fix(rl): enhance intervention handling in actor and learner

Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com>

.github/workflows/model_profiling.yml

@@ -1,237 +0,0 @@
-# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-name: Model Profiling
-
-on:
-  schedule:
-    - cron: "0 0 * * 0"
-  pull_request:
-    branches:
-      - main
-    paths:
-      - .github/workflows/model_profiling.yml
-      - src/lerobot/configs/train.py
-      - src/lerobot/scripts/lerobot_train.py
-      - src/lerobot/utils/model_profiling.py
-      - tests/test_model_profiling.py
-  workflow_dispatch:
-    inputs:
-      git_ref:
-        description: Git ref to profile when no commit SHA is provided
-        required: false
-        type: string
-        default: main
-      git_commit:
-        description: Optional exact commit SHA to profile
-        required: false
-        type: string
-        default: ""
-      policies:
-        description: Optional comma-separated policy filter
-        required: false
-        type: string
-        default: ""
-      profile_mode:
-        description: Torch profiler mode
-        required: false
-        type: choice
-        options:
-          - trace
-          - summary
-        default: trace
-      publish_results:
-        description: Publish results to the profiling dataset when a Hub token is available
-        required: false
-        type: boolean
-        default: true
-      results_repo:
-        description: Dataset repo name or fully qualified repo id
-        required: false
-        type: string
-        default: model-profiling-history
-
-permissions:
-  contents: read
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.event_name }}-${{ github.event.inputs.git_commit || github.event.inputs.git_ref || github.ref_name || github.run_id }}
-  cancel-in-progress: true
-
-jobs:
-  profile-models:
-    name: Weekly Model Profiling
-    runs-on:
-      group: aws-g6-4xlarge-plus
-    env:
-      HF_USER_TOKEN: ${{ secrets.LEROBOT_HF_USER }}
-      PROFILE_MODE: ${{ github.event_name == 'pull_request' && 'summary' || github.event.inputs.profile_mode || 'trace' }}
-      POLICY_FILTER: ${{ github.event_name == 'pull_request' && 'act,diffusion,pi0,pi05,smolvla,groot,xvla,wall_x' || github.event.inputs.policies || '' }}
-      RESULTS_REPO: ${{ github.event.inputs.results_repo || 'model-profiling-history' }}
-      SHOULD_PUBLISH: ${{ github.event_name == 'schedule' || (github.event_name == 'workflow_dispatch' && github.event.inputs.publish_results == 'true') }}
-    steps:
-      - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd  # v6.0.2
-        with:
-          persist-credentials: false
-          lfs: true
-          ref: ${{ github.event.pull_request.head.sha || github.event.inputs.git_commit || github.event.inputs.git_ref || 'main' }}
-
-      - name: Pull GPU image
-        run: docker pull huggingface/lerobot-gpu:latest
-
-      - name: Run model profiling
-        env:
-          HOST_GIT_COMMIT: ${{ github.event.pull_request.head.sha || github.event.inputs.git_commit || github.sha }}
-          PROFILE_GIT_REF: ${{ github.head_ref || github.ref_name || github.event.inputs.git_ref || 'main' }}
-          PROFILE_PR_NUMBER: ${{ github.event.pull_request.number || '' }}
-        run: |
-          set -eux
-          mkdir -p profiling-results
-          docker run --rm --gpus all \
-            --user "$(id -u):$(id -g)" \
-            --shm-size=16g \
-            -e HOME=/tmp/lerobot-home \
-            -e HF_HOME=/tmp/hf \
-            -e HF_LEROBOT_HOME=/tmp/hf-lerobot \
-            -e TORCH_HOME=/tmp/torch-home \
-            -e TORCHINDUCTOR_CACHE_DIR=/tmp/torchinductor-cache \
-            -e UV_PROJECT_ENVIRONMENT=/tmp/lerobot-venv \
-            -e UV_CACHE_DIR=/tmp/uv-cache \
-            -e UV_PYTHON_PREFERENCE=only-system \
-            -e XDG_DATA_HOME=/tmp/xdg-data \
-            -e XDG_CACHE_HOME=/tmp/xdg-cache \
-            -e HOST_GIT_COMMIT="${HOST_GIT_COMMIT}" \
-            -e PROFILE_GIT_REF="${PROFILE_GIT_REF}" \
-            -e PROFILE_PR_NUMBER="${PROFILE_PR_NUMBER}" \
-            -e HF_USER_TOKEN="${HF_USER_TOKEN}" \
-            -e HF_TOKEN="${HF_USER_TOKEN}" \
-            -e PROFILE_MODE="${PROFILE_MODE}" \
-            -e POLICY_FILTER="${POLICY_FILTER}" \
-            -e RESULTS_REPO="${RESULTS_REPO}" \
-            -e SHOULD_PUBLISH="${SHOULD_PUBLISH}" \
-            -v "${GITHUB_WORKSPACE}:/workspace" \
-            -w /workspace \
-            huggingface/lerobot-gpu:latest \
-            bash -c '
-              set -euxo pipefail
-              mkdir -p "${HOME}" "${HF_HOME}" "${HF_LEROBOT_HOME}" "${TORCH_HOME}" "${UV_CACHE_DIR}" "${XDG_CACHE_HOME}" "${XDG_DATA_HOME}" "${TORCHINDUCTOR_CACHE_DIR}"
-              rm -rf /tmp/lerobot-src
-              cp -a /workspace/. /tmp/lerobot-src
-              cd /tmp/lerobot-src
-
-              if [[ -n "${HF_USER_TOKEN:-}" ]]; then
-                hf auth login --token "${HF_USER_TOKEN}" --add-to-git-credential 2>/dev/null || true
-              fi
-
-              policies_to_run=()
-              if [[ -n "${POLICY_FILTER}" ]]; then
-                IFS="," read -ra policies_to_run <<< "${POLICY_FILTER}"
-              else
-                policies_to_run=(act diffusion groot multi_task_dit pi0 pi0_fast pi05 smolvla wall_x xvla)
-              fi
-
-              policy_extras() {
-                case "$1" in
-                  act) ;;
-                  diffusion) echo "diffusion" ;;
-                  groot) echo "groot" ;;
-                  multi_task_dit) echo "multi_task_dit" ;;
-                  pi0|pi0_fast|pi05) echo "pi" ;;
-                  smolvla) echo "smolvla" ;;
-                  wall_x) echo "wallx" ;;
-                  xvla) echo "xvla" ;;
-                  *)
-                    echo "Unknown profiling policy $1" >&2
-                    return 1
-                    ;;
-                esac
-              }
-
-              # Policies whose dep-install may fail due to environment constraints
-              # (e.g. groot requires compiling flash-attn, which needs nvcc; the CI
-              # image only ships the CUDA runtime). Install failures for these are
-              # logged as warnings and do not fail the job. See the TODO next to
-              # `lerobot[groot]` in pyproject.toml.
