delay, prev_chunk_left_over=prev_actions
+                        )
+
+                        original = actions.squeeze(0).clone()
+                        processed = self._postprocessor(actions).squeeze(0)
+                        new_latency = time.perf_counter() - current_time
+                        new_delay = math.ceil(new_latency / time_per_chunk)
+
+                        inference_count += 1
+                        consecutive_errors = 0
+                        is_warmup = self._use_torch_compile and inference_count <= warmup_required
+                        if is_warmup:
+                            latency_tracker.reset()
+                        else:
+                            latency_tracker.add(new_latency)
+
+                        queue.merge(original, processed, new_delay, idx_before)
+
+                        if (
+                            is_warmup
+                            and inference_count >= warmup_required
+                            and not self._compile_warmup_done.is_set()
+                        ):
+                            self._compile_warmup_done.set()
+                            logger.info("Compile warmup complete (%d inferences)", inference_count)
+
+                        logger.debug("RTC inference latency=%.2fs, queue=%d", new_latency, queue.qsize())
+
+                    except Exception as e:
+                        consecutive_errors += 1
+                        logger.error(
+                            "RTC inference error (%d/%d): %s",
+                            consecutive_errors,
+                            _RTC_MAX_CONSECUTIVE_ERRORS,
+                            e,
+                        )
+                        logger.debug(traceback.format_exc())
+                        if consecutive_errors >= _RTC_MAX_CONSECUTIVE_ERRORS:
+                            # Persistent failure: stop retrying and propagate shutdown.
+                            raise
+                        time.sleep(_RTC_ERROR_RETRY_DELAY_S)
+                else:
+                    time.sleep(_RTC_IDLE_SLEEP_S)
+
+        except Exception as e:
+            logger.error("Fatal error in RTC thread: %s", e)
+            logger.error(traceback.format_exc())
+            self._rtc_error.set()
+            # Unblock any warmup waiters so the main loop doesn't spin forever
+            self._compile_warmup_done.set()
+            # Signal the top-level shutdown so strategies exit their control loops
+            if self._global_shutdown_event is not None:
+                self._global_shutdown_event.set()

src/lerobot/rollout/inference/sync.py

@@ -0,0 +1,128 @@
+# 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.
+
+"""Synchronous inference engine: inline policy call per control tick."""
+
+from __future__ import annotations
+
+import logging
+from collections import deque
+from contextlib import nullcontext
+from copy import copy
+
+import torch
+
+from lerobot.policies.pretrained import PreTrainedPolicy
+from lerobot.policies.utils import make_robot_action, prepare_observation_for_inference
+from lerobot.processor import PolicyProcessorPipeline
+
+from .base import InferenceEngine
+
+logger = logging.getLogger(__name__)
+
+
+class SyncInferenceEngine(InferenceEngine):
+    """Inline synchronous inference: compute one action per call.
+
+    ``get_action`` runs the full policy pipeline when its local action
+    queue is empty, postprocesses the whole predicted chunk immediately,
+    and then returns one already-postprocessed CPU action at a time.
+    """
+
+    def __init__(
+        self,
+        policy: PreTrainedPolicy,
+        preprocessor: PolicyProcessorPipeline,
+        postprocessor: PolicyProcessorPipeline,
+        dataset_features: dict,
+        ordered_action_keys: list[str],
+        task: str,
+        device: str | None,
+        robot_type: str,
+    ) -> None:
+        self._policy = policy
+        self._preprocessor = preprocessor
+        self._postprocessor = postprocessor
+        self._dataset_features = dataset_features
+        self._ordered_action_keys = ordered_action_keys
+        self._task = task
+        self._device = torch.device(device or "cpu")
+        self._robot_type = robot_type
+        self._processed_action_queue: deque[torch.Tensor] = deque()
+
+        logger.info(
+            "SyncInferenceEngine initialized (device=%s, action_keys=%d)",
+            self._device,
+            len(ordered_action_keys),
+        )
+
+    def start(self) -> None:
+        """No background resources to start."""
+        logger.info("SyncInferenceEngine started (inline mode — no background thread)")
+
+    def stop(self) -> None:
+        """No background resources to stop."""
+        logger.info("SyncInferenceEngine stopped")
+
+    def reset(self) -> None:
+        """Reset the policy and pre/post-processors."""
+        logger.info("Resetting sync inference state (policy + processors)")
+        self._policy.reset()
+        self._preprocessor.reset()
+        self._postprocessor.reset()
+        self._processed_action_queue.clear()
+
+    def _enqueue_processed_chunk(self, action_chunk: torch.Tensor) -> None:
+        """Convert a postprocessed action chunk into ordered per-step CPU tensors."""
+        if action_chunk.ndim == 2:
+            action_chunk = action_chunk.unsqueeze(0)
+
+        n_action_steps = getattr(self._policy.config, "n_action_steps", action_chunk.shape[1])
+        action_chunk = action_chunk[:, : min(n_action_steps, action_chunk.shape[1])]
+
+        for action in action_chunk.squeeze(0):
+            action_tensor = action.cpu()
+            action_dict = make_robot_action(action_tensor, self._dataset_features)
+            ordered_action = torch.tensor(
+                [action_dict[k] for k in self._ordered_action_keys], dtype=action_tensor.dtype
+            )
+            self._processed_action_queue.append(ordered_action)
+
+    def get_action(self, obs_frame: dict | None) -> torch.Tensor | None:
+        """Run the full inference pipeline on ``obs_frame`` and return an action tensor."""
+        if self._processed_action_queue:
+            return self._processed_action_queue.popleft().clone()
+        if obs_frame is None:
+            return None
+        # Shallow copy is intentional: the caller (`send_next_action`) builds
+        # ``obs_frame`` fresh per tick via ``build_dataset_frame``, so the
+        # tensor/array values are not shared with any other reader.
+        observation = copy(obs_frame)
+        autocast_ctx = (
+            torch.autocast(device_type=self._device.type)
+            if self._device.type == "cuda" and self._policy.config.use_amp
+            else nullcontext()
+        )
+        with torch.inference_mode(), autocast_ctx:
+            observation = prepare_observation_for_inference(
+                observation, self._device, self._task, self._robot_type
+            )
+            observation = self._preprocessor(observation)
+            action_chunk = self._policy.predict_action_chunk(observation)
+            processed_chunk = self._postprocessor(action_chunk)
+
+        self._enqueue_processed_chunk(processed_chunk)
+        if not self._processed_action_queue:
+            return None
+        return self._processed_action_queue.popleft().clone()

src/lerobot/rollout/ring_buffer.py

@@ -0,0 +1,112 @@
+# 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.
+
+"""Memory-bounded ring buffer for the Highlight Reel rollout strategy."""
+
+from __future__ import annotations
+
+from collections import deque
+
+import numpy as np
+import torch
+
+
+class RolloutRingBuffer:
+    """Fixed-capacity circular buffer for observation/action frames.
+
+    Stores the last *N* seconds of telemetry in memory, bounded by both
+    time (``max_frames``) and memory (``max_memory_bytes``).  When either
+    limit is reached the oldest frames are evicted.
+
+    .. note::
+       This class is **single-threaded**.  ``append``/``drain``/``clear``
+       must all be called from the same thread (the rollout main loop).
+       Concurrent access from a background thread will corrupt
+       ``_current_bytes`` accounting.
+
+    Parameters
+    ----------
+    max_seconds:
+        Maximum duration of buffered telemetry.
+    max_memory_mb:
+        Hard memory cap in MiB.  Frames are evicted when the estimated
+        total size exceeds this.
+    fps:
+        Frames per second — used to convert ``max_seconds`` to a frame
+        count.
+    """
+
+    def __init__(self, max_seconds: float = 30.0, max_memory_mb: float = 2048.0, fps: float = 30.0) -> None:
+        self._max_frames = int(max_seconds * fps)
+        self._max_bytes = int(max_memory_mb * 1024 * 1024)
+        self._buffer: deque[dict] = deque(maxlen=self._max_frames)
+        self._current_bytes: int = 0
+
+    # ------------------------------------------------------------------
+    # Public API
+    # ------------------------------------------------------------------
+
+    def append(self, frame: dict) -> None:
+        """Add *frame* to the buffer, evicting the oldest if at capacity."""
+        frame_bytes = _estimate_frame_bytes(frame)
+
+        # Evict oldest frames until we are under the memory cap
+        while self._current_bytes + frame_bytes > self._max_bytes and self._buffer:
+            evicted = self._buffer.popleft()
+            self._current_bytes -= _estimate_frame_bytes(evicted)
+
+        self._buffer.append(frame)
+        self._current_bytes += frame_bytes
+
+    def drain(self) -> list[dict]:
+        """Return all buffered frames and clear the buffer."""
+        frames = list(self._buffer)
+        self._buffer.clear()
+        self._current_bytes = 0
+        return frames
+
+    def clear(self) -> None:
+        """Discard all buffered frames."""
+        self._buffer.clear()
+        self._current_bytes = 0
+
+    def __len__(self) -> int:
+        return len(self._buffer)
+
+    @property
+    def estimated_bytes(self) -> int:
+        """Estimated total byte size of all buffered frames."""
+        return self._current_bytes
+
+
+# ------------------------------------------------------------------
+# Helpers
+# ------------------------------------------------------------------
+
+
+def _estimate_frame_bytes(frame: dict) -> int:
+    """Rough byte estimate for a single frame dictionary."""
+    total = 0
+    for v in frame.values():
+        if isinstance(v, torch.Tensor):
+            # ``torch.Tensor`` has no ``nbytes``; compute it explicitly so the
+            # memory cap is honoured even when frames hold unconverted tensors.
+            total += v.nelement() * v.element_size()
+        elif isinstance(v, np.ndarray) or hasattr(v, "nbytes"):
+            total += v.nbytes
+        elif isinstance(v, (int, float)):
+            total += 8
+        elif isinstance(v, (str, bytes)):
+            total += len(v)
+    return max(total, 1)  # avoid zero-size frames

src/lerobot/rollout/robot_wrapper.py

@@ -0,0 +1,79 @@
+# 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.
+
+"""Thread-safe robot wrapper for concurrent observation/action access."""
+
+from __future__ import annotations
+
+from threading import Lock
+from typing import Any
+
+from lerobot.robots import Robot
+
+
+class ThreadSafeRobot:
+    """Lock-protected wrapper around a :class:`Robot` for use with background threads.
+
+    When RTC inference runs in a background thread while the main loop
+    executes actions, both threads may access the robot concurrently.
+    This wrapper serialises ``get_observation`` and ``send_action`` calls.
+
+    Read-only properties are proxied without the lock since they don't
+    mutate hardware state.
+    """
+
+    def __init__(self, robot: Robot) -> None:
+        self._robot = robot
+        self._lock = Lock()
+
+    # -- Lock-protected I/O --------------------------------------------------
+
+    def get_observation(self) -> dict[str, Any]:
+        with self._lock:
+            return self._robot.get_observation()
+
+    def send_action(self, action: dict[str, Any] | Any) -> Any:
+        with self._lock:
+            return self._robot.send_action(action)
+
+    # -- Read-only proxies (no lock needed) -----------------------------------
+
+    @property
+    def observation_features(self) -> dict:
+        return self._robot.observation_features
+
+    @property
+    def action_features(self) -> dict:
+        return self._robot.action_features
+
+    @property
+    def name(self) -> str:
+        return self._robot.name
+
+    @property
+    def robot_type(self) -> str:
+        return self._robot.robot_type
+
+    @property
+    def cameras(self):
+        return getattr(self._robot, "cameras", {})
+
+    @property
+    def is_connected(self) -> bool:
+        return self._robot.is_connected
+
+    @property
+    def inner(self) -> Robot:
+        """Access the underlying robot (e.g. for connect/disconnect)."""
+        return self._robot

src/lerobot/rollout/strategies/__init__.py

@@ -0,0 +1,36 @@
+# 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.
+
+"""Rollout strategies — public API re-exports."""
+
+from .base import BaseStrategy
+from .core import RolloutStrategy, estimate_max_episode_seconds, safe_push_to_hub, send_next_action
+from .dagger import DAggerEvents, DAggerPhase, DAggerStrategy
+from .factory import create_strategy
+from .highlight import HighlightStrategy
+from .sentry import SentryStrategy
+
+__all__ = [
+    "BaseStrategy",
+    "DAggerEvents",
+    "DAggerPhase",
+    "DAggerStrategy",
+    "HighlightStrategy",
+    "RolloutStrategy",
+    "SentryStrategy",
+    "create_strategy",
+    "estimate_max_episode_seconds",
+    "safe_push_to_hub",
+    "send_next_action",
+]

src/lerobot/rollout/strategies/base.py

@@ -0,0 +1,92 @@
+# 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.
+
+"""Base rollout strategy: autonomous policy execution with no data recording."""
+
+from __future__ import annotations
+
+import logging
+import time
+
+from lerobot.utils.robot_utils import precise_sleep
+
+from ..context import RolloutContext
+from .core import RolloutStrategy, send_next_action
+
+logger = logging.getLogger(__name__)
+
+
+class BaseStrategy(RolloutStrategy):
+    """Autonomous policy rollout with no data recording.
+
+    All actions flow through the ``robot_action_processor`` pipeline
+    before reaching the robot.
+    """
+
+    def setup(self, ctx: RolloutContext) -> None:
+        """Initialise the inference engine."""
+        self._init_engine(ctx)
+        logger.info("Base strategy ready")
+
+    def run(self, ctx: RolloutContext) -> None:
+        """Run the autonomous control loop until shutdown or duration expires."""
+        engine = self._engine
+        cfg = ctx.runtime.cfg
+        robot = ctx.hardware.robot_wrapper
+        interpolator = self._interpolator
+
+        control_interval = interpolator.get_control_interval(cfg.fps)
+        observation_interval = 1.0 / cfg.fps
+
+        start_time = time.perf_counter()
+        next_observation_time = 0.0
+        obs = None
+        obs_processed = None
+        engine.resume()
+        logger.info("Base strategy control loop started")
+
+        while not ctx.runtime.shutdown_event.is_set():
+            loop_start = time.perf_counter()
+
+            if cfg.duration > 0 and (time.perf_counter() - start_time) >= cfg.duration:
+                logger.info("Duration limit reached (%.0fs)", cfg.duration)
+                break
+
+            if obs is None or loop_start >= next_observation_time:
+                obs = robot.get_observation()
+                obs_processed = ctx.processors.robot_observation_processor(obs)
+                engine.notify_observation(obs_processed)
+                next_observation_time = loop_start + observation_interval
+
+            if self._handle_warmup(cfg.use_torch_compile, loop_start, control_interval):
+                continue
+
+            if obs_processed is None:
+                continue
+
+            action_dict = send_next_action(obs_processed, obs, ctx, interpolator)
+            self._log_telemetry(obs_processed, action_dict, ctx.runtime)
+
+            dt = time.perf_counter() - loop_start
+            if (sleep_t := control_interval - dt) > 0:
+                precise_sleep(sleep_t)
+            else:
+                logger.warning(
+                    f"Record loop is running slower ({1 / dt:.1f} Hz) than the target FPS ({cfg.fps} Hz). Dataset frames might be dropped and robot control might be unstable. Common causes are: 1) Camera FPS not keeping up 2) Policy inference taking too long 3) CPU starvation"
+                )
+
+    def teardown(self, ctx: RolloutContext) -> None:
+        """Disconnect hardware and stop inference."""
+        self._teardown_hardware(ctx.hardware)
+        logger.info("Base strategy teardown complete")

src/lerobot/rollout/strategies/core.py

@@ -0,0 +1,275 @@
+# 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.
+
+"""Rollout strategy ABC and shared action-dispatch helper."""
+
+from __future__ import annotations
+
+import abc
+import logging
+import time
+from typing import TYPE_CHECKING
+
+from lerobot.datasets.utils import DEFAULT_VIDEO_FILE_SIZE_IN_MB
+from lerobot.utils.action_interpolator import ActionInterpolator
+from lerobot.utils.constants import OBS_STR
+from lerobot.utils.feature_utils import build_dataset_frame
+from lerobot.utils.robot_utils import precise_sleep
+from lerobot.utils.visualization_utils import log_rerun_data
+
+from ..inference import InferenceEngine
+
+if TYPE_CHECKING:
+    from ..configs import RolloutStrategyConfig
+    from ..context import HardwareContext, RolloutContext, RuntimeContext
+
+logger = logging.getLogger(__name__)
+
+
+class RolloutStrategy(abc.ABC):
+    """Abstract base for rollout execution strategies.
+
+    Each concrete strategy implements a self-contained control loop with
+    its own recording/interaction semantics.  Strategies are mutually
+    exclusive — only one runs per session.
+    """
+
+    def __init__(self, config: RolloutStrategyConfig) -> None:
+        self.config = config
+        self._engine: InferenceEngine | None = None
+        self._interpolator: ActionInterpolator | None = None
+        self._warmup_flushed: bool = False
+
+    def _init_engine(self, ctx: RolloutContext) -> None:
+        """Attach the inference engine and action interpolator, then start the backend.
+
+        Creates an :class:`ActionInterpolator` from the config's
+        ``interpolation_multiplier`` and starts the inference engine.
+        Call this from ``setup()`` so strategies share identical
+        initialisation without duplicating code.
+        """
+        self._interpolator = ActionInterpolator(multiplier=ctx.runtime.cfg.interpolation_multiplier)
+        self._engine = ctx.policy.inference
+        logger.info("Starting inference engine...")
+        self._engine.reset()
+        self._engine.start()
+        self._warmup_flushed = False
+        logger.info("Inference engine started")
+
+    def _handle_warmup(self, use_torch_compile: bool, loop_start: float, control_interval: float) -> bool:
+        """Handle torch.compile warmup phase.
+
+        Returns ``True`` if the caller should ``continue`` (still warming
+        up).  On the first post-warmup iteration the engine and
+        interpolator are reset so stale warmup state is discarded.
+        """
+        engine = self._engine
+        interpolator = self._interpolator
+        if not use_torch_compile:
+            return False
+        if not engine.ready:
+            dt = time.perf_counter() - loop_start
+            if (sleep_t := control_interval - dt) > 0:
+                precise_sleep(sleep_t)
+            return True
+        if not self._warmup_flushed:
+            logger.info("Warmup complete — flushing stale state and resuming engine")
+            engine.reset()
+            interpolator.reset()
+            self._warmup_flushed = True
+            engine.resume()
+        return False
+
+    def _teardown_hardware(self, hw: HardwareContext) -> None:
+        """Stop the inference engine, return robot to initial position, and disconnect hardware."""
+        if self._engine is not None:
+            logger.info("Stopping inference engine...")
+            self._engine.stop()
+        robot = hw.robot_wrapper.inner
+        if robot.is_connected:
+            if hw.initial_position:
+                logger.info("Returning robot to initial position before shutdown...")
+                self._return_to_initial_position(hw)
+            logger.info("Disconnecting robot...")
+            robot.disconnect()
+        teleop = hw.teleop
+        if teleop is not None and teleop.is_connected:
+            logger.info("Disconnecting teleoperator...")
+            teleop.disconnect()
+
+    @staticmethod
+    def _return_to_initial_position(hw: HardwareContext, duration_s: float = 3.0, fps: int = 50) -> None:
+        """Smoothly interpolate the robot back to its initial position."""
+        robot = hw.robot_wrapper
+        target = hw.initial_position
+        try:
+            current_obs = robot.get_observation()
+            current_pos = {k: v for k, v in current_obs.items() if k in target}
+            steps = max(int(duration_s * fps), 1)
+            for step in range(1, steps + 1):
+                t = step / steps
+                interp = {}
+                for k in current_pos:
+                    interp[k] = current_pos[k] * (1 - t) + target[k] * t
+                robot.send_action(interp)
+                precise_sleep(1 / fps)
+        except Exception as e:
+            logger.warning("Could not return to initial position: %s", e)
+
+    @staticmethod
+    def _log_telemetry(
+        obs_processed: dict | None,
+        action_dict: dict | None,
+        runtime_ctx: RuntimeContext,
+    ) -> None:
+        """Log observation/action telemetry to Rerun if display_data is enabled."""
+        cfg = runtime_ctx.cfg
+        if not cfg.display_data:
+            return
+        log_rerun_data(
+            observation=obs_processed,
+            action=action_dict,
+            compress_images=cfg.display_compressed_images,
+        )
+
+    @abc.abstractmethod
+    def setup(self, ctx: RolloutContext) -> None:
+        """Strategy-specific initialisation (keyboard listeners, buffers, etc.)."""
+
+    @abc.abstractmethod
+    def run(self, ctx: RolloutContext) -> None:
+        """Main rollout loop.  Returns when shutdown is requested or duration expires."""
+
+    @abc.abstractmethod
+    def teardown(self, ctx: RolloutContext) -> None:
+        """Cleanup: save dataset, stop threads, disconnect hardware."""
+
+
+# ---------------------------------------------------------------------------
+# Shared helpers
+# ---------------------------------------------------------------------------
+
+
+def safe_push_to_hub(dataset, tags=None, private=False) -> bool:
+    """Push dataset to hub, skipping if no episodes have been saved.
+
+    Returns ``True`` if the push was attempted, ``False`` if skipped.
+    """
+    if dataset.num_episodes == 0:
+        logger.warning("No episodes saved — skipping push to hub")
+        return False
+    dataset.push_to_hub(tags=tags, private=private)
+    return True
+
+
+def estimate_max_episode_seconds(
+    dataset_features: dict,
+    fps: float,
+    target_size_mb: float = DEFAULT_VIDEO_FILE_SIZE_IN_MB,
+) -> float:
+    """Conservatively estimate how many seconds of video will exceed *target_size_mb*.
+
+    Each camera produces its own video file, so the episode duration is
+    driven by the **slowest** camera to fill ``target_size_mb`` — i.e.
+    the one with the fewest pixels per frame (lowest bitrate).
+
+    Uses a deliberately **low** bits-per-pixel estimate so the computed
+    duration is *longer* than reality.  By the time the timer fires the
+    actual video file is guaranteed to have crossed the target size,
+    which aligns episode boundaries with the dataset's video-file
+    chunking — each ``push_to_hub`` uploads complete files rather than
+    re-uploading a still-growing one.
+
+    The estimate ignores codec-specific settings (CRF, preset) on purpose:
+    we only need a rough lower bound on bitrate, not a precise prediction.
+
+    Falls back to 600 s (10 min) when no video features are present.
+    """
+    # 0.1 bits-per-pixel is a *low* estimate for CRF-30 streaming video of
+    # robot footage (real-world is typically 0.1 – 0.3 bpp).  Under-
+    # estimating the bitrate over-estimates the time → the episode will be
+    # *larger* than target_size_mb when we save, which is what we want.
+    conservative_bpp = 0.1
+
+    # Collect per-camera pixel counts — each camera has its own video file.
+    camera_pixels = []
+    for feat in dataset_features.values():
+        if feat.get("dtype") == "video":
+            shape = feat.get("shape", ())
+
+            # Assuming shape could be (C, H, W) or (T, C, H, W)
+            # We want to extract the spatial dimensions.
+            if len(shape) >= 3:
+                h, w = shape[-2], shape[-1]
+                pixels = h * w
+                if pixels > 0:
+                    camera_pixels.append(pixels)
+
+    if not camera_pixels:
+        return 600.0
+
+    # Use the smallest camera: it produces the lowest bitrate and therefore
+    # takes the longest to reach the target — the conservative choice.
+    min_pixels = min(camera_pixels)
+    bits_per_frame = min_pixels * conservative_bpp
+    bytes_per_second = (bits_per_frame * fps) / 8
+
+    # Guard against division by zero just in case
+    if bytes_per_second <= 0:
+        return 600.0
+
+    return (target_size_mb * 1024 * 1024) / bytes_per_second
+
+
+# ---------------------------------------------------------------------------
+# Shared action-dispatch helper
+# ---------------------------------------------------------------------------
+
+
+def send_next_action(
+    obs_processed: dict,
+    obs_raw: dict,
+    ctx: RolloutContext,
+    interpolator: ActionInterpolator,
+) -> dict | None:
+    """Dispatch the next action to the robot.
+
+    Pulls the next action tensor from the inference engine, feeds the
+    interpolator, and sends the interpolated action through the
+    ``robot_action_processor`` to the robot.  Works identically for
+    sync and async backends — the rollout strategy never needs to branch.
+
+    Returns the action dict that was sent, or ``None`` if no action was
+    ready (e.g. empty async queue, interpolator not yet primed).
+    """
+    engine = ctx.policy.inference
+    features = ctx.data.dataset_features
+    ordered_keys = ctx.data.ordered_action_keys
+
+    if interpolator.needs_new_action():
+        obs_frame = build_dataset_frame(features, obs_processed, prefix=OBS_STR)
+        action_tensor = engine.get_action(obs_frame)
+        if action_tensor is not None:
+            interpolator.add(action_tensor.cpu())
+
+    interp = interpolator.get()
+    if interp is None:
+        return None
+
+    if len(interp) != len(ordered_keys):
+        raise ValueError(f"Interpolated tensor length ({len(interp)}) != action keys ({len(ordered_keys)})")
+    action_dict = {k: interp[i].item() for i, k in enumerate(ordered_keys)}
+    processed = ctx.processors.robot_action_processor((action_dict, obs_raw))
+    ctx.hardware.robot_wrapper.send_action(processed)
+    return action_dict