-              is_install_failure_tolerated() {
-                case "$1" in
-                  groot) return 0 ;;
-                  *) return 1 ;;
-                esac
-              }
-
-              overall_status=0
-              for raw_policy in "${policies_to_run[@]}"; do
-                policy="$(echo "${raw_policy}" | xargs)"
-                [[ -z "${policy}" ]] && continue
-
-                echo "::group::Profile ${policy}"
-
-                extra="$(policy_extras "${policy}")" || { overall_status=1; echo "::endgroup::"; continue; }
-
-                # Fresh, isolated dependency resolution per policy so that
-                # incompatible extras (e.g. flash-attn for groot) never block
-                # the rest of the matrix.
-                sync_cmd=(uv sync --locked --extra training --extra test)
-                if [[ -n "${extra}" ]]; then
-                  sync_cmd+=(--extra "${extra}")
-                fi
-                # flash-attn does not declare torch as a build-time dep, so its
-                # isolated build env fails with ModuleNotFoundError. Torch is a
-                # core lerobot dep and is already resolved here, so we disable
-                # build isolation for flash-attn specifically.
-                sync_cmd+=(--no-build-isolation-package flash-attn)
-                if ! "${sync_cmd[@]}"; then
-                  if is_install_failure_tolerated "${policy}"; then
-                    echo "::warning::Dependency install failed for ${policy} (known-fragile); skipping."
-                  else
-                    echo "Dependency install failed for ${policy}; skipping." >&2
-                    overall_status=1
-                  fi
-                  echo "::endgroup::"
-                  continue
-                fi
-
-                cmd=(
-                  uv run python -m lerobot.utils.model_profiling
-                  --output_dir=/workspace/profiling-results
-                  --hub_org=lerobot
-                  --results_repo="${RESULTS_REPO}"
-                  --profile_mode="${PROFILE_MODE}"
-                  --git_commit="${HOST_GIT_COMMIT}"
-                  --git_ref="${PROFILE_GIT_REF}"
-                  --pr_number="${PROFILE_PR_NUMBER}"
-                  --policies "${policy}"
-                )
-                if [[ "${SHOULD_PUBLISH}" == "true" && -n "${HF_USER_TOKEN:-}" ]]; then
-                  cmd+=(--publish)
-                fi
-
-                if ! "${cmd[@]}"; then
-                  echo "Profiling failed for ${policy}." >&2
-                  overall_status=1
-                fi
-
-                echo "::endgroup::"
-              done
-
-              exit "${overall_status}"
-            '
-
-      - name: Upload profiling artifacts
-        if: always()
-        uses: actions/upload-artifact@v4 # zizmor: ignore[unpinned-uses]
-        with:
-          name: model-profiling-results
-          path: profiling-results
-          if-no-files-found: warn

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/xvla.mdx

@@ -220,7 +220,7 @@ REAL_DIM = 12
 # Postprocessing: Trim 20D predictions to 12D for deployment
 ```
 
-See the [action_hub.py](/home/jade_choghari/robot/lerobot/src/lerobot/policies/xvla/action_hub.py) implementation for details.
+See the [action_hub.py](https://github.com/huggingface/lerobot/blob/main/src/lerobot/policies/xvla/action_hub.py) implementation for details.
 
 #### Auto Action Mode (Recommended)
 
@@ -519,9 +519,9 @@ If you use X-VLA in your research, please cite:
 
 - [X-VLA Paper](https://arxiv.org/pdf/2510.10274)
 - [LeRobot Documentation](https://github.com/huggingface/lerobot)
-- [Action Registry Implementation](https://github.com/huggingface/lerobot/src/lerobot/policies/xvla/action_hub.py)
-- [Processor Implementation](https://github.com/huggingface/lerobot/src/lerobot/policies/xvla/processor_xvla.py)
-- [Model Configuration](https://github.com/huggingface/lerobot/src/lerobot/policies/xvla/configuration_xvla.py)
+- [Action Registry Implementation](https://github.com/huggingface/lerobot/blob/main/src/lerobot/policies/xvla/action_hub.py)
+- [Processor Implementation](https://github.com/huggingface/lerobot/blob/main/src/lerobot/policies/xvla/processor_xvla.py)
+- [Model Configuration](https://github.com/huggingface/lerobot/blob/main/src/lerobot/policies/xvla/configuration_xvla.py)
 
 ## Contributing
 

src/lerobot/cameras/realsense/camera_realsense.py

@@ -17,6 +17,7 @@ Provides the RealSenseCamera class for capturing frames from Intel RealSense cam
 """
 
 import logging
+import sys
 import time
 from threading import Event, Lock, Thread
 from typing import TYPE_CHECKING, Any
@@ -41,6 +42,7 @@ from ..utils import get_cv2_rotation
 from .configuration_realsense import RealSenseCameraConfig
 
 logger = logging.getLogger(__name__)
+pkg_name = "pyrealsense2-macosx" if sys.platform == "darwin" else "pyrealsense2"
 
 
 class RealSenseCamera(Camera):
@@ -114,7 +116,7 @@ class RealSenseCamera(Camera):
         Args:
             config: The configuration settings for the camera.
         """
-        require_package("pyrealsense2", extra="intelrealsense")
+        require_package(pkg_name, extra="intelrealsense", import_name="pyrealsense2")
         super().__init__(config)
 
         self.config = config

src/lerobot/configs/train.py

@@ -16,7 +16,7 @@ import datetime as dt
 import os
 from dataclasses import dataclass, field
 from pathlib import Path
-from typing import Any, Literal
+from typing import Any
 
 import draccus
 from huggingface_hub import hf_hub_download
@@ -58,8 +58,6 @@ class TrainPipelineConfig(HubMixin):
     batch_size: int = 8
     prefetch_factor: int = 4
     persistent_workers: bool = True
-    profile_mode: Literal["off", "summary", "trace"] = "off"
-    profile_output_dir: Path | None = None
     steps: int = 100_000
     eval_freq: int = 20_000
     log_freq: int = 200
@@ -132,15 +130,9 @@ class TrainPipelineConfig(HubMixin):
             now = dt.datetime.now()
             train_dir = f"{now:%Y-%m-%d}/{now:%H-%M-%S}_{self.job_name}"
             self.output_dir = Path("outputs/train") / train_dir
-        if self.profile_mode != "off" and self.profile_output_dir is None:
-            self.profile_output_dir = self.output_dir / "profiling"
 
         if isinstance(self.dataset.repo_id, list):
             raise NotImplementedError("LeRobotMultiDataset is not currently implemented.")
-        if self.profile_mode not in {"off", "summary", "trace"}:
-            raise ValueError(
-                f"`profile_mode` must be one of 'off', 'summary', or 'trace', got {self.profile_mode}."
-            )
 
         if not self.use_policy_training_preset and (self.optimizer is None or self.scheduler is None):
             raise ValueError("Optimizer and Scheduler must be set when the policy presets are not used.")