src/lerobot/rollout/strategies/dagger.py

@@ -0,0 +1,747 @@
+# 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.
+
+"""DAgger rollout strategy: Human-in-the-Loop data collection.
+
+Implements the RaC paradigm (Recovery and Correction) for interactive
+imitation learning.  Alternates between autonomous policy execution and
+human intervention via teleoperator.
+
+Input is controlled via either a keyboard or foot pedal, selected by
+the ``input_device`` config field.  Each device exposes three actions:
+
+    1. **pause_resume** — Toggle policy execution (AUTONOMOUS <-> PAUSED).
+    2. **correction**   — Toggle correction recording (PAUSED <-> CORRECTING).
+    3. **upload**        — Push dataset to hub on demand (corrections-only mode).
+    ESC (keyboard only)  — Stop session.
+
+Recording Modes:
+    ``record_autonomous=True``:  Sentry-like continuous recording with
+        time-based episode rotation.  Both autonomous and correction
+        frames are recorded; corrections tagged ``intervention=True``.
+    ``record_autonomous=False``: Only correction windows are recorded.
+        Each correction (start to stop) becomes one episode.
+"""
+
+from __future__ import annotations
+
+import contextlib
+import enum
+import logging
+import os
+import sys
+import time
+from concurrent.futures import Future, ThreadPoolExecutor
+from threading import Event, Lock
+from typing import Any
+
+import numpy as np
+
+from lerobot.common.control_utils import is_headless
+from lerobot.datasets import VideoEncodingManager
+from lerobot.datasets.utils import DEFAULT_VIDEO_FILE_SIZE_IN_MB
+from lerobot.teleoperators import Teleoperator
+from lerobot.utils.constants import ACTION, OBS_STR
+from lerobot.utils.feature_utils import build_dataset_frame
+from lerobot.utils.import_utils import _pynput_available
+from lerobot.utils.pedal import start_pedal_listener
+from lerobot.utils.robot_utils import precise_sleep
+from lerobot.utils.utils import log_say
+
+from ..configs import DAggerKeyboardConfig, DAggerPedalConfig, DAggerStrategyConfig
+from ..context import RolloutContext
+from ..robot_wrapper import ThreadSafeRobot
+from .core import RolloutStrategy, estimate_max_episode_seconds, safe_push_to_hub, send_next_action
+
+PYNPUT_AVAILABLE = _pynput_available
+keyboard = None
+if PYNPUT_AVAILABLE:
+    try:
+        if ("DISPLAY" not in os.environ) and ("linux" in sys.platform):
+            logging.info("No DISPLAY set. Skipping pynput import.")
+            PYNPUT_AVAILABLE = False
+        else:
+            from pynput import keyboard
+    except Exception as e:
+        PYNPUT_AVAILABLE = False
+        logging.info(f"Could not import pynput: {e}")
+
+logger = logging.getLogger(__name__)
+
+
+# ---------------------------------------------------------------------------
+# DAgger state machine
+# ---------------------------------------------------------------------------
+
+
+class DAggerPhase(enum.Enum):
+    """Observable phases of a DAgger episode."""
+
+    AUTONOMOUS = "autonomous"  # Policy driving
+    PAUSED = "paused"  # Engine paused, teleop aligned, awaiting input
+    CORRECTING = "correcting"  # Human driving via teleop, recording interventions
+
+
+# Valid (current_phase, event) -> next_phase
+_DAGGER_TRANSITIONS: dict[tuple[DAggerPhase, str], DAggerPhase] = {
+    (DAggerPhase.AUTONOMOUS, "pause_resume"): DAggerPhase.PAUSED,
+    (DAggerPhase.PAUSED, "pause_resume"): DAggerPhase.AUTONOMOUS,
+    (DAggerPhase.PAUSED, "correction"): DAggerPhase.CORRECTING,
+    (DAggerPhase.CORRECTING, "correction"): DAggerPhase.PAUSED,
+}
+
+
+class DAggerEvents:
+    """Thread-safe container for DAgger input device events.
+
+    The keyboard/pedal threads write transition requests; the main loop
+    consumes them.
+    """
+
+    def __init__(self) -> None:
+        self._lock = Lock()
+        self._phase = DAggerPhase.AUTONOMOUS
+        self._pending_transition: str | None = None
+
+        # Session-level flags
+        self.stop_recording = Event()
+        self.upload_requested = Event()
+
+    # -- Thread-safe phase access ------------------------------------------
+
+    @property
+    def phase(self) -> DAggerPhase:
+        """Current phase of the DAgger state machine."""
+        with self._lock:
+            return self._phase
+
+    @phase.setter
+    def phase(self, value: DAggerPhase) -> None:
+        with self._lock:
+            self._phase = value
+
+    def request_transition(self, event: str) -> None:
+        """Request a phase transition (called from keyboard/pedal threads).
+
+        Only enqueues the request if it corresponds to a valid transition
+        from the current phase, preventing impossible state changes.
+        """
+        with self._lock:
+            if (self._phase, event) in _DAGGER_TRANSITIONS:
+                self._pending_transition = event
+
+    def consume_transition(self) -> tuple[DAggerPhase, DAggerPhase] | None:
+        """Consume a pending transition (called from main loop)."""
+        with self._lock:
+            if self._pending_transition is None:
+                return None
+            key = (self._phase, self._pending_transition)
+            self._pending_transition = None
+            new_phase = _DAGGER_TRANSITIONS.get(key)
+            if new_phase is None:
+                return None
+            old_phase = self._phase
+            self._phase = new_phase
+            return old_phase, new_phase
+
+    def reset(self) -> None:
+        """Reset all transient state for a fresh session."""
+        with self._lock:
+            self._phase = DAggerPhase.AUTONOMOUS
+            self._pending_transition = None
+        self.upload_requested.clear()
+
+
+# ---------------------------------------------------------------------------
+# Teleoperator helpers
+# ---------------------------------------------------------------------------
+
+
+# TODO(Steven): either enforce this (meaning all teleop must implement these methods) or
+# user is responsible for moving the teleop to the same position as the robot when starting the correction.
+def _teleop_smooth_move_to(
+    teleop: Teleoperator, target_pos: dict, duration_s: float = 2.0, fps: int = 50
+) -> None:
+    """Smoothly move teleop to target position via linear interpolation.
+
+    Requires the teleoperator to support motor control methods
+    (``enable_torque``, ``write_goal_positions``, ``get_action``).
+    """
+    teleop.enable_torque()
+    current = teleop.get_action()
+    steps = max(int(duration_s * fps), 1)
+
+    for step in range(steps + 1):
+        t = step / steps
+        interp = {}
+        for k in current:
+            if k in target_pos:
+                interp[k] = current[k] * (1 - t) + target_pos[k] * t
+            else:
+                interp[k] = current[k]
+        teleop.write_goal_positions(interp)
+        time.sleep(1 / fps)
+
+
+# ---------------------------------------------------------------------------
+# Input device handlers
+# ---------------------------------------------------------------------------
+
+
+def _init_dagger_keyboard(events: DAggerEvents, cfg: DAggerKeyboardConfig):
+    """Initialise keyboard listener with DAgger 3-key controls.
+
+    Returns the pynput Listener (or ``None`` in headless mode or when
+    pynput is unavailable).
+    """
+    if not PYNPUT_AVAILABLE or is_headless():
+        logger.warning("Headless environment or pynput unavailable — keyboard controls disabled")
+        return None
+
+    # Map config key names to pynput Key objects for special keys
+    special_keys = {
+        "space": keyboard.Key.space,
+        "tab": keyboard.Key.tab,
+        "enter": keyboard.Key.enter,
+    }
+
+    def _resolve_key(key) -> str | None:
+        """Resolve a pynput key event to a config-comparable string."""
+        if key == keyboard.Key.esc:
+            return "esc"
+        for name, pynput_key in special_keys.items():
+            if key == pynput_key:
+                return name
+        if hasattr(key, "char") and key.char:
+            return key.char
+        return None
+
+    # Build mapping: resolved key string -> DAgger event name
+    key_to_event = {
+        cfg.pause_resume: "pause_resume",
+        cfg.correction: "correction",
+    }
+
+    def on_press(key):
+        try:
+            resolved = _resolve_key(key)
+            if resolved is None:
+                return
+            if resolved == "esc":
+                logger.info("Stop recording...")
+                events.stop_recording.set()
+                return
+            if resolved in key_to_event:
+                events.request_transition(key_to_event[resolved])
+            if resolved == cfg.upload:
+                events.upload_requested.set()
+        except Exception as e:
+            logger.debug("Key error: %s", e)
+
+    listener = keyboard.Listener(on_press=on_press)
+    listener.start()
+    logger.info(
+        "DAgger keyboard listener started (pause_resume='%s', correction='%s', upload='%s', ESC=stop)",
+        cfg.pause_resume,
+        cfg.correction,
+        cfg.upload,
+    )
+    return listener
+
+
+def _init_dagger_pedal(events: DAggerEvents, cfg: DAggerPedalConfig):
+    """Initialise foot pedal listener with DAgger 3-pedal controls.
+
+    Returns the pedal listener thread (or ``None`` if evdev is unavailable).
+    """
+    code_to_event = {
+        cfg.pause_resume: "pause_resume",
+        cfg.correction: "correction",
+    }
+
+    def on_press(code: str) -> None:
+        if code in code_to_event:
+            events.request_transition(code_to_event[code])
+        if code == cfg.upload:
+            events.upload_requested.set()
+
+    logger.info("Initializing DAgger foot pedal listener (device=%s)", cfg.device_path)
+    return start_pedal_listener(on_press, device_path=cfg.device_path)
+
+
+# ---------------------------------------------------------------------------
+# DAgger Strategy
+# ---------------------------------------------------------------------------
+
+
+class DAggerStrategy(RolloutStrategy):
+    """Human-in-the-Loop data collection with intervention tagging.
+
+    State machine::
+
+        AUTONOMOUS --(key1)--> PAUSED --(key2)--> CORRECTING --(key2)--> PAUSED
+                               --(key1)--> AUTONOMOUS
+
+    Recording modes:
+        ``record_autonomous=True``: Sentry-like continuous recording with
+            time-based episode rotation.  Intervention frames tagged True.
+        ``record_autonomous=False``: Only correction windows recorded.
+            Each correction = one episode.  Upload on demand via key3.
+    """
+
+    config: DAggerStrategyConfig
+
+    def __init__(self, config: DAggerStrategyConfig):
+        super().__init__(config)
+        self._listener = None
+        self._pedal_thread = None
+        self._events = DAggerEvents()
+        self._push_executor: ThreadPoolExecutor | None = None
+        self._pending_push: Future | None = None
+        self._needs_push = Event()
+        self._episode_lock = Lock()
+
+    def setup(self, ctx: RolloutContext) -> None:
+        """Initialise the inference engine and input device listener."""
+        self._init_engine(ctx)
+        self._push_executor = ThreadPoolExecutor(max_workers=1, thread_name_prefix="dagger-push")
+        target_mb = self.config.target_video_file_size_mb or DEFAULT_VIDEO_FILE_SIZE_IN_MB
+        self._episode_duration_s = estimate_max_episode_seconds(
+            ctx.data.dataset_features, ctx.runtime.cfg.fps, target_size_mb=target_mb
+        )
+
+        if self.config.input_device == "keyboard":
+            self._listener = _init_dagger_keyboard(self._events, self.config.keyboard)
+        else:
+            self._pedal_thread = _init_dagger_pedal(self._events, self.config.pedal)
+
+        record_mode = "all frames (sentry-like)" if self.config.record_autonomous else "corrections only"
+        logger.info(
+            "DAgger strategy ready (input=%s, episodes=%d, record=%s, episode_duration=%.0fs)",
+            self.config.input_device,
+            self.config.num_episodes,
+            record_mode,
+            self._episode_duration_s,
+        )
+
+    def run(self, ctx: RolloutContext) -> None:
+        """Run DAgger episodes with human-in-the-loop intervention."""
+        if self.config.record_autonomous:
+            self._run_continuous(ctx)
+        else:
+            self._run_corrections_only(ctx)
+
+    def teardown(self, ctx: RolloutContext) -> None:
+        """Stop listeners, finalise the dataset, and disconnect hardware."""
+        play_sounds = ctx.runtime.cfg.play_sounds
+        logger.info("Stopping DAgger recording")
+        log_say("Stopping DAgger recording", play_sounds)
+
+        if self._listener is not None and not is_headless():
+            logger.info("Stopping keyboard listener")
+            self._listener.stop()
+
+        # Flush any queued/running push cleanly
+        if self._push_executor is not None:
+            logger.info("Shutting down push executor (waiting for pending pushes)...")
+            self._push_executor.shutdown(wait=True)
+            self._push_executor = None
+
+        if ctx.data.dataset is not None:
+            logger.info("Finalizing dataset...")
+            ctx.data.dataset.finalize()
+            if self._needs_push.is_set() and ctx.runtime.cfg.dataset and ctx.runtime.cfg.dataset.push_to_hub:
+                logger.info("Pushing final dataset to hub...")
+                if safe_push_to_hub(
+                    ctx.data.dataset,
+                    tags=ctx.runtime.cfg.dataset.tags,
+                    private=ctx.runtime.cfg.dataset.private,
+                ):
+                    logger.info("Dataset uploaded to hub")
+                    log_say("Dataset uploaded to hub", play_sounds)
+
+        self._teardown_hardware(ctx.hardware)
+        logger.info("DAgger strategy teardown complete")
+
+    # ------------------------------------------------------------------
+    # Continuous recording mode (record_autonomous=True)
+    # ------------------------------------------------------------------
+
+    def _run_continuous(self, ctx: RolloutContext) -> None:
+        """Sentry-like continuous recording with intervention tagging.
+
+        Episodes are auto-rotated every ``episode_time_s`` seconds and
+        uploaded in the background every ``upload_every_n_episodes`` episodes.
+        Both autonomous and correction frames are recorded; corrections are
+        tagged with ``intervention=True``.
+        """
+        engine = self._engine
+        cfg = ctx.runtime.cfg
+        robot = ctx.hardware.robot_wrapper
+        teleop = ctx.hardware.teleop
+        dataset = ctx.data.dataset
+        events = self._events
+        interpolator = self._interpolator
+        features = ctx.data.dataset_features
+
+        control_interval = interpolator.get_control_interval(cfg.fps)
+        record_stride = max(1, cfg.interpolation_multiplier)
+        task_str = cfg.dataset.single_task if cfg.dataset else cfg.task
+        play_sounds = cfg.play_sounds
+
+        engine.reset()
+        interpolator.reset()
+        events.reset()
+        # TODO(Steven): either enforce this (meaning all teleop must implement these methods) or
+        # user is responsible for moving the teleop to the same position as the robot when starting the correction.
+        # teleop.disable_torque()
+        engine.resume()
+
+        last_action: dict[str, Any] | None = None
+        record_tick = 0
+        start_time = time.perf_counter()
+        episode_start = time.perf_counter()
+        episodes_since_push = 0
+        episode_duration_s = self._episode_duration_s
+        logger.info("DAgger continuous recording started (episode_duration=%.0fs)", episode_duration_s)
+
+        with VideoEncodingManager(dataset):
+            try:
+                while not events.stop_recording.is_set() and not ctx.runtime.shutdown_event.is_set():
+                    loop_start = time.perf_counter()
+
+                    if cfg.duration > 0 and (time.perf_counter() - start_time) >= cfg.duration:
+                        logger.info("Duration limit reached (%.0fs)", cfg.duration)
+                        break
+
+                    # Process transitions
+                    transition = events.consume_transition()
+                    if transition is not None:
+                        old_phase, new_phase = transition
+                        self._apply_transition(old_phase, new_phase, engine, interpolator, robot, teleop)
+                        last_action = None
+
+                    phase = events.phase
+                    obs = robot.get_observation()
+                    obs_processed = ctx.processors.robot_observation_processor(obs)
+                    obs_frame = build_dataset_frame(features, obs_processed, prefix=OBS_STR)
+
+                    # --- CORRECTING: human teleop control ---
+                    if phase == DAggerPhase.CORRECTING:
+                        teleop_action = teleop.get_action()
+                        processed_teleop = ctx.processors.teleop_action_processor((teleop_action, obs))
+                        robot_action_to_send = ctx.processors.robot_action_processor((processed_teleop, obs))
+                        robot.send_action(robot_action_to_send)
+                        last_action = robot_action_to_send
+                        self._log_telemetry(obs_processed, processed_teleop, ctx.runtime)
+                        action_frame = build_dataset_frame(features, processed_teleop, prefix=ACTION)
+                        if record_tick % record_stride == 0:
+                            frame = {
+                                **obs_frame,
+                                **action_frame,
+                                "task": task_str,
+                                "intervention": np.array([True], dtype=bool),
+                            }
+                            dataset.add_frame(frame)
+                        record_tick += 1
+
+                    # --- PAUSED: hold position ---
+                    elif phase == DAggerPhase.PAUSED:
+                        if last_action:
+                            robot.send_action(last_action)
+
+                    # --- AUTONOMOUS: policy control ---
+                    else:
+                        engine.notify_observation(obs_processed)
+
+                        if self._handle_warmup(cfg.use_torch_compile, loop_start, control_interval):
+                            continue
+
+                        action_dict = send_next_action(obs_processed, obs, ctx, interpolator)
+                        if action_dict is not None:
+                            self._log_telemetry(obs_processed, action_dict, ctx.runtime)
+                            last_action = ctx.processors.robot_action_processor((action_dict, obs))
+                            action_frame = build_dataset_frame(features, action_dict, prefix=ACTION)
+                            if record_tick % record_stride == 0:
+                                frame = {
+                                    **obs_frame,
+                                    **action_frame,
+                                    "task": task_str,
+                                    "intervention": np.array([False], dtype=bool),
+                                }
+                                dataset.add_frame(frame)
+                            record_tick += 1
+
+                    # Episode rotation derived from video file-size target.
+                    # Do NOT save mid-correction — wait for the correction
+                    # to finish so the episode boundary is clean.
+                    elapsed = time.perf_counter() - episode_start
+                    if elapsed >= episode_duration_s and phase != DAggerPhase.CORRECTING:
+                        with self._episode_lock:
+                            dataset.save_episode()
+                        episodes_since_push += 1
+                        self._needs_push.set()
+                        logger.info(
+                            "Episode saved (total: %d, elapsed: %.1fs)",
+                            dataset.num_episodes,
+                            elapsed,
+                        )
+                        log_say(f"Episode {dataset.num_episodes} saved", play_sounds)
+
+                        if episodes_since_push >= self.config.upload_every_n_episodes:
+                            self._background_push(dataset, cfg)
+                            episodes_since_push = 0
+
+                        episode_start = time.perf_counter()
+
+                    dt = time.perf_counter() - loop_start
+                    if (sleep_t := control_interval - dt) > 0:
+                        precise_sleep(sleep_t)
+                    else:
+                        logger.warning(
+                            f"Record loop is running slower ({1 / dt:.1f} Hz) than the target FPS ({cfg.fps} Hz). Dataset frames might be dropped and robot control might be unstable. Common causes are: 1) Camera FPS not keeping up 2) Policy inference taking too long 3) CPU starvation"
+                        )
+
+            finally:
+                logger.info("DAgger continuous control loop ended — pausing engine")
+                engine.pause()
+                # TODO(Steven): either enforce this (meaning all teleop must implement these methods) or
+                # user is responsible for moving the teleop to the same position as the robot when starting the correction.
+                # teleop.disable_torque()
+                with contextlib.suppress(Exception):
+                    with self._episode_lock:
+                        dataset.save_episode()
+                    self._needs_push.set()
+                    logger.info("Final in-progress episode saved")
+
+    # ------------------------------------------------------------------
+    # Corrections-only mode (record_autonomous=False)
+    # ------------------------------------------------------------------
+
+    def _run_corrections_only(self, ctx: RolloutContext) -> None:
+        """Record only human correction windows.  Each correction = one episode.
+
+        The policy runs autonomously without recording.  When the user
+        pauses and starts a correction, frames are recorded with
+        ``intervention=True``.  Stopping the correction saves the episode.
+        The dataset can be uploaded on demand via the upload key/pedal.
+        """
+        engine = self._engine
+        cfg = ctx.runtime.cfg
+        robot = ctx.hardware.robot_wrapper
+        teleop = ctx.hardware.teleop
+        dataset = ctx.data.dataset
+        events = self._events
+        interpolator = self._interpolator
+        features = ctx.data.dataset_features
+
+        control_interval = interpolator.get_control_interval(cfg.fps)
+        record_stride = max(1, cfg.interpolation_multiplier)
+        task_str = cfg.dataset.single_task if cfg.dataset else cfg.task
+        play_sounds = cfg.play_sounds
+
+        engine.reset()
+        interpolator.reset()
+        events.reset()
+        # TODO(Steven): either enforce this (meaning all teleop must implement these methods) or
+        # user is responsible for moving the teleop to the same position as the robot when starting the correction.
+        # teleop.disable_torque()
+        engine.resume()
+
+        last_action: dict[str, Any] | None = None
+        start_time = time.perf_counter()
+        record_tick = 0
+        recorded = 0
+        logger.info(
+            "DAgger corrections-only recording started (target: %d episodes)", self.config.num_episodes
+        )
+
+        with VideoEncodingManager(dataset):
+            try:
+                while (
+                    recorded < self.config.num_episodes
+                    and not events.stop_recording.is_set()
+                    and not ctx.runtime.shutdown_event.is_set()
+                ):
+                    loop_start = time.perf_counter()
+
+                    if cfg.duration > 0 and (time.perf_counter() - start_time) >= cfg.duration:
+                        logger.info("Duration limit reached (%.0fs)", cfg.duration)
+                        break
+
+                    # Process transitions
+                    transition = events.consume_transition()
+                    if transition is not None:
+                        old_phase, new_phase = transition
+                        self._apply_transition(old_phase, new_phase, engine, interpolator, robot, teleop)
+                        last_action = None
+
+                        # Correction ended -> save episode (blocking if not streaming)
+                        if old_phase == DAggerPhase.CORRECTING and new_phase == DAggerPhase.PAUSED:
+                            with self._episode_lock:
+                                dataset.save_episode()
+                            recorded += 1
+                            self._needs_push.set()
+                            logger.info(
+                                "Correction %d/%d saved",
+                                recorded,
+                                self.config.num_episodes,
+                            )
+                            log_say(f"Correction {recorded} saved", play_sounds)
+
+                    # On-demand upload
+                    if events.upload_requested.is_set():
+                        events.upload_requested.clear()
+                        logger.info("Upload requested by user")
+                        self._background_push(dataset, cfg)
+
+                    phase = events.phase
+                    obs = robot.get_observation()
+                    obs_processed = ctx.processors.robot_observation_processor(obs)
+
+                    # --- CORRECTING: human teleop control + recording ---
+                    if phase == DAggerPhase.CORRECTING:
+                        teleop_action = teleop.get_action()
+                        processed_teleop = ctx.processors.teleop_action_processor((teleop_action, obs))
+                        robot_action_to_send = ctx.processors.robot_action_processor((processed_teleop, obs))
+                        robot.send_action(robot_action_to_send)
+                        last_action = robot_action_to_send
+                        self._log_telemetry(obs_processed, processed_teleop, ctx.runtime)
+
+                        obs_frame = build_dataset_frame(features, obs_processed, prefix=OBS_STR)
+                        action_frame = build_dataset_frame(features, processed_teleop, prefix=ACTION)
+                        if record_tick % record_stride == 0:
+                            dataset.add_frame(
+                                {
+                                    **obs_frame,
+                                    **action_frame,
+                                    "task": task_str,
+                                    "intervention": np.array([True], dtype=bool),
+                                }
+                            )
+                        record_tick += 1
+
+                    # --- PAUSED: hold position ---
+                    elif phase == DAggerPhase.PAUSED:
+                        if last_action:
+                            robot.send_action(last_action)
+
+                    # --- AUTONOMOUS: policy control (no recording) ---
+                    else:
+                        engine.notify_observation(obs_processed)
+
+                        if self._handle_warmup(cfg.use_torch_compile, loop_start, control_interval):
+                            continue
+
+                        action_dict = send_next_action(obs_processed, obs, ctx, interpolator)
+                        if action_dict is not None:
+                            self._log_telemetry(obs_processed, action_dict, ctx.runtime)
+                            last_action = ctx.processors.robot_action_processor((action_dict, obs))
+
+                    dt = time.perf_counter() - loop_start
+                    if (sleep_t := control_interval - dt) > 0:
+                        precise_sleep(sleep_t)
+                    else:
+                        logger.warning(
+                            f"Record loop is running slower ({1 / dt:.1f} Hz) than the target FPS ({cfg.fps} Hz). Dataset frames might be dropped and robot control might be unstable. Common causes are: 1) Camera FPS not keeping up 2) Policy inference taking too long 3) CPU starvation"
+                        )
+
+            finally:
+                logger.info("DAgger corrections-only loop ended — pausing engine")
+                engine.pause()
+                # TODO(Steven): either enforce this (meaning all teleop must implement these methods) or
+                # user is responsible for moving the teleop to the same position as the robot when starting the correction.
+                # teleop.disable_torque()
+                with contextlib.suppress(Exception):
+                    with self._episode_lock:
+                        dataset.save_episode()
+                    self._needs_push.set()
+                    logger.info("Final in-progress episode saved")
+
+    # ------------------------------------------------------------------
+    # State-machine transition side-effects
+    # ------------------------------------------------------------------
+
+    @staticmethod
+    def _apply_transition(
+        old_phase: DAggerPhase,
+        new_phase: DAggerPhase,
+        engine,
+        interpolator,
+        robot: ThreadSafeRobot,
+        teleop: Teleoperator,
+    ) -> None:
+        """Execute side-effects for a validated phase transition."""
+        logger.info("Phase transition: %s -> %s", old_phase.value, new_phase.value)
+        if old_phase == DAggerPhase.AUTONOMOUS and new_phase == DAggerPhase.PAUSED:
+            logger.info("Pausing engine — robot holds position")
+            engine.pause()
+            obs = robot.get_observation()
+            _robot_pos = {
+                k: v for k, v in obs.items() if k.endswith(".pos") and k in robot.observation_features
+            }
+            # TODO(Steven): either enforce this (meaning all teleop must implement these methods) or
+            # user is responsible for moving the teleop to the same position as the robot when starting the correction.
+            # Consider also a method that moves the robot to the teleop smoothly (similar to what we do at HW shutdown).
+            # _teleop_smooth_move_to(teleop, robot_pos, duration_s=2.0, fps=50)
+
+        elif new_phase == DAggerPhase.CORRECTING:
+            logger.info("Entering correction mode — human teleop control")
+            # TODO(Steven): either enforce this (meaning all teleop must implement these methods) or
+            # user is responsible for moving the teleop to the same position as the robot when starting the correction.
+            # teleop.disable_torque()
+
+        elif new_phase == DAggerPhase.AUTONOMOUS:
+            logger.info("Resuming autonomous mode — resetting engine and interpolator")
+            interpolator.reset()
+            engine.reset()
+            engine.resume()
+
+    # ------------------------------------------------------------------
+    # Background push (shared by both modes)
+    # ------------------------------------------------------------------
+
+    def _background_push(self, dataset, cfg) -> None:
+        """Queue a Hub push on the single-worker executor.
+
+        The executor's max_workers=1 guarantees at most one push runs at
+        a time; submitted tasks are queued rather than dropped.  Pushes
+        are blocked while the operator is mid-correction to avoid
+        uploading a partially-recorded episode.
+        """
+        if self._push_executor is None:
+            return
+
+        if self._events.phase == DAggerPhase.CORRECTING:
+            logger.info("Skipping push — correction in progress")
+            return
+
+        if self._pending_push is not None and not self._pending_push.done():
+            logger.info("Previous push still in progress; queueing next")
+
+        def _push():
+            try:
+                with self._episode_lock:
+                    if safe_push_to_hub(
+                        dataset,
+                        tags=cfg.dataset.tags if cfg.dataset else None,
+                        private=cfg.dataset.private if cfg.dataset else False,
+                    ):
+                        self._needs_push.clear()
+                        logger.info("Background push to hub complete")
+            except Exception as e:
+                logger.error("Background push failed: %s", e)
+
+        self._pending_push = self._push_executor.submit(_push)
+        logger.info("Background push task submitted")

src/lerobot/rollout/strategies/factory.py

@@ -0,0 +1,45 @@
+# 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.
+
+"""Strategy factory: config type-name → strategy class dispatch."""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+from .base import BaseStrategy
+from .core import RolloutStrategy
+from .dagger import DAggerStrategy
+from .highlight import HighlightStrategy
+from .sentry import SentryStrategy
+
+if TYPE_CHECKING:
+    from ..configs import RolloutStrategyConfig
+
+
+def create_strategy(config: RolloutStrategyConfig) -> RolloutStrategy:
+    """Instantiate the appropriate strategy from a config object.
+
+    Dispatches on ``config.type`` (the name registered via
+    ``draccus.ChoiceRegistry``).
+    """
+    if config.type == "base":
+        return BaseStrategy(config)
+    if config.type == "sentry":
+        return SentryStrategy(config)
+    if config.type == "highlight":
+        return HighlightStrategy(config)
+    if config.type == "dagger":
+        return DAggerStrategy(config)
+    raise ValueError(f"Unknown strategy type '{config.type}'. Available: base, sentry, highlight, dagger")

src/lerobot/rollout/strategies/highlight.py

@@ -0,0 +1,281 @@
+# 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.
+
+"""Highlight Reel strategy: on-demand recording via ring buffer."""
+
+from __future__ import annotations
+
+import contextlib
+import logging
+import os
+import sys
+import time
+from concurrent.futures import Future, ThreadPoolExecutor
+from threading import Event as ThreadingEvent, Lock
+
+from lerobot.common.control_utils import is_headless
+from lerobot.datasets import VideoEncodingManager
+from lerobot.utils.constants import ACTION, OBS_STR
+from lerobot.utils.feature_utils import build_dataset_frame
+from lerobot.utils.import_utils import _pynput_available, require_package
+from lerobot.utils.robot_utils import precise_sleep
+from lerobot.utils.utils import log_say
+
+from ..configs import HighlightStrategyConfig
+from ..context import RolloutContext
+from ..ring_buffer import RolloutRingBuffer
+from .core import RolloutStrategy, safe_push_to_hub, send_next_action
+
+PYNPUT_AVAILABLE = _pynput_available
+keyboard = None
+if PYNPUT_AVAILABLE:
+    try:
+        if ("DISPLAY" not in os.environ) and ("linux" in sys.platform):
+            logging.info("No DISPLAY set. Skipping pynput import.")
+            PYNPUT_AVAILABLE = False
+        else:
+            from pynput import keyboard
+    except Exception as e:
+        PYNPUT_AVAILABLE = False
+        logging.info(f"Could not import pynput: {e}")
+
+logger = logging.getLogger(__name__)
+
+
+class HighlightStrategy(RolloutStrategy):
+    """Autonomous rollout with on-demand recording via ring buffer.
+
+    The robot runs autonomously while a memory-bounded ring buffer
+    captures continuous telemetry.  When the user presses the save key:
+
+    1. The ring buffer is flushed to the dataset (last *Z* seconds).
+    2. Live recording continues until the save key is pressed again.
+    3. The episode is saved and the ring buffer resumes capturing.
+
+    Requires ``streaming_encoding=True`` (enforced in config validation)
+    so that ``dataset.add_frame`` is a non-blocking queue put — draining
+    900 frames stays sub-ms per frame.
+    """
+
+    config: HighlightStrategyConfig
+
+    def __init__(self, config: HighlightStrategyConfig):
+        super().__init__(config)
+        require_package("pynput", extra="pynput-dep")
+        self._ring: RolloutRingBuffer | None = None
+        self._listener = None
+        self._save_requested = ThreadingEvent()
+        self._recording_live = ThreadingEvent()
+        self._push_requested = ThreadingEvent()
+        self._push_executor: ThreadPoolExecutor | None = None
+        self._pending_push: Future | None = None
+        self._episode_lock = Lock()
+
+    def setup(self, ctx: RolloutContext) -> None:
+        """Initialise the inference engine, ring buffer, and keyboard listener."""
+        self._init_engine(ctx)
+
+        self._ring = RolloutRingBuffer(
+            max_seconds=self.config.ring_buffer_seconds,
+            max_memory_mb=self.config.ring_buffer_max_memory_mb,
+            fps=ctx.runtime.cfg.fps,
+        )
+
+        self._push_executor = ThreadPoolExecutor(max_workers=1, thread_name_prefix="highlight-push")
+        logger.info(
+            "Ring buffer initialized (max_seconds=%.0f, max_memory=%.0fMB)",
+            self.config.ring_buffer_seconds,
+            self.config.ring_buffer_max_memory_mb,
+        )
+        self._setup_keyboard(ctx.runtime.shutdown_event)
+        logger.info(
+            "Highlight strategy ready (buffer=%.0fs, save='%s', push='%s')",
+            self.config.ring_buffer_seconds,
+            self.config.save_key,
+            self.config.push_key,
+        )
+
+    def run(self, ctx: RolloutContext) -> None:
+        """Run the autonomous loop, buffering frames and recording on demand."""
+        engine = self._engine
+        cfg = ctx.runtime.cfg
+        robot = ctx.hardware.robot_wrapper
+        dataset = ctx.data.dataset
+        ring = self._ring
+        interpolator = self._interpolator
+        features = ctx.data.dataset_features
+
+        control_interval = interpolator.get_control_interval(cfg.fps)
+
+        engine.resume()
+        play_sounds = cfg.play_sounds
+
+        start_time = time.perf_counter()
+        task_str = cfg.dataset.single_task if cfg.dataset else cfg.task
+        logger.info("Highlight strategy recording started (press '%s' to save)", self.config.save_key)
+
+        with VideoEncodingManager(dataset):
+            try:
+                while not ctx.runtime.shutdown_event.is_set():
+                    loop_start = time.perf_counter()
+
+                    if cfg.duration > 0 and (time.perf_counter() - start_time) >= cfg.duration:
+                        logger.info("Duration limit reached (%.0fs)", cfg.duration)
+                        break
+
+                    obs = robot.get_observation()
+                    obs_processed = ctx.processors.robot_observation_processor(obs)
+                    engine.notify_observation(obs_processed)
+
+                    if self._handle_warmup(cfg.use_torch_compile, loop_start, control_interval):
+                        continue
+
+                    action_dict = send_next_action(obs_processed, obs, ctx, interpolator)
+
+                    if action_dict is not None:
+                        self._log_telemetry(obs_processed, action_dict, ctx.runtime)
+                        obs_frame = build_dataset_frame(features, obs_processed, prefix=OBS_STR)
+                        action_frame = build_dataset_frame(features, action_dict, prefix=ACTION)
+                        frame = {**obs_frame, **action_frame, "task": task_str}
+
+                        # NOTE: ``is_set()`` then ``clear()`` is not atomic
+                        # against the keyboard thread setting the flag again
+                        # in between — but that is benign: we lose at most one
+                        # toggle, processed on the next iteration.
+                        if self._save_requested.is_set():
+                            self._save_requested.clear()
+                            if not self._recording_live.is_set():
+                                logger.info(
+                                    "Flushing ring buffer (%d frames) + starting live recording",
+                                    len(ring),
+                                )
+                                for buffered_frame in ring.drain():
+                                    dataset.add_frame(buffered_frame)
+                                self._recording_live.set()
+                            else:
+                                dataset.add_frame(frame)
+                                with self._episode_lock:
+                                    dataset.save_episode()
+                                logger.info("Episode saved (total: %d)", dataset.num_episodes)
+                                log_say(
+                                    f"Episode {dataset.num_episodes} saved",
+                                    play_sounds,
+                                )
+                                self._recording_live.clear()
+                                continue  # frame already consumed — skip ring.append
+
+                        if self._push_requested.is_set():
+                            self._push_requested.clear()
+                            logger.info("Push requested by user")
+                            self._background_push(dataset, cfg)
+
+                        if self._recording_live.is_set():
+                            dataset.add_frame(frame)
+                        else:
+                            ring.append(frame)
+
+                    dt = time.perf_counter() - loop_start
+                    if (sleep_t := control_interval - dt) > 0:
+                        precise_sleep(sleep_t)
+                    else:
+                        logger.warning(
+                            f"Record loop is running slower ({1 / dt:.1f} Hz) than the target FPS ({cfg.fps} Hz). Dataset frames might be dropped and robot control might be unstable. Common causes are: 1) Camera FPS not keeping up 2) Policy inference taking too long 3) CPU starvation"
+                        )
+
+            finally:
+                logger.info("Highlight control loop ended")
+                if self._recording_live.is_set():
+                    logger.info("Saving in-progress live episode")
+                    with contextlib.suppress(Exception), self._episode_lock:
+                        dataset.save_episode()
+
+    def teardown(self, ctx: RolloutContext) -> None:
+        """Stop listeners, finalise the dataset, and disconnect hardware."""
+        play_sounds = ctx.runtime.cfg.play_sounds
+        logger.info("Stopping highlight recording")
+        log_say("Stopping highlight recording", play_sounds)
+
+        if self._listener is not None:
+            logger.info("Stopping keyboard listener")
+            self._listener.stop()
+
+        if self._push_executor is not None:
+            logger.info("Shutting down push executor (waiting for pending pushes)...")
+            self._push_executor.shutdown(wait=True)
+            self._push_executor = None
+
+        if ctx.data.dataset is not None:
+            logger.info("Finalizing dataset...")
+            ctx.data.dataset.finalize()
+            if ctx.runtime.cfg.dataset and ctx.runtime.cfg.dataset.push_to_hub:
+                logger.info("Pushing final dataset to hub...")
+                if safe_push_to_hub(
+                    ctx.data.dataset,
+                    tags=ctx.runtime.cfg.dataset.tags,
+                    private=ctx.runtime.cfg.dataset.private,
+                ):
+                    logger.info("Dataset uploaded to hub")
+                    log_say("Dataset uploaded to hub", play_sounds)
+
+        self._teardown_hardware(ctx.hardware)
+        logger.info("Highlight strategy teardown complete")
+
+    def _setup_keyboard(self, shutdown_event: ThreadingEvent) -> None:
+        """Set up keyboard listener for save and push keys."""
+        if is_headless():
+            logger.warning("Headless environment — highlight keys unavailable")
+            return
+
+        try:
+            save_key = self.config.save_key
+            push_key = self.config.push_key
+
+            def on_press(key):
+                with contextlib.suppress(Exception):
+                    if hasattr(key, "char") and key.char == save_key:
+                        self._save_requested.set()
+                    elif hasattr(key, "char") and key.char == push_key:
+                        self._push_requested.set()
+                    elif key == keyboard.Key.esc:
+                        self._save_requested.clear()
+                        shutdown_event.set()
+
+            self._listener = keyboard.Listener(on_press=on_press)
+            self._listener.start()
+            logger.info("Keyboard listener started (save='%s', push='%s', ESC=stop)", save_key, push_key)
+        except ImportError:
+            logger.warning("pynput not available — keyboard listener disabled")
+
+    def _background_push(self, dataset, cfg) -> None:
+        """Queue a Hub push on the single-worker executor."""
+        if self._push_executor is None:
+            return
+
+        if self._pending_push is not None and not self._pending_push.done():
+            logger.info("Previous push still in progress; queueing next")
+
+        def _push():
+            try:
+                with self._episode_lock:
+                    if safe_push_to_hub(
+                        dataset,
+                        tags=cfg.dataset.tags if cfg.dataset else None,
+                        private=cfg.dataset.private if cfg.dataset else False,
+                    ):
+                        logger.info("Background push to hub complete")
+            except Exception as e:
+                logger.error("Background push failed: %s", e)
+
+        self._pending_push = self._push_executor.submit(_push)
+        logger.info("Background push task submitted")