src/lerobot/policies/act/modeling_act.py

@@ -142,9 +142,10 @@ class ACTPolicy(PreTrainedPolicy):
 
         actions_hat, (mu_hat, log_sigma_x2_hat) = self.model(batch)
 
-        l1_loss = (
-            F.l1_loss(batch[ACTION], actions_hat, reduction="none") * ~batch["action_is_pad"].unsqueeze(-1)
-        ).mean()
+        abs_err = F.l1_loss(batch[ACTION], actions_hat, reduction="none")
+        valid_mask = ~batch["action_is_pad"].unsqueeze(-1)
+        num_valid = valid_mask.sum() * abs_err.shape[-1]
+        l1_loss = (abs_err * valid_mask).sum() / num_valid.clamp_min(1)
 
         loss_dict = {"l1_loss": l1_loss.item()}
         if self.config.use_vae:

src/lerobot/policies/diffusion/modeling_diffusion.py

@@ -380,7 +380,9 @@ class DiffusionModel(nn.Module):
                     f"{self.config.do_mask_loss_for_padding=}."
                 )
             in_episode_bound = ~batch["action_is_pad"]
-            loss = loss * in_episode_bound.unsqueeze(-1)
+            mask = in_episode_bound.unsqueeze(-1)
+            num_valid = mask.sum() * loss.shape[-1]
+            return (loss * mask).sum() / num_valid.clamp_min(1)
 
         return loss.mean()
 

src/lerobot/policies/groot/groot_n1.py

@@ -13,7 +13,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from dataclasses import dataclass, field
 from pathlib import Path
 from typing import TYPE_CHECKING
 
@@ -174,17 +173,14 @@ N_COLOR_CHANNELS = 3
 
 
 # config
-@dataclass
 class GR00TN15Config(PretrainedConfig):
     model_type = "gr00t_n1_5"
-    backbone_cfg: dict = field(init=False, metadata={"help": "Backbone configuration."})
 
-    action_head_cfg: dict = field(init=False, metadata={"help": "Action head configuration."})
-
-    action_horizon: int = field(init=False, metadata={"help": "Action horizon."})
-
-    action_dim: int = field(init=False, metadata={"help": "Action dimension."})
-    compute_dtype: str = field(default="float32", metadata={"help": "Compute dtype."})
+    backbone_cfg: dict
+    action_head_cfg: dict
+    action_horizon: int
+    action_dim: int
+    compute_dtype: str = "float32"
 
     def __init__(self, **kwargs):
         super().__init__(**kwargs)

src/lerobot/policies/multi_task_dit/modeling_multi_task_dit.py

@@ -688,8 +688,9 @@ class DiffusionObjective(nn.Module):
         loss = F.mse_loss(predicted, target, reduction="none")
 
         if self.do_mask_loss_for_padding and "action_is_pad" in batch:
-            valid_actions = ~batch["action_is_pad"]
-            loss = loss * valid_actions.unsqueeze(-1)
+            mask = ~batch["action_is_pad"].unsqueeze(-1)
+            num_valid = mask.sum() * loss.shape[-1]
+            return (loss * mask).sum() / num_valid.clamp_min(1)
 
         return loss.mean()
 
@@ -752,8 +753,9 @@ class FlowMatchingObjective(nn.Module):
         loss = F.mse_loss(predicted_velocity, target_velocity, reduction="none")
 
         if self.do_mask_loss_for_padding and "action_is_pad" in batch:
-            valid_mask = ~batch["action_is_pad"]
-            loss = loss * valid_mask.unsqueeze(-1)
+            mask = ~batch["action_is_pad"].unsqueeze(-1)
+            num_valid = mask.sum() * loss.shape[-1]
+            return (loss * mask).sum() / num_valid.clamp_min(1)
 
         return loss.mean()
 

src/lerobot/policies/sarm/processor_sarm.py

@@ -455,7 +455,13 @@ class SARMEncodingProcessorStep(ProcessorStep):
             inputs = {k: v.to(self.device) for k, v in inputs.items()}
 
             # Get image embeddings
-            embeddings = self.clip_model.get_image_features(**inputs).detach().cpu()
+            # transformers 5.x returns BaseModelOutputWithPooling instead of a plain tensor
+            output = self.clip_model.get_image_features(**inputs)
+            if not isinstance(output, torch.Tensor):
+                output = output.pooler_output
+                if output is None:
+                    raise ValueError("pooler_output should not be None for CLIP models.")
+            embeddings = output.detach().cpu()
 
             # Handle single frame case
             if embeddings.dim() == 1:
@@ -482,7 +488,13 @@ class SARMEncodingProcessorStep(ProcessorStep):
         inputs = self.clip_processor.tokenizer([text], return_tensors="pt", padding=True, truncation=True)
         inputs = {k: v.to(self.device) for k, v in inputs.items()}
 
-        text_embedding = self.clip_model.get_text_features(**inputs).detach().cpu()
+        # transformers 5.x returns BaseModelOutputWithPooling instead of a plain tensor
+        output = self.clip_model.get_text_features(**inputs)
+        if not isinstance(output, torch.Tensor):
+            output = output.pooler_output
+            if output is None:
+                raise ValueError("pooler_output should not be None for CLIP models.")
+        text_embedding = output.detach().cpu()
         text_embedding = text_embedding.expand(batch_size, -1)
 
         return text_embedding

src/lerobot/policies/smolvla/modeling_smolvla.py

@@ -394,13 +394,21 @@ class SmolVLAPolicy(PreTrainedPolicy):
         loss_dict["losses_after_rm_padding"] = losses.clone().mean().item()
 
         if reduction == "none":
-            # Return per-sample losses (B,) by averaging over time and action dims
-            per_sample_loss = losses.mean(dim=(1, 2))
+            # Return per-sample losses (B,) by averaging over valid (time, action) entries
+            if actions_is_pad is None:
+                per_sample_loss = losses.mean(dim=(1, 2))
+            else:
+                num_valid = ((~actions_is_pad).sum(dim=1) * losses.shape[-1]).clamp_min(1)
+                per_sample_loss = losses.sum(dim=(1, 2)) / num_valid
             loss_dict["loss"] = per_sample_loss.mean().item()
             return per_sample_loss, loss_dict
         else:
-            # Default: return scalar mean loss
-            loss = losses.mean()
+            # Default: return scalar mean loss over valid (time, action) entries
+            if actions_is_pad is None:
+                loss = losses.mean()
+            else:
+                num_valid = ((~actions_is_pad).sum() * losses.shape[-1]).clamp_min(1)
+                loss = losses.sum() / num_valid
             loss_dict["loss"] = loss.item()
             return loss, loss_dict
 
@@ -655,6 +663,7 @@ class VLAFlowMatching(nn.Module):
                 pad_masks.append(image_start_mask)
 
             img_emb = self.vlm_with_expert.embed_image(img)
+            img_emb = img_emb
 
             # Normalize image embeddings
             img_emb_dim = img_emb.shape[-1]

src/lerobot/processor/hil_processor.py

@@ -321,6 +321,7 @@ class GymHILAdapterProcessorStep(ProcessorStep):
     This step normalizes the `transition` object by:
     1. Copying `teleop_action` from `info` to `complementary_data`.
     2. Copying `is_intervention` from `info` (using the string key) to `info` (using the enum key).
+    3. Copying `discrete_penalty` from `info` to `complementary_data`.