src/lerobot/rollout/strategies/sentry.py

@@ -0,0 +1,229 @@
+# 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.
+
+"""Sentry rollout strategy: continuous autonomous recording with auto-upload."""
+
+from __future__ import annotations
+
+import contextlib
+import logging
+import time
+from concurrent.futures import Future, ThreadPoolExecutor
+from threading import Event, Lock
+
+from lerobot.datasets import VideoEncodingManager
+from lerobot.datasets.utils import DEFAULT_VIDEO_FILE_SIZE_IN_MB
+from lerobot.utils.constants import ACTION, OBS_STR
+from lerobot.utils.feature_utils import build_dataset_frame
+from lerobot.utils.robot_utils import precise_sleep
+from lerobot.utils.utils import log_say
+
+from ..configs import SentryStrategyConfig
+from ..context import RolloutContext
+from .core import RolloutStrategy, estimate_max_episode_seconds, safe_push_to_hub, send_next_action
+
+logger = logging.getLogger(__name__)
+
+
+class SentryStrategy(RolloutStrategy):
+    """Continuous autonomous rollout with always-on recording.
+
+    Episode duration is derived from camera resolution, FPS, and
+    ``DEFAULT_VIDEO_FILE_SIZE_IN_MB`` so that each saved episode
+    produces a video file that has crossed the chunk-size boundary.
+    This keeps ``push_to_hub`` efficient — it uploads complete video
+    files rather than re-uploading a still-growing one.
+
+    The dataset is pushed to the Hub via a bounded single-worker executor
+    so no push is ever silently dropped and exactly one push runs at a
+    time.
+
+    Policy state (hidden state, RTC queue) intentionally persists across
+    episode boundaries — Sentry slices one continuous rollout, the robot
+    does not reset between slices.
+
+    Requires ``streaming_encoding=True`` (enforced in config validation)
+    to prevent disk I/O from blocking the control loop.
+    """
+
+    config: SentryStrategyConfig
+
+    def __init__(self, config: SentryStrategyConfig):
+        super().__init__(config)
+        self._push_executor: ThreadPoolExecutor | None = None
+        self._pending_push: Future | None = None
+        self._needs_push = Event()
+        self._episode_lock = Lock()
+
+    def setup(self, ctx: RolloutContext) -> None:
+        """Initialise the inference engine and background push executor."""
+        self._init_engine(ctx)
+        self._push_executor = ThreadPoolExecutor(max_workers=1, thread_name_prefix="sentry-push")
+        target_mb = self.config.target_video_file_size_mb or DEFAULT_VIDEO_FILE_SIZE_IN_MB
+        self._episode_duration_s = estimate_max_episode_seconds(
+            ctx.data.dataset_features, ctx.runtime.cfg.fps, target_size_mb=target_mb
+        )
+        logger.info(
+            "Sentry strategy ready (episode_duration=%.0fs, upload_every=%d eps)",
+            self._episode_duration_s,
+            self.config.upload_every_n_episodes,
+        )
+
+    def run(self, ctx: RolloutContext) -> None:
+        """Run the continuous recording loop with automatic episode rotation."""
+        engine = self._engine
+        cfg = ctx.runtime.cfg
+        robot = ctx.hardware.robot_wrapper
+        dataset = ctx.data.dataset
+        interpolator = self._interpolator
+        features = ctx.data.dataset_features
+
+        control_interval = interpolator.get_control_interval(cfg.fps)
+
+        engine.resume()
+        play_sounds = cfg.play_sounds
+        episode_duration_s = self._episode_duration_s
+
+        start_time = time.perf_counter()
+        episode_start = time.perf_counter()
+        episodes_since_push = 0
+        task_str = cfg.dataset.single_task if cfg.dataset else cfg.task
+        logger.info("Sentry recording started (episode_duration=%.0fs)", episode_duration_s)
+
+        with VideoEncodingManager(dataset):
+            try:
+                while not ctx.runtime.shutdown_event.is_set():
+                    loop_start = time.perf_counter()
+
+                    if cfg.duration > 0 and (time.perf_counter() - start_time) >= cfg.duration:
+                        logger.info("Duration limit reached (%.0fs)", cfg.duration)
+                        break
+
+                    obs = robot.get_observation()
+                    obs_processed = ctx.processors.robot_observation_processor(obs)
+                    engine.notify_observation(obs_processed)
+
+                    if self._handle_warmup(cfg.use_torch_compile, loop_start, control_interval):
+                        continue
+
+                    action_dict = send_next_action(obs_processed, obs, ctx, interpolator)
+
+                    if action_dict is not None:
+                        self._log_telemetry(obs_processed, action_dict, ctx.runtime)
+                        obs_frame = build_dataset_frame(features, obs_processed, prefix=OBS_STR)
+                        action_frame = build_dataset_frame(features, action_dict, prefix=ACTION)
+                        frame = {**obs_frame, **action_frame, "task": task_str}
+                        # ``add_frame`` writes to the in-progress episode buffer; the
+                        # background pusher only ever touches *finalised* episode
+                        # artifacts on disk.  The two operate on disjoint state, so
+                        # ``add_frame`` does not need ``_episode_lock``.
+                        dataset.add_frame(frame)
+
+                    # Episode rotation derived from video file-size target.
+                    # The duration is a conservative estimate so the actual
+                    # video has crossed DEFAULT_VIDEO_FILE_SIZE_IN_MB by now,
+                    # keeping push_to_hub efficient (uploads complete files).
+                    elapsed = time.perf_counter() - episode_start
+                    if elapsed >= episode_duration_s:
+                        # ``save_episode`` finalises the in-progress episode and
+                        # flushes it to disk; ``_episode_lock`` serialises this with
+                        # ``push_to_hub`` (run in the background executor) so the
+                        # pusher never reads a half-written episode.
+                        with self._episode_lock:
+                            dataset.save_episode()
+                        episodes_since_push += 1
+                        self._needs_push.set()
+                        logger.info(
+                            "Episode saved (total: %d, elapsed: %.1fs)",
+                            dataset.num_episodes,
+                            elapsed,
+                        )
+                        log_say(f"Episode {dataset.num_episodes} saved", play_sounds)
+
+                        if episodes_since_push >= self.config.upload_every_n_episodes:
+                            self._background_push(dataset, cfg)
+                            episodes_since_push = 0
+
+                        episode_start = time.perf_counter()
+
+                    dt = time.perf_counter() - loop_start
+                    if (sleep_t := control_interval - dt) > 0:
+                        precise_sleep(sleep_t)
+                    else:
+                        logger.warning(
+                            f"Record loop is running slower ({1 / dt:.1f} Hz) than the target FPS ({cfg.fps} Hz). Dataset frames might be dropped and robot control might be unstable. Common causes are: 1) Camera FPS not keeping up 2) Policy inference taking too long 3) CPU starvation"
+                        )
+
+            finally:
+                logger.info("Sentry control loop ended — saving final episode")
+                with contextlib.suppress(Exception):
+                    with self._episode_lock:
+                        dataset.save_episode()
+                    self._needs_push.set()
+
+    def teardown(self, ctx: RolloutContext) -> None:
+        """Flush pending pushes, finalise the dataset, and disconnect hardware."""
+        play_sounds = ctx.runtime.cfg.play_sounds
+        logger.info("Stopping sentry recording")
+        log_say("Stopping sentry recording", play_sounds)
+
+        # Flush any queued/running push cleanly.
+        if self._push_executor is not None:
+            logger.info("Shutting down push executor (waiting for pending pushes)...")
+            self._push_executor.shutdown(wait=True)
+            self._push_executor = None
+
+        if ctx.data.dataset is not None:
+            logger.info("Finalizing dataset...")
+            ctx.data.dataset.finalize()
+            if self._needs_push.is_set() and ctx.runtime.cfg.dataset and ctx.runtime.cfg.dataset.push_to_hub:
+                logger.info("Pushing final dataset to hub...")
+                if safe_push_to_hub(
+                    ctx.data.dataset,
+                    tags=ctx.runtime.cfg.dataset.tags,
+                    private=ctx.runtime.cfg.dataset.private,
+                ):
+                    logger.info("Dataset uploaded to hub")
+                    log_say("Dataset uploaded to hub", play_sounds)
+
+        self._teardown_hardware(ctx.hardware)
+        logger.info("Sentry strategy teardown complete")
+
+    def _background_push(self, dataset, cfg) -> None:
+        """Queue a Hub push on the single-worker executor.
+
+        The executor's max_workers=1 guarantees at most one push runs at
+        a time; submitted tasks are queued rather than dropped.
+        """
+        if self._push_executor is None:
+            return
+
+        if self._pending_push is not None and not self._pending_push.done():
+            logger.info("Previous push still in progress; queueing next")
+
+        def _push():
+            try:
+                with self._episode_lock:
+                    if safe_push_to_hub(
+                        dataset,
+                        tags=cfg.dataset.tags if cfg.dataset else None,
+                        private=cfg.dataset.private if cfg.dataset else False,
+                    ):
+                        self._needs_push.clear()
+                        logger.info("Background push to hub complete")
+            except Exception as e:
+                logger.error("Background push failed: %s", e)
+
+        self._pending_push = self._push_executor.submit(_push)
+        logger.info("Background push task submitted")

src/lerobot/scripts/lerobot_record.py

@@ -13,70 +13,62 @@
 # limitations under the License.
 
 """
-Records a dataset. Actions for the robot can be either generated by teleoperation or by a policy.
+Records a dataset via teleoperation.  This is a pure data-collection
+tool — no policy inference.  For deploying trained policies, use
+``lerobot-rollout`` instead.
 
 Requires: pip install 'lerobot[core_scripts]'  (includes dataset + hardware + viz extras)
 
 Example:
 
 ```shell
-lerobot-record \
-    --robot.type=so100_follower \
-    --robot.port=/dev/tty.usbmodem58760431541 \
-    --robot.cameras="{laptop: {type: opencv, index_or_path: 0, width: 640, height: 480, fps: 30}}" \
-    --robot.id=black \
-    --dataset.repo_id=<my_username>/<my_dataset_name> \
-    --dataset.num_episodes=2 \
-    --dataset.single_task="Grab the cube" \
-    --dataset.streaming_encoding=true \
-    --dataset.encoder_threads=2 \
+lerobot-record \\
+    --robot.type=so100_follower \\
+    --robot.port=/dev/tty.usbmodem58760431541 \\
+    --robot.cameras="{laptop: {type: opencv, index_or_path: 0, width: 640, height: 480, fps: 30}}" \\
+    --robot.id=black \\
+    --teleop.type=so100_leader \\
+    --teleop.port=/dev/tty.usbmodem58760431551 \\
+    --teleop.id=blue \\
+    --dataset.repo_id=<my_username>/<my_dataset_name> \\
+    --dataset.num_episodes=2 \\
+    --dataset.single_task="Grab the cube" \\
+    --dataset.streaming_encoding=true \\
+    --dataset.encoder_threads=2 \\
     --display_data=true
-    # <- Optional: specify video codec (auto, h264, hevc, libsvtav1). Default is libsvtav1. \
-    # --dataset.vcodec=h264 \
-    # <- Teleop optional if you want to teleoperate to record or in between episodes with a policy \
-    # --teleop.type=so100_leader \
-    # --teleop.port=/dev/tty.usbmodem58760431551 \
-    # --teleop.id=blue \
-    # <- Policy optional if you want to record with a policy \
-    # --policy.path=${HF_USER}/my_policy \
 ```
 
 Example recording with bimanual so100:
 ```shell
-lerobot-record \
-  --robot.type=bi_so_follower \
-  --robot.left_arm_config.port=/dev/tty.usbmodem5A460822851 \
-  --robot.right_arm_config.port=/dev/tty.usbmodem5A460814411 \
-  --robot.id=bimanual_follower \
+lerobot-record \\
+  --robot.type=bi_so_follower \\
+  --robot.left_arm_config.port=/dev/tty.usbmodem5A460822851 \\
+  --robot.right_arm_config.port=/dev/tty.usbmodem5A460814411 \\
+  --robot.id=bimanual_follower \\
   --robot.left_arm_config.cameras='{
     wrist: {"type": "opencv", "index_or_path": 1, "width": 640, "height": 480, "fps": 30},
     top: {"type": "opencv", "index_or_path": 3, "width": 640, "height": 480, "fps": 30},
   }' --robot.right_arm_config.cameras='{
     wrist: {"type": "opencv", "index_or_path": 2, "width": 640, "height": 480, "fps": 30},
     front: {"type": "opencv", "index_or_path": 4, "width": 640, "height": 480, "fps": 30},
-  }' \
-  --teleop.type=bi_so_leader \
-  --teleop.left_arm_config.port=/dev/tty.usbmodem5A460852721 \
-  --teleop.right_arm_config.port=/dev/tty.usbmodem5A460819811 \
-  --teleop.id=bimanual_leader \
-  --display_data=true \
-  --dataset.repo_id=${HF_USER}/bimanual-so-handover-cube \
-  --dataset.num_episodes=25 \
-  --dataset.single_task="Grab and handover the red cube to the other arm" \
-  --dataset.streaming_encoding=true \
-  # --dataset.vcodec=auto \
+  }' \\
+  --teleop.type=bi_so_leader \\
+  --teleop.left_arm_config.port=/dev/tty.usbmodem5A460852721 \\
+  --teleop.right_arm_config.port=/dev/tty.usbmodem5A460819811 \\
+  --teleop.id=bimanual_leader \\
+  --display_data=true \\
+  --dataset.repo_id=${HF_USER}/bimanual-so-handover-cube \\
+  --dataset.num_episodes=25 \\
+  --dataset.single_task="Grab and handover the red cube to the other arm" \\
+  --dataset.streaming_encoding=true \\
   --dataset.encoder_threads=2
 ```
 """
 
 import logging
 import time
-from dataclasses import asdict, dataclass, field
-from pathlib import Path
+from dataclasses import asdict, dataclass
 from pprint import pformat
-from typing import Any
-
-import torch
 
 from lerobot.cameras import CameraConfig  # noqa: F401
 from lerobot.cameras.opencv import OpenCVCameraConfig  # noqa: F401
@@ -86,11 +78,10 @@ from lerobot.cameras.zmq import ZMQCameraConfig  # noqa: F401
 from lerobot.common.control_utils import (
     init_keyboard_listener,
     is_headless,
-    predict_action,
-    sanity_check_dataset_name,
     sanity_check_dataset_robot_compatibility,
 )
-from lerobot.configs import PreTrainedConfig, parser
+from lerobot.configs import parser
+from lerobot.configs.dataset import DatasetRecordConfig
 from lerobot.datasets import (
     LeRobotDataset,
     VideoEncodingManager,
@@ -98,21 +89,11 @@ from lerobot.datasets import (
     create_initial_features,
     safe_stop_image_writer,
 )
-from lerobot.policies import (
-    ActionInterpolator,
-    PreTrainedPolicy,
-    make_policy,
-    make_pre_post_processors,
-    make_robot_action,
-)
 from lerobot.processor import (
-    PolicyAction,
-    PolicyProcessorPipeline,
     RobotAction,
     RobotObservation,
     RobotProcessorPipeline,
     make_default_processors,
-    rename_stats,
 )
 from lerobot.robots import (  # noqa: F401
     Robot,
@@ -146,7 +127,6 @@ from lerobot.teleoperators import (  # noqa: F401
 )
 from lerobot.teleoperators.keyboard import KeyboardTeleop
 from lerobot.utils.constants import ACTION, OBS_STR
-from lerobot.utils.device_utils import get_safe_torch_device
 from lerobot.utils.feature_utils import build_dataset_frame, combine_feature_dicts
 from lerobot.utils.import_utils import register_third_party_plugins
 from lerobot.utils.robot_utils import precise_sleep
@@ -157,71 +137,12 @@ from lerobot.utils.utils import (
 from lerobot.utils.visualization_utils import init_rerun, log_rerun_data
 
 
-@dataclass
-class DatasetRecordConfig:
-    # Dataset identifier. By convention it should match '{hf_username}/{dataset_name}' (e.g. `lerobot/test`).
-    repo_id: str
-    # A short but accurate description of the task performed during the recording (e.g. "Pick the Lego block and drop it in the box on the right.")
-    single_task: str
-    # Root directory where the dataset will be stored (e.g. 'dataset/path'). If None, defaults to $HF_LEROBOT_HOME/repo_id.
-    root: str | Path | None = None
-    # Limit the frames per second.
-    fps: int = 30
-    # Number of seconds for data recording for each episode.
-    episode_time_s: int | float = 60
-    # Number of seconds for resetting the environment after each episode.
-    reset_time_s: int | float = 60
-    # Number of episodes to record.
-    num_episodes: int = 50
-    # Encode frames in the dataset into video
-    video: bool = True
-    # Upload dataset to Hugging Face hub.
-    push_to_hub: bool = True
-    # Upload on private repository on the Hugging Face hub.
-    private: bool = False
-    # Add tags to your dataset on the hub.
-    tags: list[str] | None = None
-    # Number of subprocesses handling the saving of frames as PNG. Set to 0 to use threads only;
-    # set to ≥1 to use subprocesses, each using threads to write images. The best number of processes
-    # and threads depends on your system. We recommend 4 threads per camera with 0 processes.
-    # If fps is unstable, adjust the thread count. If still unstable, try using 1 or more subprocesses.
-    num_image_writer_processes: int = 0
-    # Number of threads writing the frames as png images on disk, per camera.
-    # Too many threads might cause unstable teleoperation fps due to main thread being blocked.
-    # Not enough threads might cause low camera fps.
-    num_image_writer_threads_per_camera: int = 4
-    # Number of episodes to record before batch encoding videos
-    # Set to 1 for immediate encoding (default behavior), or higher for batched encoding
-    video_encoding_batch_size: int = 1
-    # Video codec for encoding videos. Options: 'h264', 'hevc', 'libsvtav1', 'auto',
-    # or hardware-specific: 'h264_videotoolbox', 'h264_nvenc', 'h264_vaapi', 'h264_qsv'.
-    # Use 'auto' to auto-detect the best available hardware encoder.
-    vcodec: str = "libsvtav1"
-    # 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
-    # Maximum number of frames to buffer per camera when using streaming encoding.
-    # ~1s buffer at 30fps. Provides backpressure if the encoder can't keep up.
-    encoder_queue_maxsize: int = 30
-    # Number of threads per encoder instance. None = auto (codec default).
-    # Lower values reduce CPU usage, maps to 'lp' (via svtav1-params) for libsvtav1 and 'threads' for h264/hevc..
-    encoder_threads: int | None = None
-    # Rename map for the observation to override the image and state keys
-    rename_map: dict[str, str] = field(default_factory=dict)
-
-    def __post_init__(self):
-        if self.single_task is None:
-            raise ValueError("You need to provide a task as argument in `single_task`.")
-
-
 @dataclass
 class RecordConfig:
     robot: RobotConfig
     dataset: DatasetRecordConfig
-    # Whether to control the robot with a teleoperator
+    # Teleoperator to control the robot (required)
     teleop: TeleoperatorConfig | None = None
-    # Whether to control the robot with a policy
-    policy: PreTrainedConfig | None = None
     # Display all cameras on screen
     display_data: bool = False
     # Display data on a remote Rerun server
@@ -234,27 +155,14 @@ class RecordConfig:
     play_sounds: bool = True
     # Resume recording on an existing dataset.
     resume: bool = False
-    # Action interpolation multiplier for smoother policy control (1=off, 2=2x, 3=3x)
-    # Only applies when using a policy (not teleop)
-    interpolation_multiplier: int = 1
 
     def __post_init__(self):
-        # HACK: We parse again the cli args here to get the pretrained path if there was one.
-        policy_path = parser.get_path_arg("policy")
-
-        if policy_path:
-            cli_overrides = parser.get_cli_overrides("policy")
-
-            self.policy = PreTrainedConfig.from_pretrained(policy_path, cli_overrides=cli_overrides)
-            self.policy.pretrained_path = policy_path
-
-        if self.teleop is None and self.policy is None:
-            raise ValueError("Choose a policy, a teleoperator or both to control the robot")
-
-    @classmethod
-    def __get_path_fields__(cls) -> list[str]:
-        """This enables the parser to load config from the policy using `--policy.path=local/dir`"""
-        return ["policy"]
+        if self.teleop is None:
+            raise ValueError(
+                "A teleoperator is required for recording. "
+                "Use --teleop.type=... to specify one. "
+                "For policy-based deployment, use lerobot-rollout instead."
+            )
 
 
 """ --------------- record_loop() data flow --------------------------
@@ -264,18 +172,14 @@ class RecordConfig:
            V
      [ robot_observation_processor ] ---> processed_obs
            V
-     .-----( ACTION LOGIC )------------------.
-     V                                       V
-     [ From Teleoperator ]                   [ From Policy ]
-     |                                       |
-     |  [teleop.get_action] -> raw_action    |   [predict_action]
-     |          |                            |          |
-     |          V                            |          V
-     | [teleop_action_processor]             |          |
-     |          |                            |          |
-     '---> processed_teleop_action           '---> processed_policy_action
-     |                                       |
-     '-------------------------.-------------'
+     [ Teleoperator ]
+     |
+     |  [teleop.get_action] -> raw_action
+     |          |
+     |          V
+     | [teleop_action_processor]
+     |          |
+     '---> processed_teleop_action
                                V
                   [ robot_action_processor ] --> robot_action_to_send
                                V
@@ -303,13 +207,9 @@ def record_loop(
     ],  # runs after robot
     dataset: LeRobotDataset | None = None,
     teleop: Teleoperator | list[Teleoperator] | None = None,
-    policy: PreTrainedPolicy | None = None,
-    preprocessor: PolicyProcessorPipeline[dict[str, Any], dict[str, Any]] | None = None,
-    postprocessor: PolicyProcessorPipeline[PolicyAction, PolicyAction] | None = None,
     control_time_s: int | None = None,
     single_task: str | None = None,
     display_data: bool = False,
-    interpolator: ActionInterpolator | None = None,
     display_compressed_images: bool = False,
 ):
     if dataset is not None and dataset.fps != fps:
@@ -340,21 +240,7 @@ def record_loop(
                 "For multi-teleop, the list must contain exactly one KeyboardTeleop and one arm teleoperator. Currently only supported for LeKiwi robot."
             )
 
-    # Reset policy and processor if they are provided
-    if policy is not None and preprocessor is not None and postprocessor is not None:
-        policy.reset()
-        preprocessor.reset()
-        postprocessor.reset()
-
-    # Reset interpolator if provided
-    if interpolator is not None:
-        interpolator.reset()
-
-    # Calculate control interval based on interpolation
-    use_interpolation = interpolator is not None and interpolator.enabled and policy is not None
-    control_interval = interpolator.get_control_interval(fps) if interpolator else 1 / fps
-    # Pre-compute action key order outside the hot loop — it won't change mid-episode.
-    action_keys = sorted(robot.action_features) if use_interpolation else []
+    control_interval = 1 / fps
 
     no_action_count = 0
     timestamp = 0
@@ -372,63 +258,11 @@ def record_loop(
         # Applies a pipeline to the raw robot observation, default is IdentityProcessor
         obs_processed = robot_observation_processor(obs)
 
-        if policy is not None or dataset is not None:
+        if dataset is not None:
             observation_frame = build_dataset_frame(dataset.features, obs_processed, prefix=OBS_STR)
 
-        # Track whether this iteration should be recorded to the dataset.
-        # Interpolated-only iterations send actions to the robot but don't record frames,
-        # keeping the dataset at the original fps while the robot moves at the higher rate.
-        is_record_frame = True
-
-        # Get action from either policy or teleop
-        if policy is not None and preprocessor is not None and postprocessor is not None:
-            # With interpolation: only call policy when interpolator needs new action
-            if use_interpolation:
-                ran_inference = False
-
-                if interpolator.needs_new_action():
-                    action_values = predict_action(
-                        observation=observation_frame,
-                        policy=policy,
-                        device=get_safe_torch_device(policy.config.device),
-                        preprocessor=preprocessor,
-                        postprocessor=postprocessor,
-                        use_amp=policy.config.use_amp,
-                        task=single_task,
-                        robot_type=robot.robot_type,
-                    )
-                    act_processed_policy = make_robot_action(action_values, dataset.features)
-                    robot_action_to_send = robot_action_processor((act_processed_policy, obs))
-
-                    action_tensor = torch.tensor([robot_action_to_send[k] for k in action_keys])
-                    interpolator.add(action_tensor)
-                    ran_inference = True
-
-                interp_action = interpolator.get()
-                if interp_action is not None:
-                    robot_action_to_send = {k: interp_action[i].item() for i, k in enumerate(action_keys)}
-                    action_values = robot_action_to_send
-                else:
-                    continue
-
-                is_record_frame = ran_inference
-            else:
-                action_values = predict_action(
-                    observation=observation_frame,
-                    policy=policy,
-                    device=get_safe_torch_device(policy.config.device),
-                    preprocessor=preprocessor,
-                    postprocessor=postprocessor,
-                    use_amp=policy.config.use_amp,
-                    task=single_task,
-                    robot_type=robot.robot_type,
-                )
-                act_processed_policy: RobotAction = make_robot_action(action_values, dataset.features)
-                # Applies a pipeline to the action, default is IdentityProcessor
-                robot_action_to_send = robot_action_processor((act_processed_policy, obs))
-                action_values = robot_action_to_send
-
-        elif policy is None and isinstance(teleop, Teleoperator):
+        # Get action from teleop
+        if isinstance(teleop, Teleoperator):
             act = teleop.get_action()
             if robot.name == "unitree_g1":
                 teleop.send_feedback(obs)
@@ -438,7 +272,7 @@ def record_loop(
             action_values = act_processed_teleop
             robot_action_to_send = robot_action_processor((act_processed_teleop, obs))
 
-        elif policy is None and isinstance(teleop, list):
+        elif isinstance(teleop, list):
             arm_action = teleop_arm.get_action()
             arm_action = {f"arm_{k}": v for k, v in arm_action.items()}
             keyboard_action = teleop_keyboard.get_action()
@@ -451,7 +285,7 @@ def record_loop(
             no_action_count += 1
             if no_action_count == 1 or no_action_count % 10 == 0:
                 logging.warning(
-                    "No policy or teleoperator provided, skipping action generation. "
+                    "No teleoperator provided, skipping action generation. "
                     "This is likely to happen when resetting the environment without a teleop device. "
                     "The robot won't be at its rest position at the start of the next episode."
                 )
@@ -463,8 +297,8 @@ def record_loop(
         # TODO(steven, pepijn, adil): we should use a pipeline step to clip the action, so the sent action is the action that we input to the robot.
         _sent_action = robot.send_action(robot_action_to_send)
 
-        # Write to dataset (only on real policy frames, not interpolated-only iterations)
-        if dataset is not None and is_record_frame:
+        # Write to dataset
+        if dataset is not None:
             action_frame = build_dataset_frame(dataset.features, action_values, prefix=ACTION)
             frame = {**observation_frame, **action_frame, "task": single_task}
             dataset.add_frame(frame)
@@ -488,7 +322,12 @@ def record_loop(
 
 
 @parser.wrap()
-def record(cfg: RecordConfig) -> LeRobotDataset:
+def record(
+    cfg: RecordConfig,
+    teleop_action_processor: RobotProcessorPipeline | None = None,
+    robot_action_processor: RobotProcessorPipeline | None = None,
+    robot_observation_processor: RobotProcessorPipeline | None = None,
+) -> LeRobotDataset:
     init_logging()
     logging.info(pformat(asdict(cfg)))
     if cfg.display_data:
@@ -502,7 +341,16 @@ def record(cfg: RecordConfig) -> LeRobotDataset:
     robot = make_robot_from_config(cfg.robot)
     teleop = make_teleoperator_from_config(cfg.teleop) if cfg.teleop is not None else None
 
-    teleop_action_processor, robot_action_processor, robot_observation_processor = make_default_processors()
+    # Fall back to identity pipelines when the caller doesn't supply processors.
+    if (
+        teleop_action_processor is None
+        or robot_action_processor is None
+        or robot_observation_processor is None
+    ):
+        _t, _r, _o = make_default_processors()
+        teleop_action_processor = teleop_action_processor or _t
+        robot_action_processor = robot_action_processor or _r
+        robot_observation_processor = robot_observation_processor or _o
 
     dataset_features = combine_feature_dicts(
         aggregate_pipeline_dataset_features(
@@ -540,8 +388,13 @@ def record(cfg: RecordConfig) -> LeRobotDataset:
             )
             sanity_check_dataset_robot_compatibility(dataset, robot, cfg.dataset.fps, dataset_features)
         else:
-            # Create empty dataset or load existing saved episodes
-            sanity_check_dataset_name(cfg.dataset.repo_id, cfg.policy)
+            # Reject eval_ prefix — for policy evaluation use lerobot-rollout
+            if cfg.dataset.repo_id.startswith("eval_"):
+                raise ValueError(
+                    "Dataset names starting with 'eval_' are reserved for policy evaluation. "
+                    "lerobot-record is for data collection only. Use lerobot-rollout for policy deployment."
+                )
+            cfg.dataset.stamp_repo_id()
             dataset = LeRobotDataset.create(
                 cfg.dataset.repo_id,
                 cfg.dataset.fps,
@@ -558,30 +411,6 @@ def record(cfg: RecordConfig) -> LeRobotDataset:
                 encoder_threads=cfg.dataset.encoder_threads,
             )
 
-        # Load pretrained policy
-        policy = (
-            None
-            if cfg.policy is None
-            else make_policy(cfg.policy, ds_meta=dataset.meta, rename_map=cfg.dataset.rename_map)
-        )
-        preprocessor = None
-        postprocessor = None
-        interpolator = None
-        if cfg.policy is not None:
-            preprocessor, postprocessor = make_pre_post_processors(
-                policy_cfg=cfg.policy,
-                pretrained_path=cfg.policy.pretrained_path,
-                dataset_stats=rename_stats(dataset.meta.stats, cfg.dataset.rename_map),
-                preprocessor_overrides={
-                    "device_processor": {"device": cfg.policy.device},
-                    "rename_observations_processor": {"rename_map": cfg.dataset.rename_map},
-                },
-            )
-            # Create interpolator for smoother policy control
-            if cfg.interpolation_multiplier > 1:
-                interpolator = ActionInterpolator(multiplier=cfg.interpolation_multiplier)
-                logging.info(f"Action interpolation enabled: {cfg.interpolation_multiplier}x control rate")
-
         robot.connect()
         if teleop is not None:
             teleop.connect()
@@ -605,14 +434,10 @@ def record(cfg: RecordConfig) -> LeRobotDataset:
                     robot_action_processor=robot_action_processor,
                     robot_observation_processor=robot_observation_processor,
                     teleop=teleop,
-                    policy=policy,
-                    preprocessor=preprocessor,
-                    postprocessor=postprocessor,
                     dataset=dataset,
                     control_time_s=cfg.dataset.episode_time_s,
                     single_task=cfg.dataset.single_task,
                     display_data=cfg.display_data,
-                    interpolator=interpolator,
                     display_compressed_images=display_compressed_images,
                 )
 
@@ -660,7 +485,10 @@ def record(cfg: RecordConfig) -> LeRobotDataset:
             listener.stop()
 
         if cfg.dataset.push_to_hub:
-            dataset.push_to_hub(tags=cfg.dataset.tags, private=cfg.dataset.private)
+            if dataset and dataset.num_episodes > 0:
+                dataset.push_to_hub(tags=cfg.dataset.tags, private=cfg.dataset.private)
+            else:
+                logging.warning("No episodes saved — skipping push to hub")
 
         log_say("Exiting", cfg.play_sounds)
     return dataset

src/lerobot/scripts/lerobot_rollout.py

@@ -0,0 +1,211 @@
+#!/usr/bin/env python
+
+# 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.
+
+"""Policy deployment engine with pluggable rollout strategies.
+
+``lerobot-rollout`` is the single CLI for running trained policies on
+real robots.
+
+Strategies
+----------
+    --strategy.type=base       Autonomous rollout, no recording
+    --strategy.type=sentry     Continuous recording with auto-upload
+    --strategy.type=highlight  Ring buffer + keystroke save
+    --strategy.type=dagger     Human-in-the-loop (DAgger / RaC)
+
+Inference backends
+------------------
+    --inference.type=sync      One policy call per control tick (default)
+    --inference.type=rtc       Real-Time Chunking for slow VLA models
+
+Usage examples
+--------------
+::
+
+    # Base mode — quick evaluation with sync inference
+    lerobot-rollout \\
+        --strategy.type=base \\
+        --policy.path=lerobot/act_koch_real \\
+        --robot.type=koch_follower \\
+        --robot.port=/dev/ttyACM0 \\
+        --task="pick up cube" --duration=30
+
+    # Base mode — RTC inference for slow VLAs (Pi0, Pi0.5, SmolVLA)
+    lerobot-rollout \\
+        --strategy.type=base \\
+        --policy.path=lerobot/pi0_base \\
+        --inference.type=rtc \\
+        --inference.rtc.execution_horizon=10 \\
+        --inference.rtc.max_guidance_weight=10.0 \\
+        --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 cube" --duration=60
+
+    # Sentry mode — continuous recording with periodic upload
+    lerobot-rollout \\
+        --strategy.type=sentry \\
+        --strategy.upload_every_n_episodes=5 \\
+        --policy.path=lerobot/pi0_base \\
+        --inference.type=rtc \\
+        --robot.type=so100_follower \\
+        --robot.port=/dev/ttyACM0 \\
+        --dataset.repo_id=user/rollout_sentry_data \\
+        --dataset.single_task="patrol" --duration=3600
+
+    # Highlight mode — ring buffer, press 's' to save, 'h' to push
+    lerobot-rollout \\
+        --strategy.type=highlight \\
+        --strategy.ring_buffer_seconds=30 \\
+        --policy.path=lerobot/act_koch_real \\
+        --robot.type=koch_follower \\
+        --robot.port=/dev/ttyACM0 \\
+        --dataset.repo_id=user/rollout_highlight_data \\
+        --dataset.single_task="pick up cube"
+
+    # DAgger mode — human-in-the-loop corrections only
+    lerobot-rollout \\
+        --strategy.type=dagger \\
+        --strategy.num_episodes=20 \\
+        --policy.path=outputs/pretrain/checkpoints/last/pretrained_model \\
+        --robot.type=bi_openarm_follower \\
+        --teleop.type=openarm_mini \\
+        --dataset.repo_id=user/rollout_hil_data \\
+        --dataset.single_task="Fold the T-shirt"
+
+    # DAgger mode — continuous recording with RTC inference
+    lerobot-rollout \\
+        --strategy.type=dagger \\
+        --strategy.record_autonomous=true \\
+        --strategy.num_episodes=50 \\
+        --inference.type=rtc \\
+        --inference.rtc.execution_horizon=10 \\
+        --policy.path=user/my_pi0_policy \\
+        --robot.type=so100_follower \\
+        --robot.port=/dev/ttyACM0 \\
+        --teleop.type=so101_leader \\
+        --teleop.port=/dev/ttyACM1 \\
+        --dataset.repo_id=user/rollout_dagger_rtc_data \\
+        --dataset.single_task="Grasp the block"
+
+    # With Rerun visualization and torch.compile
+    lerobot-rollout \\
+        --strategy.type=base \\
+        --policy.path=lerobot/act_koch_real \\
+        --robot.type=koch_follower \\
+        --robot.port=/dev/ttyACM0 \\
+        --task="pick up cube" --duration=60 \\
+        --display_data=true \\
+        --use_torch_compile=true
+
+    # Resume a previous sentry recording session
+    lerobot-rollout \\
+        --strategy.type=sentry \\
+        --policy.path=user/my_policy \\
+        --robot.type=so100_follower \\
+        --robot.port=/dev/ttyACM0 \\
+        --dataset.repo_id=user/rollout_sentry_data \\
+        --dataset.single_task="patrol" \\
+        --resume=true
+"""
+
+import logging
+
+from lerobot.cameras.opencv import OpenCVCameraConfig  # noqa: F401
+from lerobot.cameras.realsense import RealSenseCameraConfig  # noqa: F401
+from lerobot.cameras.zmq import ZMQCameraConfig  # noqa: F401
+from lerobot.configs import parser
+from lerobot.robots import (  # noqa: F401
+    Robot,
+    RobotConfig,
+    bi_openarm_follower,
+    bi_so_follower,
+    earthrover_mini_plus,
+    hope_jr,
+    koch_follower,
+    omx_follower,
+    openarm_follower,
+    reachy2,
+    so_follower,
+    unitree_g1 as unitree_g1_robot,
+)
+from lerobot.rollout import RolloutConfig, build_rollout_context, create_strategy
+from lerobot.teleoperators import (  # noqa: F401
+    Teleoperator,
+    TeleoperatorConfig,
+    bi_openarm_leader,
+    bi_so_leader,
+    homunculus,
+    koch_leader,
+    omx_leader,
+    openarm_leader,
+    openarm_mini,
+    reachy2_teleoperator,
+    so_leader,
+    unitree_g1,
+)
+from lerobot.utils.import_utils import register_third_party_plugins
+from lerobot.utils.process import ProcessSignalHandler
+from lerobot.utils.utils import init_logging
+from lerobot.utils.visualization_utils import init_rerun
+
+logger = logging.getLogger(__name__)
+
+
+@parser.wrap()
+def rollout(cfg: RolloutConfig):
+    """Main entry point for policy deployment."""
+    init_logging()
+
+    if cfg.display_data:
+        logger.info("Initializing Rerun visualization (ip=%s, port=%s)", cfg.display_ip, cfg.display_port)
+        init_rerun(session_name="rollout", ip=cfg.display_ip, port=cfg.display_port)
+
+    signal_handler = ProcessSignalHandler(use_threads=True, display_pid=False)
+    shutdown_event = signal_handler.shutdown_event
+
+    logger.info("Building rollout context...")
+    ctx = build_rollout_context(cfg, shutdown_event)
+
+    strategy = create_strategy(cfg.strategy)
+    logger.info("Rollout strategy: %s", cfg.strategy.type)
+    logger.info(
+        "Robot: %s | FPS: %.0f | Duration: %s",
+        cfg.robot.type if cfg.robot else "?",
+        cfg.fps,
+        f"{cfg.duration}s" if cfg.duration > 0 else "infinite",
+    )
+
+    try:
+        strategy.setup(ctx)
+        logger.info("Rollout setup complete, starting rollout...")
+        strategy.run(ctx)
+    except KeyboardInterrupt:
+        logger.info("Interrupted by user")
+    finally:
+        strategy.teardown(ctx)
+
+    logger.info("Rollout finished")
+
+
+def main():
+    """CLI entry point for ``lerobot-rollout``."""
+    register_third_party_plugins()
+    rollout()
+
+
+if __name__ == "__main__":
+    main()