     """
 
     def __call__(self, transition: EnvTransition) -> EnvTransition:
@@ -330,6 +331,9 @@ class GymHILAdapterProcessorStep(ProcessorStep):
         if TELEOP_ACTION_KEY in info:
             complementary_data[TELEOP_ACTION_KEY] = info[TELEOP_ACTION_KEY]
 
+        if DISCRETE_PENALTY_KEY in info:
+            complementary_data[DISCRETE_PENALTY_KEY] = info[DISCRETE_PENALTY_KEY]
+
         if "is_intervention" in info:
             info[TeleopEvents.IS_INTERVENTION] = info["is_intervention"]
 
@@ -348,18 +352,24 @@ class GymHILAdapterProcessorStep(ProcessorStep):
 @ProcessorStepRegistry.register("gripper_penalty_processor")
 class GripperPenaltyProcessorStep(ProcessorStep):
     """
-    Applies a penalty for inefficient gripper usage.
+    Applies a small per-transition cost on the discrete gripper action.
 
-    This step penalizes actions that attempt to close an already closed gripper or
-    open an already open one, based on position thresholds.
+    Fires only when the commanded action would actually transition the gripper
+    from one extreme to the other (close-while-open or open-while-closed).
+    This discourages gripper oscillation while leaving "stay" and saturating-further
+    commands unpenalized.
 
     Attributes:
         penalty: The negative reward value to apply.
         max_gripper_pos: The maximum position value for the gripper, used for normalization.
+        open_threshold: Normalized state below which the gripper is considered "open".
+        closed_threshold: Normalized state above which the gripper is considered "closed".
     """
 
-    penalty: float = -0.01
+    penalty: float = -0.02
     max_gripper_pos: float = 30.0
+    open_threshold: float = 0.1
+    closed_threshold: float = 0.9
 
     def __call__(self, transition: EnvTransition) -> EnvTransition:
         """
@@ -391,9 +401,13 @@ class GripperPenaltyProcessorStep(ProcessorStep):
         gripper_state_normalized = current_gripper_pos / self.max_gripper_pos
 
         # Calculate penalty boolean as in original
-        gripper_penalty_bool = (gripper_state_normalized < 0.5 and gripper_action_normalized > 0.5) or (
-            gripper_state_normalized > 0.75 and gripper_action_normalized < 0.5
-        )
+        #   - currently open  AND target is closed  -> close transition
+        #   - currently closed AND target is open   -> open transition
+        is_open = gripper_state_normalized < self.open_threshold
+        is_closed = gripper_state_normalized > self.closed_threshold
+        cmd_close = gripper_action_normalized > self.closed_threshold
+        cmd_open = gripper_action_normalized < self.open_threshold
+        gripper_penalty_bool = (is_open and cmd_close) or (is_closed and cmd_open)
 
         gripper_penalty = self.penalty * int(gripper_penalty_bool)
 
@@ -409,11 +423,14 @@ class GripperPenaltyProcessorStep(ProcessorStep):
         Returns the configuration of the step for serialization.
 
         Returns:
-            A dictionary containing the penalty value and max gripper position.
+            A dictionary containing the penalty value, max gripper position,
+            and the open/closed thresholds.
         """
         return {
             "penalty": self.penalty,
             "max_gripper_pos": self.max_gripper_pos,
+            "open_threshold": self.open_threshold,
+            "closed_threshold": self.closed_threshold,
         }
 
     def reset(self) -> None:

src/lerobot/processor/normalize_processor.py

@@ -134,6 +134,15 @@ class _NormalizationMixin:
         if self.dtype is None:
             self.dtype = torch.float32
         self._tensor_stats = to_tensor(self.stats, device=self.device, dtype=self.dtype)
+        self._reshape_visual_stats()
+
+    def _reshape_visual_stats(self) -> None:
+        """Reshape visual stats from ``[C]`` to ``[C, 1, 1]`` for image broadcasting."""
+        for key, feature in self.features.items():
+            if feature.type == FeatureType.VISUAL and key in self._tensor_stats:
+                for stat_name, stat_tensor in self._tensor_stats[key].items():
+                    if isinstance(stat_tensor, Tensor) and stat_tensor.ndim == 1:
+                        self._tensor_stats[key][stat_name] = stat_tensor.reshape(-1, 1, 1)
 
     def to(
         self, device: torch.device | str | None = None, dtype: torch.dtype | None = None
@@ -152,6 +161,7 @@ class _NormalizationMixin:
         if dtype is not None:
             self.dtype = dtype
         self._tensor_stats = to_tensor(self.stats, device=self.device, dtype=self.dtype)
+        self._reshape_visual_stats()
         return self
 
     def state_dict(self) -> dict[str, Tensor]:
@@ -201,6 +211,7 @@ class _NormalizationMixin:
             # Don't load from state_dict, keep the explicitly provided stats
             # But ensure _tensor_stats is properly initialized
             self._tensor_stats = to_tensor(self.stats, device=self.device, dtype=self.dtype)  # type: ignore[assignment]
+            self._reshape_visual_stats()
             return
 
         # Normal behavior: load stats from state_dict
@@ -211,6 +222,7 @@ class _NormalizationMixin:
             self._tensor_stats.setdefault(key, {})[stat_name] = tensor.to(
                 dtype=torch.float32, device=self.device
             )
+        self._reshape_visual_stats()
 
         # Reconstruct the original stats dict from tensor stats for compatibility with to() method
         # and other functions that rely on self.stats

src/lerobot/rl/actor.py

@@ -60,7 +60,7 @@ from torch.multiprocessing import Event, Queue
 from lerobot.cameras import opencv  # noqa: F401
 from lerobot.configs import parser
 from lerobot.configs.train import TrainRLServerPipelineConfig
-from lerobot.policies import make_policy
+from lerobot.policies import make_policy, make_pre_post_processors
 from lerobot.policies.sac.modeling_sac import SACPolicy
 from lerobot.robots import so_follower  # noqa: F401
 from lerobot.teleoperators import gamepad, so_leader  # noqa: F401
@@ -89,9 +89,9 @@ from lerobot.utils.utils import (
 )
 
 from .gym_manipulator import (
-    create_transition,
     make_processors,
     make_robot_env,
+    reset_and_build_transition,
     step_env_and_process_transition,
 )
 from .process import ProcessSignalHandler
@@ -261,13 +261,12 @@ def act_with_policy(
     policy = policy.eval()
     assert isinstance(policy, nn.Module)
 
-    obs, info = online_env.reset()
-    env_processor.reset()
-    action_processor.reset()
+    preprocessor, postprocessor = make_pre_post_processors(
+        policy_cfg=cfg.policy,
+        dataset_stats=cfg.policy.dataset_stats,
+    )
 
-    # Process initial observation
-    transition = create_transition(observation=obs, info=info)
-    transition = env_processor(transition)
+    transition = reset_and_build_transition(online_env, env_processor, action_processor)
 
     # NOTE: For the moment we will solely handle the case of a single environment
     sum_reward_episode = 0
@@ -291,8 +290,21 @@ def act_with_policy(
 
         # Time policy inference and check if it meets FPS requirement
         with policy_timer:
-            # Extract observation from transition for policy
-            action = policy.select_action(batch=observation)
+            normalized_observation = preprocessor.process_observation(observation)
+            action = policy.select_action(batch=normalized_observation)
+            # Unnormalize only the continuous part. When `num_discrete_actions` is set,
+            # `select_action` concatenates an argmax index in env space at the last dim;
+            # action stats cover the continuous dims only, so feeding the full vector to
+            # the unnormalizer would shape-mismatch and would also corrupt the discrete
+            # index by treating it as a normalized value.