src/lerobot/utils/action_interpolator.py

@@ -0,0 +1,116 @@
+# 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.
+
+"""Action interpolation for smoother robot control.
+
+Provides configurable Nx control rate by interpolating between consecutive actions.
+Useful with RTC and action-chunking policies to reduce jerkiness.
+"""
+
+from torch import Tensor
+
+
+class ActionInterpolator:
+    """Interpolates between consecutive actions for smoother control.
+
+    When enabled with multiplier N, produces N actions per policy action
+    by linearly interpolating between the previous and current action.
+
+    Example with multiplier=3:
+        prev_action -> [1/3 interpolated, 2/3 interpolated, current_action]
+
+    This effectively multiplies the control rate for smoother motion.
+
+    Usage:
+        interpolator = ActionInterpolator(multiplier=2)  # 2x control rate
+
+        # In control loop:
+        if interpolator.needs_new_action():
+            new_action = queue.get()
+            if new_action:
+                interpolator.add(new_action.cpu())
+
+        action = interpolator.get()
+        if action:
+            robot.send_action(action)
+    """
+
+    def __init__(self, multiplier: int = 1):
+        """Initialize the interpolator.
+
+        Args:
+            multiplier: Control rate multiplier (1 = no interpolation, 2 = 2x, 3 = 3x, etc.)
+        """
+        if multiplier < 1:
+            raise ValueError(f"multiplier must be >= 1, got {multiplier}")
+        self.multiplier = multiplier
+        self._prev: Tensor | None = None
+        self._buffer: list[Tensor] = []
+        self._idx = 0
+
+    @property
+    def enabled(self) -> bool:
+        """Whether interpolation is active (multiplier > 1)."""
+        return self.multiplier > 1
+
+    def reset(self):
+        """Reset interpolation state (call between episodes)."""
+        self._prev = None
+        self._buffer = []
+        self._idx = 0
+
+    def needs_new_action(self) -> bool:
+        """Check if a new action is needed from the queue."""
+        return self._idx >= len(self._buffer)
+
+    def add(self, action: Tensor) -> None:
+        """Add a new action and compute interpolated sequence.
+
+        Args:
+            action: New action tensor from policy/queue (already on CPU).
+        """
+        if self.multiplier > 1 and self._prev is not None:
+            self._buffer = []
+            for i in range(1, self.multiplier + 1):
+                t = i / self.multiplier
+                interp = self._prev + t * (action - self._prev)
+                self._buffer.append(interp)
+        else:
+            # First step: no previous action yet, so run at base FPS without interpolation.
+            self._buffer = [action.clone()]
+        self._prev = action.clone()
+        self._idx = 0
+
+    def get(self) -> Tensor | None:
+        """Get the next interpolated action.
+
+        Returns:
+            Next action tensor, or None if buffer is exhausted.
+        """
+        if self._idx >= len(self._buffer):
+            return None
+        action = self._buffer[self._idx]
+        self._idx += 1
+        return action
+
+    def get_control_interval(self, fps: float) -> float:
+        """Get the control interval based on interpolation multiplier.
+
+        Args:
+            fps: Base frames per second.
+
+        Returns:
+            Control interval in seconds (divided by multiplier).
+        """
+        return 1.0 / (fps * self.multiplier)

src/lerobot/utils/pedal.py

@@ -0,0 +1,83 @@
+# 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.
+
+"""Generic foot pedal listener using evdev.
+
+Callers supply a callback receiving the pressed key code (e.g. ``"KEY_A"``)
+and an optional device path.  The listener runs in a daemon thread and
+silently no-ops when :mod:`evdev` is not installed or the device is
+unavailable.  Strategy-specific key mapping logic lives in the caller.
+"""
+
+from __future__ import annotations
+
+import logging
+import threading
+from collections.abc import Callable
+
+logger = logging.getLogger(__name__)
+
+DEFAULT_PEDAL_DEVICE = "/dev/input/by-id/usb-PCsensor_FootSwitch-event-kbd"
+
+
+def start_pedal_listener(
+    on_press: Callable[[str], None],
+    device_path: str = DEFAULT_PEDAL_DEVICE,
+) -> threading.Thread | None:
+    """Spawn a daemon thread that forwards pedal key-press codes to ``on_press``.
+
+    Parameters
+    ----------
+    on_press:
+        Callback invoked with the pressed key code string (e.g. ``"KEY_A"``)
+        on each pedal press event.  The callback runs in the listener thread
+        and must be thread-safe.
+    device_path:
+        Linux input device path (e.g. ``/dev/input/by-id/...``).
+
+    Returns
+    -------
+    The started daemon :class:`threading.Thread`, or ``None`` when
+    :mod:`evdev` is not installed (optional dependency; silent no-op).
+    """
+    try:
+        from evdev import InputDevice, categorize, ecodes
+    except ImportError:
+        return None
+
+    def pedal_reader() -> None:
+        try:
+            dev = InputDevice(device_path)
+            logger.info("Pedal connected: %s", dev.name)
+            for ev in dev.read_loop():
+                if ev.type != ecodes.EV_KEY:
+                    continue
+                key = categorize(ev)
+                code = key.keycode
+                if isinstance(code, (list, tuple)):
+                    code = code[0]
+                if key.keystate != 1:  # only key-down events
+                    continue
+                try:
+                    on_press(code)
+                except Exception as cb_err:  # pragma: no cover - defensive
+                    logger.warning("Pedal callback error: %s", cb_err)
+        except (FileNotFoundError, PermissionError):
+            pass
+        except Exception as e:
+            logger.warning("Pedal error: %s", e)
+
+    thread = threading.Thread(target=pedal_reader, daemon=True, name="PedalListener")
+    thread.start()
+    return thread

tests/policies/rtc/test_action_interpolator.py

@@ -17,9 +17,9 @@
 import pytest
 import torch
 
-from lerobot.policies.rtc.action_interpolator import ActionInterpolator
 from lerobot.policies.rtc.action_queue import ActionQueue
 from lerobot.policies.rtc.configuration_rtc import RTCConfig
+from lerobot.utils.action_interpolator import ActionInterpolator
 
 # ====================== Fixtures ======================
 

tests/policies/rtc/test_configuration_rtc.py

@@ -26,7 +26,7 @@ def test_rtc_config_default_initialization():
     """Test RTCConfig initializes with default values."""
     config = RTCConfig()
 
-    assert config.enabled is False
+    assert config.enabled is True
     assert config.prefix_attention_schedule == RTCAttentionSchedule.LINEAR
     assert config.max_guidance_weight == 10.0
     assert config.execution_horizon == 10

tests/policies/rtc/test_rtc_relative_actions.py

@@ -13,7 +13,9 @@ Flow under test:
 import importlib.util
 import sys
 from pathlib import Path
+from types import ModuleType, SimpleNamespace
 
+import numpy as np
 import torch
 
 from lerobot.configs.types import (
@@ -22,7 +24,13 @@ from lerobot.configs.types import (
     PolicyFeature,
     RTCAttentionSchedule,
 )
-from lerobot.processor import TransitionKey, batch_to_transition
+from lerobot.processor import (
+    PolicyProcessorPipeline,
+    TransitionKey,
+    batch_to_transition,
+    policy_action_to_transition,
+    transition_to_policy_action,
+)
 from lerobot.processor.normalize_processor import NormalizerProcessorStep, UnnormalizerProcessorStep
 from lerobot.processor.relative_action_processor import (
     AbsoluteActionsProcessorStep,
@@ -52,6 +60,34 @@ _rtc_debug_mod = _import_rtc_module("lerobot.policies.rtc.debug_tracker", "debug
 _rtc_mod = _import_rtc_module("lerobot.policies.rtc.modeling_rtc", "modeling_rtc.py")
 RTCProcessor = _rtc_mod.RTCProcessor
 
+
+def _ensure_rollout_test_packages() -> Path:
+    rollout_dir = Path(__file__).resolve().parents[3] / "src" / "lerobot" / "rollout"
+    rollout_pkg = sys.modules.setdefault("lerobot.rollout", ModuleType("lerobot.rollout"))
+    rollout_pkg.__path__ = [str(rollout_dir)]
+    inference_pkg = sys.modules.setdefault(
+        "lerobot.rollout.inference", ModuleType("lerobot.rollout.inference")
+    )
+    inference_pkg.__path__ = [str(rollout_dir / "inference")]
+    return rollout_dir
+
+
+def _import_rollout_module(module_name: str, relative_path: str):
+    rollout_dir = _ensure_rollout_test_packages()
+    spec = importlib.util.spec_from_file_location(module_name, rollout_dir / relative_path)
+    mod = importlib.util.module_from_spec(spec)
+    sys.modules[module_name] = mod
+    spec.loader.exec_module(mod)
+    return mod
+
+
+_rollout_robot_wrapper_mod = _import_rollout_module("lerobot.rollout.robot_wrapper", "robot_wrapper.py")
+_rollout_base_mod = _import_rollout_module("lerobot.rollout.inference.base", "inference/base.py")
+_rollout_sync_mod = _import_rollout_module("lerobot.rollout.inference.sync", "inference/sync.py")
+_rollout_rtc_mod = _import_rollout_module("lerobot.rollout.inference.rtc", "inference/rtc.py")
+SyncInferenceEngine = _rollout_sync_mod.SyncInferenceEngine
+get_current_raw_state = _rollout_rtc_mod._get_current_raw_state
+
 ACTION_DIM = 6
 CHUNK_SIZE = 50
 EXECUTION_HORIZON = 10
@@ -89,6 +125,44 @@ def _make_relative_pipeline(action_dim=ACTION_DIM, norm_mode=NormalizationMode.M
     return relative_step, normalizer, unnormalizer, absolute_step
 
 
+def _make_relative_sync_pipelines(
+    action_dim=ACTION_DIM,
+    action_names: list[str] | None = None,
+    exclude_joints: list[str] | None = None,
+):
+    relative_step = RelativeActionsProcessorStep(
+        enabled=True,
+        exclude_joints=exclude_joints or [],
+        action_names=action_names or [f"joint_{i}.pos" for i in range(action_dim)],
+    )
+    absolute_step = AbsoluteActionsProcessorStep(enabled=True, relative_step=relative_step)
+    preprocessor = PolicyProcessorPipeline(steps=[relative_step], name="test_preprocessor")
+    postprocessor = PolicyProcessorPipeline(
+        steps=[absolute_step],
+        name="test_postprocessor",
+        to_transition=policy_action_to_transition,
+        to_output=transition_to_policy_action,
+    )
+    return relative_step, preprocessor, postprocessor
+
+
+class _ChunkPolicyStub:
+    def __init__(self, action_dim: int, n_action_steps: int):
+        self.config = SimpleNamespace(use_amp=False, n_action_steps=n_action_steps)
+        self._chunk = torch.zeros(1, n_action_steps, action_dim)
+        self.predict_calls = 0
+
+    def reset(self):
+        return None
+
+    def predict_action_chunk(self, batch):
+        self.predict_calls += 1
+        return self._chunk.clone()
+
+    def select_action(self, batch):
+        raise AssertionError("SyncInferenceEngine should consume chunk outputs directly")
+
+
 class TestActionQueueRelativeActions:
     """Verify ActionQueue stores model-space (relative) actions for RTC and absolute for robot."""
 
@@ -120,6 +194,82 @@ class TestActionQueueRelativeActions:
         torch.testing.assert_close(first_action, absolute_actions[0])
 
 
+class TestRolloutInferenceRelativeActions:
+    """Regression tests for rollout inference engines with relative-action policies."""
+
+    def test_sync_engine_postprocesses_chunk_before_queueing(self):
+        """Queued sync actions must stay anchored to the state from the chunk-producing step."""
+        _, preprocessor, postprocessor = _make_relative_sync_pipelines(ACTION_DIM)
+        policy = _ChunkPolicyStub(action_dim=ACTION_DIM, n_action_steps=3)
+        ordered_action_keys = [f"joint_{i}.pos" for i in range(ACTION_DIM)]
+        dataset_features = {ACTION: {"names": ordered_action_keys}}
+
+        engine = SyncInferenceEngine(
+            policy=policy,
+            preprocessor=preprocessor,
+            postprocessor=postprocessor,
+            dataset_features=dataset_features,
+            ordered_action_keys=ordered_action_keys,
+            task="test",
+            device="cpu",
+            robot_type="mock",
+        )
+
+        state_1 = np.arange(1, ACTION_DIM + 1, dtype=np.float32)
+        state_2 = 10 * state_1
+
+        action_1 = engine.get_action({OBS_STATE: state_1.copy()})
+        action_2 = engine.get_action({OBS_STATE: state_2.copy()})
+
+        torch.testing.assert_close(action_1, torch.from_numpy(state_1))
+        torch.testing.assert_close(action_2, torch.from_numpy(state_1))
+        assert policy.predict_calls == 1
+
+    def test_rtc_reanchoring_prefers_raw_cached_state(self):
+        """RTC re-anchoring must use the raw state cached before observation normalization."""
+        action_dim = ACTION_DIM
+        relative_step = RelativeActionsProcessorStep(
+            enabled=True,
+            action_names=[f"joint_{i}.pos" for i in range(action_dim)],
+        )
+        features = {
+            OBS_STATE: PolicyFeature(type=FeatureType.STATE, shape=(action_dim,)),
+            ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(action_dim,)),
+        }
+        stats = {
+            OBS_STATE: {
+                "mean": np.arange(1, action_dim + 1, dtype=np.float32),
+                "std": 2 * np.ones(action_dim, dtype=np.float32),
+            },
+            ACTION: {
+                "mean": np.zeros(action_dim, dtype=np.float32),
+                "std": np.ones(action_dim, dtype=np.float32),
+            },
+        }
+        preprocessor = PolicyProcessorPipeline(
+            steps=[
+                relative_step,
+                NormalizerProcessorStep(
+                    features=features,
+                    norm_map={
+                        FeatureType.STATE: NormalizationMode.MEAN_STD,
+                        FeatureType.ACTION: NormalizationMode.MEAN_STD,
+                    },
+                    stats=stats,
+                ),
+            ],
+            name="test_preprocessor_with_state_norm",
+        )
+
+        raw_state = torch.from_numpy(np.arange(5, 5 + action_dim, dtype=np.float32)).unsqueeze(0)
+        preprocessed = preprocessor({OBS_STATE: raw_state.clone()})
+
+        current_state = get_current_raw_state(relative_step, preprocessed.get(OBS_STATE))
+
+        torch.testing.assert_close(current_state, raw_state)
+        assert not torch.allclose(preprocessed[OBS_STATE], raw_state)
+
+
 class TestRTCDenoiseWithRelativeLeftovers:
     """Verify RTC denoise_step correctly handles relative-space prev_chunk_left_over."""
 
@@ -187,7 +337,7 @@ class TestRTCDenoiseWithRelativeLeftovers:
 
 
 class TestFullPipelineRelativeRTC:
-    """End-to-end test of the RTC + relative actions pipeline matching eval_with_real_robot.py flow."""
+    """End-to-end test of the RTC + relative actions pipeline matching lerobot-rollout flow."""
 
     def test_preprocessor_caches_state_for_postprocessor(self):
         """Preprocessor's relative step should cache state so postprocessor can convert back."""
@@ -240,7 +390,7 @@ class TestFullPipelineRelativeRTC:
         torch.testing.assert_close(recovered, actions, atol=1e-5, rtol=1e-5)
 
     def test_eval_loop_simulation(self):
-        """Simulate the eval_with_real_robot.py loop with relative actions.
+        """Simulate the lerobot-rollout loop with relative actions.
 