+            if cfg.policy.num_discrete_actions is not None:
+                continuous_action = postprocessor.process_action(action[..., :-1])
+                discrete_action = action[..., -1:].to(
+                    device=continuous_action.device, dtype=continuous_action.dtype
+                )
+                action = torch.cat([continuous_action, discrete_action], dim=-1)
+            else:
+                action = postprocessor.process_action(action)
         policy_fps = policy_timer.fps_last
 
         log_policy_frequency_issue(policy_fps=policy_fps, cfg=cfg, interaction_step=interaction_step)
@@ -326,7 +338,8 @@ def act_with_policy(
 
         # Check for intervention from transition info
         intervention_info = new_transition[TransitionKey.INFO]
-        if intervention_info.get(TeleopEvents.IS_INTERVENTION, False):
+        is_intervention = bool(intervention_info.get(TeleopEvents.IS_INTERVENTION, False))
+        if is_intervention:
             episode_intervention = True
             episode_intervention_steps += 1
 
@@ -334,6 +347,10 @@ def act_with_policy(
             "discrete_penalty": torch.tensor(
                 [new_transition[TransitionKey.COMPLEMENTARY_DATA].get("discrete_penalty", 0.0)]
             ),
+            # Forward the intervention flag so the learner can route this transition
+            # into the offline replay buffer (see `process_transitions` in learner.py).
+            # Use the plain string key so the payload survives torch.load(weights_only=True).
+            TeleopEvents.IS_INTERVENTION.value: is_intervention,
         }
         # Create transition for learner (convert to old format)
         list_transition_to_send_to_learner.append(
@@ -390,14 +407,7 @@ def act_with_policy(
             episode_intervention_steps = 0
             episode_total_steps = 0
 
-            # Reset environment and processors
-            obs, info = online_env.reset()
-            env_processor.reset()
-            action_processor.reset()
-
-            # Process initial observation
-            transition = create_transition(observation=obs, info=info)
-            transition = env_processor(transition)
+            transition = reset_and_build_transition(online_env, env_processor, action_processor)
 
         if cfg.env.fps is not None:
             dt_time = time.perf_counter() - start_time

src/lerobot/rl/gym_manipulator.py

@@ -383,10 +383,21 @@ def make_processors(
             GymHILAdapterProcessorStep(),
             Numpy2TorchActionProcessorStep(),
             VanillaObservationProcessorStep(),
-            AddBatchDimensionProcessorStep(),
-            DeviceProcessorStep(device=device),
         ]
 
+        # Add time limit processor if reset config exists
+        if cfg.processor.reset is not None:
+            env_pipeline_steps.append(
+                TimeLimitProcessorStep(max_episode_steps=int(cfg.processor.reset.control_time_s * cfg.fps))
+            )
+
+        env_pipeline_steps.extend(
+            [
+                AddBatchDimensionProcessorStep(),
+                DeviceProcessorStep(device=device),
+            ]
+        )
+
         return DataProcessorPipeline(
             steps=env_pipeline_steps, to_transition=identity_transition, to_output=identity_transition
         ), DataProcessorPipeline(
@@ -551,8 +562,19 @@ def step_env_and_process_transition(
     terminated = terminated or processed_action_transition[TransitionKey.DONE]
     truncated = truncated or processed_action_transition[TransitionKey.TRUNCATED]
     complementary_data = processed_action_transition[TransitionKey.COMPLEMENTARY_DATA].copy()
+
+    if hasattr(env, "get_raw_joint_positions"):
+        raw_joint_positions = env.get_raw_joint_positions()
+        if raw_joint_positions is not None:
+            complementary_data["raw_joint_positions"] = raw_joint_positions
+
+    # Merge env and action-processor info: env wins for str keys, action-processor
+    # wins for `TeleopEvents` enum keys
+    action_info = processed_action_transition[TransitionKey.INFO]
     new_info = info.copy()
-    new_info.update(processed_action_transition[TransitionKey.INFO])
+    for key, value in action_info.items():
+        if isinstance(key, TeleopEvents):
+            new_info[key] = value
 
     new_transition = create_transition(
         observation=obs,
@@ -568,6 +590,24 @@ def step_env_and_process_transition(
     return new_transition
 
 
+def reset_and_build_transition(
+    env: gym.Env,
+    env_processor: DataProcessorPipeline[EnvTransition, EnvTransition],
+    action_processor: DataProcessorPipeline[EnvTransition, EnvTransition],
+) -> EnvTransition:
+    """Reset env + processors and return the first env-processed transition."""
+    obs, info = env.reset()
+    env_processor.reset()
+    action_processor.reset()
+    complementary_data: dict[str, Any] = {}
+    if hasattr(env, "get_raw_joint_positions"):
+        raw_joint_positions = env.get_raw_joint_positions()
+        if raw_joint_positions is not None:
+            complementary_data["raw_joint_positions"] = raw_joint_positions
+    transition = create_transition(observation=obs, info=info, complementary_data=complementary_data)
+    return env_processor(data=transition)
+
+
 def control_loop(
     env: gym.Env,
     env_processor: DataProcessorPipeline[EnvTransition, EnvTransition],
@@ -593,17 +633,7 @@ def control_loop(
     print("- When not intervening, robot will stay still")
     print("- Press Ctrl+C to exit")
 
-    # Reset environment and processors
-    obs, info = env.reset()
-    complementary_data = (
-        {"raw_joint_positions": info.pop("raw_joint_positions")} if "raw_joint_positions" in info else {}
-    )
-    env_processor.reset()
-    action_processor.reset()
-
-    # Process initial observation
-    transition = create_transition(observation=obs, info=info, complementary_data=complementary_data)
-    transition = env_processor(data=transition)
+    transition = reset_and_build_transition(env, env_processor, action_processor)
 
     # Determine if gripper is used
     use_gripper = cfg.env.processor.gripper.use_gripper if cfg.env.processor.gripper is not None else True
@@ -665,7 +695,7 @@ def control_loop(
         # Create a neutral action (no movement)
         neutral_action = torch.tensor([0.0, 0.0, 0.0], dtype=torch.float32)
         if use_gripper:
-            neutral_action = torch.cat([neutral_action, torch.tensor([0.0])])  # Gripper stay
+            neutral_action = torch.cat([neutral_action, torch.tensor([1.0])])  # Gripper stay
 
         # Use the new step function
         transition = step_env_and_process_transition(
@@ -723,12 +753,7 @@ def control_loop(
                     dataset.save_episode()
 
             # Reset for new episode
-            obs, info = env.reset()
-            env_processor.reset()
-            action_processor.reset()
-
-            transition = create_transition(observation=obs, info=info)
-            transition = env_processor(transition)
+            transition = reset_and_build_transition(env, env_processor, action_processor)
 
         # Maintain fps timing
         precise_sleep(max(dt - (time.perf_counter() - step_start_time), 0.0))

src/lerobot/rl/learner.py

@@ -70,7 +70,7 @@ from lerobot.common.wandb_utils import WandBLogger
 from lerobot.configs import parser
 from lerobot.configs.train import TrainRLServerPipelineConfig
 from lerobot.datasets import LeRobotDataset, make_dataset
-from lerobot.policies import make_policy
+from lerobot.policies import make_policy, make_pre_post_processors
 from lerobot.policies.sac.modeling_sac import SACPolicy
 from lerobot.robots import so_follower  # noqa: F401
 from lerobot.teleoperators import gamepad, so_leader  # noqa: F401
@@ -317,6 +317,11 @@ def add_actor_information_and_train(
 
     policy.train()
 
+    preprocessor, _postprocessor = make_pre_post_processors(
+        policy_cfg=cfg.policy,
+        dataset_stats=cfg.policy.dataset_stats,
+    )
+
     push_actor_policy_to_queue(parameters_queue=parameters_queue, policy=policy)
 
     last_time_policy_pushed = time.time()
@@ -405,8 +410,8 @@ def add_actor_information_and_train(
 
             actions = batch[ACTION]
             rewards = batch["reward"]
-            observations = batch["state"]
-            next_observations = batch["next_state"]
+            observations = preprocessor.process_observation(batch["state"])
+            next_observations = preprocessor.process_observation(batch["next_state"])
             done = batch["done"]
             check_nan_in_transition(observations=observations, actions=actions, next_state=next_observations)
 
@@ -463,8 +468,8 @@ def add_actor_information_and_train(
 
         actions = batch[ACTION]
         rewards = batch["reward"]
-        observations = batch["state"]
-        next_observations = batch["next_state"]
+        observations = preprocessor.process_observation(batch["state"])
+        next_observations = preprocessor.process_observation(batch["next_state"])
         done = batch["done"]
 
         check_nan_in_transition(observations=observations, actions=actions, next_state=next_observations)
@@ -1163,7 +1168,7 @@ def process_transitions(
 
             # Add to offline buffer if it's an intervention
             if dataset_repo_id is not None and transition.get("complementary_info", {}).get(
-                TeleopEvents.IS_INTERVENTION
+                TeleopEvents.IS_INTERVENTION.value
             ):
                 offline_replay_buffer.add(**transition)
 

src/lerobot/robots/so_follower/robot_kinematic_processor.py

@@ -353,7 +353,8 @@ class GripperVelocityToJoint(RobotActionProcessorStep):
         speed_factor: A scaling factor to convert the normalized velocity command to a position change.
         clip_min: The minimum allowed gripper joint position.
         clip_max: The maximum allowed gripper joint position.
-        discrete_gripper: If True, treat the input action as discrete (0: open, 1: close, 2: stay).
+        discrete_gripper: If True, interpret the input as a discrete class index
+            {0 = close, 1 = stay, 2 = open}, matching `GamepadTeleop.GripperAction`.
     """
 
     speed_factor: float = 20.0
@@ -377,10 +378,10 @@ class GripperVelocityToJoint(RobotActionProcessorStep):
             raise ValueError("Joints observation is require for computing robot kinematics")
 
         if self.discrete_gripper:
-            # Discrete gripper actions are in [0, 1, 2]
-            # 0: open, 1: close, 2: stay
-            # We need to shift them to [-1, 0, 1] and then scale them to clip_max
-            gripper_vel = (gripper_vel - 1) * self.clip_max
+            # Map discrete command {0=close, 1=stay, 2=open} -> signed velocity.
+            # Negation accounts for SO100 sign (joint position increases on close).
+            #   0 -> +clip_max (close), 1 -> 0 (stay), 2 -> -clip_max (open)
+            gripper_vel = -(gripper_vel - 1) * self.clip_max
 
         # Compute desired gripper position
         delta = gripper_vel * float(self.speed_factor)

src/lerobot/scripts/lerobot_edit_dataset.py

@@ -150,11 +150,24 @@ Show dataset information without feature details:
         --operation.type info \
         --operation.show_features false
 
-Recompute dataset statistics:
+Recompute dataset statistics (saves to lerobot/pusht_recomputed_stats by default):
     lerobot-edit-dataset \
         --repo_id lerobot/pusht \
         --operation.type recompute_stats
 
+Recompute stats and save to a specific new repo_id:
+    lerobot-edit-dataset \
+        --repo_id lerobot/pusht \
+        --new_repo_id lerobot/pusht_new_stats \
+        --operation.type recompute_stats
+
+Recompute stats in-place (overwrites original dataset stats):
+    lerobot-edit-dataset \
+        --repo_id lerobot/pusht \
+        --new_repo_id lerobot/pusht \
+        --operation.type recompute_stats \
+        --operation.overwrite true
+
 Recompute stats for relative actions and push to hub:
     lerobot-edit-dataset \
         --repo_id lerobot/pusht \
@@ -256,6 +269,7 @@ class RecomputeStatsConfig(OperationConfig):
     relative_exclude_joints: list[str] | None = None
     chunk_size: int = 50
     num_workers: int = 0
+    overwrite: bool = False
 
 
 @OperationConfig.register_subclass("info")
@@ -280,16 +294,30 @@ class EditDatasetConfig:
     push_to_hub: bool = False
 
 
+def _resolve_io_paths(
+    repo_id: str,
+    new_repo_id: str | None,
+    root: Path | str | None,
+    new_root: Path | str | None,
+    default_new_repo_id: str | None = None,
+) -> tuple[str, Path, Path]:
+    """Resolve input/output paths and repo_id for dataset operations.
+
+    Returns (output_repo_id, input_path, output_path) with resolved (symlink-safe) paths.