         Iteration 1: No leftovers → model generates relative actions → store for RTC
         Iteration 2: Use leftovers as RTC guidance → model generates new relative actions
@@ -401,12 +551,12 @@ class TestStateRebasingApproximation:
 
 
 def _detect_relative_actions(preprocessor) -> bool:
-    """Mirror of the helper in eval_with_real_robot.py for testing without importing it."""
+    """Mirror of the helper in lerobot-rollout for testing without importing it."""
     return any(isinstance(step, RelativeActionsProcessorStep) and step.enabled for step in preprocessor.steps)
 
 
 class TestDetectRelativeActions:
-    """Test the _detect_relative_actions helper logic used by eval_with_real_robot.py."""
+    """Test the _detect_relative_actions helper logic used by lerobot-rollout."""
 
     def test_detects_enabled_relative_step(self):
         class FakePipeline:

tests/test_cli_peft.py

@@ -24,10 +24,6 @@ def lerobot_train(args):
     return run_command(cmd="lerobot-train", module="lerobot_train", args=args)
 
 
-def lerobot_record(args):
-    return run_command(cmd="lerobot-record", module="lerobot_record", args=args)
-
-
 def resolve_model_id_for_peft_training(policy_type):
     """PEFT training needs pretrained models, this finds the pretrained model of a policy type for PEFT training."""
     if policy_type == "smolvla":
@@ -155,81 +151,3 @@ def test_peft_training_params_are_fewer(policy_type, tmp_path):
                 f"--output_dir={output_dir}",
             ]
         )
-
-
-class DummyRobot:
-    name = "dummy"
-    cameras = []
-    action_features = {"foo": 1.0, "bar": 2.0}
-    observation_features = {"obs1": 1.0, "obs2": 2.0}
-    is_connected = True
-
-    def connect(self, *args):
-        pass
-
-    def disconnect(self):
-        pass
-
-
-def dummy_make_robot_from_config(*args, **kwargs):
-    return DummyRobot()
-
-
-@pytest.mark.parametrize("policy_type", ["smolvla"])
-@skip_if_package_missing("peft")
-def test_peft_record_loads_policy(policy_type, tmp_path):
-    """Train a policy with PEFT and attempt to load it with `lerobot-record`."""
-    from peft import PeftModel
-
-    output_dir = tmp_path / f"output_{policy_type}"
-    model_id = resolve_model_id_for_peft_training(policy_type)
-
-    lerobot_train(
-        [
-            f"--policy.path={model_id}",
-            "--policy.push_to_hub=false",
-            "--policy.input_features=null",
-            "--policy.output_features=null",
-            "--peft.method=LORA",
-            "--dataset.repo_id=lerobot/pusht",
-            "--dataset.episodes=[0, 1]",
-            "--steps=1",
-            f"--output_dir={output_dir}",
-        ]
-    )
-
-    policy_dir = output_dir / "checkpoints" / "last" / "pretrained_model"
-    dataset_dir = tmp_path / "eval_pusht"
-    single_task = "move the table"
-    loaded_policy = None
-
-    def dummy_record_loop(*args, **kwargs):
-        nonlocal loaded_policy
-
-        if "dataset" not in kwargs:
-            return
-
-        dataset = kwargs["dataset"]
-        dataset.add_frame({"task": single_task})
-        loaded_policy = kwargs["policy"]
-
-    with (
-        patch("lerobot.scripts.lerobot_record.make_robot_from_config", dummy_make_robot_from_config),
-        # disable record loop since we're only interested in successful loading of the policy.
-        patch("lerobot.scripts.lerobot_record.record_loop", dummy_record_loop),
-        # disable speech output
-        patch("lerobot.utils.utils.say"),
-    ):
-        lerobot_record(
-            [
-                f"--policy.path={policy_dir}",
-                "--robot.type=so101_follower",
-                "--robot.port=/dev/null",
-                "--dataset.repo_id=lerobot/eval_pusht",
-                f'--dataset.single_task="{single_task}"',
-                f"--dataset.root={dataset_dir}",
-                "--dataset.push_to_hub=false",
-            ]
-        )
-
-        assert isinstance(loaded_policy, PeftModel)

tests/test_control_robot.py

@@ -21,8 +21,9 @@ import pytest
 pytest.importorskip("datasets", reason="datasets is required (install lerobot[dataset])")
 pytest.importorskip("deepdiff", reason="deepdiff is required (install lerobot[hardware])")
 
+from lerobot.configs.dataset import DatasetRecordConfig
 from lerobot.scripts.lerobot_calibrate import CalibrateConfig, calibrate
-from lerobot.scripts.lerobot_record import DatasetRecordConfig, RecordConfig, record
+from lerobot.scripts.lerobot_record import RecordConfig, record
 from lerobot.scripts.lerobot_replay import DatasetReplayConfig, ReplayConfig, replay
 from lerobot.scripts.lerobot_teleoperate import TeleoperateConfig, teleoperate
 from tests.fixtures.constants import DUMMY_REPO_ID

tests/test_rollout.py

@@ -0,0 +1,345 @@
+# 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.
+
+"""Minimal tests for the rollout module's public API."""
+
+from __future__ import annotations
+
+import dataclasses
+from unittest.mock import MagicMock
+
+import pytest
+import torch
+
+pytest.importorskip("datasets", reason="datasets is required (install lerobot[dataset])")
+
+# ---------------------------------------------------------------------------
+# Import smoke tests
+# ---------------------------------------------------------------------------
+
+
+def test_rollout_top_level_imports():
+    import lerobot.rollout
+
+    for name in lerobot.rollout.__all__:
+        assert hasattr(lerobot.rollout, name), f"Missing export: {name}"
+
+
+def test_inference_submodule_imports():
+    import lerobot.rollout.inference
+
+    for name in lerobot.rollout.inference.__all__:
+        assert hasattr(lerobot.rollout.inference, name), f"Missing export: {name}"
+
+
+def test_strategies_submodule_imports():
+    import lerobot.rollout.strategies
+
+    for name in lerobot.rollout.strategies.__all__:
+        assert hasattr(lerobot.rollout.strategies, name), f"Missing export: {name}"
+
+
+# ---------------------------------------------------------------------------
+# Config tests
+# ---------------------------------------------------------------------------
+
+
+def test_strategy_config_types():
+    from lerobot.rollout import (
+        BaseStrategyConfig,
+        DAggerStrategyConfig,
+        HighlightStrategyConfig,
+        SentryStrategyConfig,
+    )
+
+    assert BaseStrategyConfig().type == "base"
+    assert SentryStrategyConfig().type == "sentry"
+    assert HighlightStrategyConfig().type == "highlight"
+    assert DAggerStrategyConfig().type == "dagger"
+
+
+def test_dagger_config_invalid_input_device():
+    from lerobot.rollout import DAggerStrategyConfig
+
+    with pytest.raises(ValueError, match="input_device must be 'keyboard' or 'pedal'"):
+        DAggerStrategyConfig(input_device="joystick")
+
+
+def test_dagger_config_defaults():
+    from lerobot.rollout import DAggerStrategyConfig
+
+    cfg = DAggerStrategyConfig()
+    assert cfg.num_episodes is None
+    assert cfg.record_autonomous is False
+    assert cfg.input_device == "keyboard"
+
+
+def test_inference_config_types():
+    from lerobot.rollout import RTCInferenceConfig, SyncInferenceConfig
+
+    assert SyncInferenceConfig().type == "sync"
+
+    rtc = RTCInferenceConfig()
+    assert rtc.type == "rtc"
+    assert rtc.queue_threshold == 30
+    assert rtc.rtc is not None
+
+
+def test_sentry_config_defaults():
+    from lerobot.rollout import SentryStrategyConfig
+
+    cfg = SentryStrategyConfig()
+    assert cfg.upload_every_n_episodes == 5
+    assert cfg.target_video_file_size_mb is None
+
+
+# ---------------------------------------------------------------------------
+# RolloutRingBuffer
+# ---------------------------------------------------------------------------
+
+
+def test_ring_buffer_append_and_eviction():
+    from lerobot.rollout.ring_buffer import RolloutRingBuffer
+
+    buf = RolloutRingBuffer(max_seconds=0.5, max_memory_mb=100.0, fps=10.0)
+    # max_frames = 5
+    for i in range(8):
+        buf.append({"val": i})
+    assert len(buf) == 5
+
+
+def test_ring_buffer_drain():
+    from lerobot.rollout.ring_buffer import RolloutRingBuffer
+
+    buf = RolloutRingBuffer(max_seconds=1.0, max_memory_mb=100.0, fps=10.0)
+    for i in range(3):
+        buf.append({"val": i})
+    frames = buf.drain()
+    assert len(frames) == 3
+    assert len(buf) == 0
+    assert buf.estimated_bytes == 0
+
+
+def test_ring_buffer_clear():
+    from lerobot.rollout.ring_buffer import RolloutRingBuffer
+
+    buf = RolloutRingBuffer(max_seconds=1.0, max_memory_mb=100.0, fps=10.0)
+    buf.append({"val": 1})
+    buf.clear()
+    assert len(buf) == 0
+    assert buf.estimated_bytes == 0
+
+
+def test_ring_buffer_tensor_bytes():
+    from lerobot.rollout.ring_buffer import RolloutRingBuffer
+
+    buf = RolloutRingBuffer(max_seconds=1.0, max_memory_mb=100.0, fps=10.0)
+    t = torch.zeros(100, dtype=torch.float32)  # 400 bytes
+    buf.append({"tensor": t})
+    assert buf.estimated_bytes >= 400
+
+
+# ---------------------------------------------------------------------------
+# ThreadSafeRobot
+# ---------------------------------------------------------------------------
+
+
+def test_thread_safe_robot_delegates():
+    from lerobot.rollout.robot_wrapper import ThreadSafeRobot
+    from tests.mocks.mock_robot import MockRobot, MockRobotConfig
+
+    robot = MockRobot(MockRobotConfig(n_motors=3))
+    robot.connect()
+    wrapper = ThreadSafeRobot(robot)
+
+    obs = wrapper.get_observation()
+    assert "motor_1.pos" in obs
+    assert "motor_2.pos" in obs
+    assert "motor_3.pos" in obs
+
+    action = {"motor_1.pos": 0.0, "motor_2.pos": 1.0, "motor_3.pos": 2.0}
+    result = wrapper.send_action(action)
+    assert result == action
+
+    robot.disconnect()
+
+
+def test_thread_safe_robot_properties():
+    from lerobot.rollout.robot_wrapper import ThreadSafeRobot
+    from tests.mocks.mock_robot import MockRobot, MockRobotConfig
+
+    robot = MockRobot(MockRobotConfig(n_motors=3))
+    robot.connect()
+    wrapper = ThreadSafeRobot(robot)
+
+    assert wrapper.name == "mock_robot"
+    assert "motor_1.pos" in wrapper.observation_features
+    assert "motor_1.pos" in wrapper.action_features
+    assert wrapper.is_connected is True
+    assert wrapper.inner is robot
+
+    robot.disconnect()
+
+
+# ---------------------------------------------------------------------------
+# Strategy factory
+# ---------------------------------------------------------------------------
+
+
+def test_create_strategy_dispatches():
+    from lerobot.rollout import (
+        BaseStrategy,
+        BaseStrategyConfig,
+        DAggerStrategy,
+        DAggerStrategyConfig,
+        SentryStrategy,
+        SentryStrategyConfig,
+        create_strategy,
+    )
+
+    assert isinstance(create_strategy(BaseStrategyConfig()), BaseStrategy)
+    assert isinstance(create_strategy(SentryStrategyConfig()), SentryStrategy)
+    assert isinstance(create_strategy(DAggerStrategyConfig()), DAggerStrategy)
+
+
+def test_create_strategy_unknown_raises():
+    from lerobot.rollout import create_strategy
+
+    cfg = MagicMock()
+    cfg.type = "bogus"
+    with pytest.raises(ValueError, match="Unknown strategy type"):
+        create_strategy(cfg)
+
+
+# ---------------------------------------------------------------------------
+# Inference factory
+# ---------------------------------------------------------------------------
+
+
+def test_create_inference_engine_sync():
+    from lerobot.rollout import SyncInferenceConfig, SyncInferenceEngine, create_inference_engine
+
+    engine = create_inference_engine(
+        SyncInferenceConfig(),
+        policy=MagicMock(),
+        preprocessor=MagicMock(),
+        postprocessor=MagicMock(),
+        robot_wrapper=MagicMock(robot_type="mock"),
+        hw_features={},
+        dataset_features={},
+        ordered_action_keys=["k"],
+        task="test",
+        fps=30.0,
+        device="cpu",
+    )
+    assert isinstance(engine, SyncInferenceEngine)
+
+
+# ---------------------------------------------------------------------------
+# Pure functions
+# ---------------------------------------------------------------------------
+
+
+def test_estimate_max_episode_seconds_no_video():
+    from lerobot.rollout.strategies import estimate_max_episode_seconds
+
+    assert estimate_max_episode_seconds({}, fps=30.0) == 600.0
+
+
+def test_estimate_max_episode_seconds_with_video():
+    from lerobot.rollout.strategies import estimate_max_episode_seconds
+
+    features = {"cam": {"dtype": "video", "shape": (3, 480, 640)}}
+    result = estimate_max_episode_seconds(features, fps=30.0)
+    assert result > 0
+    # With a real camera, duration should differ from the fallback
+    assert result != 600.0
+
+
+def test_safe_push_to_hub():
+    from lerobot.rollout.strategies import safe_push_to_hub
+
+    ds = MagicMock()
+    ds.num_episodes = 0
+    assert safe_push_to_hub(ds) is False
+    ds.push_to_hub.assert_not_called()
+
+    ds.num_episodes = 5
+    assert safe_push_to_hub(ds, tags=["test"]) is True
+    ds.push_to_hub.assert_called_once_with(tags=["test"], private=False)
+
+
+# ---------------------------------------------------------------------------
+# DAgger state machine
+# ---------------------------------------------------------------------------
+
+
+def test_dagger_full_transition_cycle():
+    from lerobot.rollout.strategies import DAggerEvents, DAggerPhase
+
+    events = DAggerEvents()
+    assert events.phase == DAggerPhase.AUTONOMOUS
+
+    # AUTONOMOUS -> PAUSED
+    events.request_transition("pause_resume")
+    old, new = events.consume_transition()
+    assert (old, new) == (DAggerPhase.AUTONOMOUS, DAggerPhase.PAUSED)
+
+    # PAUSED -> CORRECTING
+    events.request_transition("correction")
+    old, new = events.consume_transition()
+    assert (old, new) == (DAggerPhase.PAUSED, DAggerPhase.CORRECTING)
+
+    # CORRECTING -> PAUSED
+    events.request_transition("correction")
+    old, new = events.consume_transition()
+    assert (old, new) == (DAggerPhase.CORRECTING, DAggerPhase.PAUSED)
+
+    # PAUSED -> AUTONOMOUS
+    events.request_transition("pause_resume")
+    old, new = events.consume_transition()
+    assert (old, new) == (DAggerPhase.PAUSED, DAggerPhase.AUTONOMOUS)
+
+
+def test_dagger_invalid_transition_ignored():
+    from lerobot.rollout.strategies import DAggerEvents, DAggerPhase
+
+    events = DAggerEvents()
+    events.request_transition("correction")  # Not valid from AUTONOMOUS
+    assert events.consume_transition() is None
+    assert events.phase == DAggerPhase.AUTONOMOUS
+
+
+def test_dagger_events_reset():
+    from lerobot.rollout.strategies import DAggerEvents, DAggerPhase
+
+    events = DAggerEvents()
+    events.request_transition("pause_resume")
+    events.consume_transition()  # -> PAUSED
+    events.upload_requested.set()
+    events.reset()
+    assert events.phase == DAggerPhase.AUTONOMOUS
+    assert not events.upload_requested.is_set()
+
+
+# ---------------------------------------------------------------------------
+# Context dataclass
+# ---------------------------------------------------------------------------
+
+
+def test_rollout_context_fields():
+    from lerobot.rollout import RolloutContext
+
+    field_names = {f.name for f in dataclasses.fields(RolloutContext)}
+    assert field_names == {"runtime", "hardware", "policy", "processors", "data"}

tests/utils/test_process.py

@@ -24,7 +24,7 @@ import pytest
 
 pytest.importorskip("grpc")
 
-from lerobot.rl.process import ProcessSignalHandler  # noqa: E402
+from lerobot.utils.process import ProcessSignalHandler  # noqa: E402
 
 
 # Fixture to reset shutdown_event_counter and original signal handlers before and after each test

uv.lock

@@ -2,30 +2,39 @@ version = 1
 revision = 2
 requires-python = ">=3.12"
 resolution-markers = [
-    "python_full_version >= '3.14' and platform_machine != 'aarch64' and platform_machine != 'arm64' and platform_machine != 'armv7l' and platform_machine != 's390x' and sys_platform == 'linux'",
-    "python_full_version >= '3.14' and platform_machine == 's390x' and sys_platform == 'linux'",
+    "python_full_version >= '3.15' and platform_machine != 'aarch64' and platform_machine != 'arm64' and platform_machine != 'armv7l' and platform_machine != 's390x' and sys_platform == 'linux'",
+    "python_full_version >= '3.15' and platform_machine == 's390x' and sys_platform == 'linux'",
+    "python_full_version == '3.14.*' and platform_machine != 'aarch64' and platform_machine != 'arm64' and platform_machine != 'armv7l' and platform_machine != 's390x' and sys_platform == 'linux'",
     "python_full_version == '3.13.*' and platform_machine != 'aarch64' and platform_machine != 'arm64' and platform_machine != 'armv7l' and platform_machine != 's390x' and sys_platform == 'linux'",
+    "python_full_version == '3.14.*' and platform_machine == 's390x' and sys_platform == 'linux'",
     "python_full_version == '3.13.*' and platform_machine == 's390x' and sys_platform == 'linux'",
     "python_full_version < '3.13' and platform_machine != 'aarch64' and platform_machine != 'arm64' and platform_machine != 'armv7l' and platform_machine != 's390x' and sys_platform == 'linux'",
     "python_full_version < '3.13' and platform_machine == 's390x' and sys_platform == 'linux'",
-    "(python_full_version >= '3.14' and platform_machine == 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.14' and platform_machine == 'arm64' and sys_platform == 'linux') or (python_full_version >= '3.14' and platform_machine == 'armv7l' and sys_platform == 'linux')",
+    "(python_full_version >= '3.15' and platform_machine == 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.15' and platform_machine == 'arm64' and sys_platform == 'linux') or (python_full_version >= '3.15' and platform_machine == 'armv7l' and sys_platform == 'linux')",
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