+    """
+    input_path = (Path(root) if root else HF_LEROBOT_HOME / repo_id).resolve()
+    output_repo_id = new_repo_id or default_new_repo_id or repo_id
+    output_path = (Path(new_root) if new_root else HF_LEROBOT_HOME / output_repo_id).resolve()
+    return output_repo_id, input_path, output_path
+
+
 def get_output_path(
     repo_id: str,
     new_repo_id: str | None,
     root: Path | str | None,
     new_root: Path | str | None,
 ) -> tuple[str, Path]:
-    input_path = Path(root) if root else HF_LEROBOT_HOME / repo_id
-
-    output_repo_id = new_repo_id if new_repo_id else repo_id
-    output_path = Path(new_root) if new_root else HF_LEROBOT_HOME / output_repo_id
+    output_repo_id, input_path, output_path = _resolve_io_paths(repo_id, new_repo_id, root, new_root)
 
     # In case of in-place modification, create a backup of the original dataset (if it exists)
     if output_path == input_path:
@@ -557,7 +585,39 @@ def handle_recompute_stats(cfg: EditDatasetConfig) -> None:
     if not isinstance(cfg.operation, RecomputeStatsConfig):
         raise ValueError("Operation config must be RecomputeStatsConfig")
 
-    dataset = LeRobotDataset(cfg.repo_id, root=cfg.root)
+    # Determine whether this is an in-place operation
+    output_repo_id, input_root, output_root = _resolve_io_paths(
+        cfg.repo_id,
+        cfg.new_repo_id,
+        cfg.root,
+        cfg.new_root,
+        default_new_repo_id=f"{cfg.repo_id}_recomputed_stats",
+    )
+    in_place = output_root == input_root
+
+    if in_place and not cfg.operation.overwrite:
+        raise ValueError(
+            f"recompute_stats would overwrite the dataset in-place at {input_root}. "
+            "Pass --operation.overwrite true to allow in-place modification, "
+            "or use --new_repo_id / --new_root to write to a different location. "
+            f"Default output repo_id when neither is set: '{cfg.repo_id}_recomputed_stats'."
+        )
+
+    if in_place:
+        logging.warning(
+            f"Overwriting dataset stats in-place at {input_root}. The original stats will be lost."
+        )
+        dataset = LeRobotDataset(cfg.repo_id, root=input_root)
+    else:
+        logging.info(f"Copying dataset from {input_root} to {output_root}")
+        if output_root.exists():
+            backup_path = output_root.with_name(output_root.name + "_old")
+            logging.warning(f"Output directory {output_root} already exists. Moving to {backup_path}")
+            if backup_path.exists():
+                shutil.rmtree(backup_path)
+            shutil.move(output_root, backup_path)
+        shutil.copytree(input_root, output_root)
+        dataset = LeRobotDataset(output_repo_id, root=output_root)
 
     logging.info(f"Recomputing stats for {cfg.repo_id}")
     if cfg.operation.relative_action:
@@ -578,7 +638,7 @@ def handle_recompute_stats(cfg: EditDatasetConfig) -> None:
     logging.info(f"Stats written to {dataset.root}")
 
     if cfg.push_to_hub:
-        logging.info(f"Pushing to hub as {dataset.meta.repo_id}...")
+        logging.info(f"Pushing to hub as {dataset.repo_id}...")
         dataset.push_to_hub()
 
 

src/lerobot/scripts/lerobot_train.py

@@ -49,7 +49,6 @@ from lerobot.optim.factory import make_optimizer_and_scheduler
 from lerobot.policies import PreTrainedPolicy, make_policy, make_pre_post_processors
 from lerobot.utils.import_utils import register_third_party_plugins
 from lerobot.utils.logging_utils import AverageMeter, MetricsTracker
-from lerobot.utils.model_profiling import TrainingProfiler
 from lerobot.utils.random_utils import set_seed
 from lerobot.utils.utils import (
     cycle,
@@ -72,7 +71,6 @@ def update_policy(
     lr_scheduler=None,
     lock=None,
     rabc_weights_provider=None,
-    profiler: "TrainingProfiler | None" = None,
 ) -> tuple[MetricsTracker, dict]:
     """
     Performs a single training step to update the policy's weights.
@@ -105,10 +103,8 @@ def update_policy(
     if rabc_weights_provider is not None:
         rabc_batch_weights, rabc_batch_stats = rabc_weights_provider.compute_batch_weights(batch)
 
-    def _section(name: str) -> Any:
-        return profiler.section(name) if profiler is not None else nullcontext()
-
-    with _section("forward"), accelerator.autocast():
+    # Let accelerator handle mixed precision
+    with accelerator.autocast():
         # Use per-sample loss when RA-BC is enabled for proper weighting
         if rabc_batch_weights is not None:
             # Get per-sample losses
@@ -127,8 +123,8 @@ def update_policy(
 
         # TODO(rcadene): policy.unnormalize_outputs(out_dict)
 
-    with _section("backward"):
-        accelerator.backward(loss)
+    # Use accelerator's backward method
+    accelerator.backward(loss)
 
     # Clip gradients if specified
     if grad_clip_norm > 0:
@@ -138,7 +134,8 @@ def update_policy(
             policy.parameters(), float("inf"), error_if_nonfinite=False
         )
 
-    with _section("optimizer"), lock if lock is not None else nullcontext():
+    # Optimizer step
+    with lock if lock is not None else nullcontext():
         optimizer.step()
 
     optimizer.zero_grad()
@@ -319,15 +316,6 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
         logging.info("Creating optimizer and scheduler")
     optimizer, lr_scheduler = make_optimizer_and_scheduler(cfg, policy)
 
-    profiler = (
-        TrainingProfiler.from_cfg(cfg, device) if cfg.profile_mode != "off" and is_main_process else None
-    )
-    if profiler:
-        profiler.record_deterministic_forward(
-            policy=policy, dataset=dataset, batch_size=cfg.batch_size, preprocessor=preprocessor
-        )
-        profiler.start()
-
     # Load precomputed SARM progress for RA-BC if enabled
     # Generate progress using: src/lerobot/policies/sarm/compute_rabc_weights.py
     rabc_weights = None
@@ -461,7 +449,6 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
             accelerator=accelerator,
             lr_scheduler=lr_scheduler,
             rabc_weights_provider=rabc_weights,
-            profiler=profiler,
         )
 
         # Note: eval and checkpoint happens *after* the `step`th training update has completed, so we
@@ -469,8 +456,6 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
         step += 1
         if is_main_process:
             progbar.update(1)
-        if profiler:
-            profiler.step(step, train_tracker)
         train_tracker.step()
         is_log_step = cfg.log_freq > 0 and step % cfg.log_freq == 0 and is_main_process
         is_saving_step = step % cfg.save_freq == 0 or step == cfg.steps
@@ -566,8 +551,6 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
 
     if is_main_process:
         progbar.close()
-        if profiler:
-            profiler.finalize()
 
     if eval_env:
         close_envs(eval_env)

src/lerobot/utils/import_utils.py

@@ -115,7 +115,9 @@ _feetech_sdk_available = is_package_available("feetech-servo-sdk", import_name="
 _reachy2_sdk_available = is_package_available("reachy2_sdk")
 _can_available = is_package_available("python-can", "can")
 _unitree_sdk_available = is_package_available("unitree-sdk2py", "unitree_sdk2py")
-_pyrealsense2_available = is_package_available("pyrealsense2")
+_pyrealsense2_available = is_package_available("pyrealsense2") or is_package_available(
+    "pyrealsense2-macosx", import_name="pyrealsense2"
+)
 _zmq_available = is_package_available("pyzmq", import_name="zmq")
 _hebi_available = is_package_available("hebi-py", import_name="hebi")
 _teleop_available = is_package_available("teleop")

src/lerobot/utils/model_profiling.py

@@ -1,783 +0,0 @@
-#!/usr/bin/env python
-
-# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-
-"""Model profiling — single-file entry point.
-
-Contains three things that used to live in three separate files:
-
-* `TrainingProfiler` — hooks the training loop. Captures per-step
-  forward/backward/optimizer timings, the torch profiler output, and a
-  deterministic-forward fingerprint for regression detection.
-* `POLICY_SPECS` — CI matrix of `policy_name → (steps, train_args)`.
-  Inline so there is no separate JSON to keep in sync.
-* `main()` — CI orchestrator. For each selected policy, spawns a
-  `lerobot-train` subprocess with profiling enabled, collects the
-  artifacts, and (optionally) publishes a row to a HF Hub dataset.
-
-Usage (CI):
-
-    python -m lerobot.utils.model_profiling \
-        --output_dir=./profiling-results \
-        --policies act diffusion \
-        --profile_mode=trace \
-        --publish
-"""
-
-from __future__ import annotations
-
-import argparse
-import hashlib
-import json
-import logging
-import re
-import shutil
-import statistics
-import subprocess
-import time
-from collections.abc import Iterator
-from contextlib import contextmanager
-from dataclasses import dataclass
-from datetime import UTC, datetime
-from numbers import Real
-from pathlib import Path
-from typing import Any
-
-import torch
-from huggingface_hub import CommitOperationAdd, HfApi
-from huggingface_hub.errors import HfHubHTTPError
-from torch.utils.data import default_collate
-
-logger = logging.getLogger(__name__)
-
-
-# ---------------------------------------------------------------------------
-# Policy matrix. Same shape as the former JSON file; inlined so the source
-# tree has one less file to keep in sync with the training args.
-# ---------------------------------------------------------------------------
-
-_LIBERO_RENAME_BASE_RGB = (
-    '--rename_map={"observation.images.front": "observation.images.base_0_rgb", '
-    '"observation.images.wrist": "observation.images.left_wrist_0_rgb"}'
-)
-_LIBERO_RENAME_CAMERAS = (
-    '--rename_map={"observation.images.front": "observation.images.camera1", '
-    '"observation.images.wrist": "observation.images.camera2"}'
-)
-_PI_SGD = [
-    "--use_policy_training_preset=false",
-    "--optimizer.type=sgd",
-    "--optimizer.lr=1e-5",
-    "--optimizer.weight_decay=0",
-    "--optimizer.grad_clip_norm=1.0",
-    "--scheduler.type=cosine_decay_with_warmup",
-    "--scheduler.peak_lr=1e-5",
-    "--scheduler.decay_lr=1e-6",
-    "--scheduler.num_warmup_steps=0",
-    "--scheduler.num_decay_steps=12",
-]
-
-
-POLICY_SPECS: dict[str, dict[str, Any]] = {
-    "act": {
-        "steps": 12,
-        "train_args": [
-            "--dataset.repo_id=lerobot/pusht",
-            "--dataset.episodes=[0]",
-            "--policy.type=act",
-            "--policy.device=cuda",
-            "--batch_size=4",
-            "--cudnn_deterministic=true",
-        ],
-    },
-    "diffusion": {
-        "steps": 12,
-        "train_args": [
-            "--dataset.repo_id=lerobot/pusht",
-            "--dataset.episodes=[0]",
-            "--policy.type=diffusion",
-            "--policy.device=cuda",
-            "--batch_size=4",
-            "--cudnn_deterministic=true",
-        ],
-    },
-    "groot": {
-        "steps": 12,
-        "train_args": [
-            "--dataset.repo_id=lerobot/libero_plus",
-            "--dataset.episodes=[0]",
-            "--policy.type=groot",
-            "--policy.base_model_path=nvidia/GR00T-N1.5-3B",
-            "--policy.tune_diffusion_model=true",
-            "--policy.tune_projector=true",
-            "--policy.tune_llm=false",
-            "--policy.tune_visual=false",
-            "--policy.use_bf16=true",
-            "--policy.device=cuda",
-            "--batch_size=1",
-            '--rename_map={"observation.images.image": "observation.images.camera1", '
-            '"observation.images.image2": "observation.images.camera2"}',
-        ],
-    },
-    "multi_task_dit": {
-        "steps": 12,
-        "train_args": [
-            "--dataset.repo_id=lerobot/pusht",
-            "--dataset.episodes=[0]",
-            "--policy.type=multi_task_dit",
-            "--policy.device=cuda",
-            "--policy.horizon=32",
-            "--policy.n_action_steps=30",
-            "--batch_size=4",
-            "--cudnn_deterministic=true",
-        ],
-    },
-    "pi0": {
-        "steps": 12,
-        "train_args": [
-            "--dataset.repo_id=lerobot/libero_plus",
-            "--dataset.episodes=[0]",
-            "--policy.path=lerobot/pi0_base",
-            "--policy.device=cuda",
-            "--policy.dtype=bfloat16",
-            "--policy.n_action_steps=30",
-            "--policy.use_amp=true",
-            "--policy.gradient_checkpointing=true",
-            "--batch_size=1",
-            *_PI_SGD,
-            _LIBERO_RENAME_BASE_RGB,
-        ],
-    },
-    "pi0_fast": {
-        "steps": 12,
-        "train_args": [
-            "--dataset.repo_id=lerobot/libero_plus",
-            "--dataset.episodes=[0]",
-            "--policy.path=lerobot/pi0fast-base",
-            "--policy.device=cuda",
-            "--policy.dtype=bfloat16",
-            "--policy.n_action_steps=30",
-            "--policy.use_amp=true",
-            "--policy.gradient_checkpointing=true",
-            "--batch_size=1",
-            *_PI_SGD,
-            _LIBERO_RENAME_BASE_RGB,
-        ],
-    },
-    "pi05": {
-        "steps": 12,
-        "train_args": [
-            "--dataset.repo_id=lerobot/libero_plus",
-            "--dataset.episodes=[0]",
-            "--policy.path=lerobot/pi05_base",
-            "--policy.device=cuda",
-            "--policy.dtype=bfloat16",
-            "--policy.n_action_steps=30",
-            "--policy.use_amp=true",
-            "--policy.gradient_checkpointing=true",
-            "--batch_size=1",
-            *_PI_SGD,
-            '--policy.normalization_mapping={"ACTION": "MEAN_STD", '
-            '"STATE": "MEAN_STD", "VISUAL": "IDENTITY"}',
-            _LIBERO_RENAME_BASE_RGB,
-        ],
-    },
-    "smolvla": {
-        "steps": 12,
-        "train_args": [
-            "--dataset.repo_id=lerobot/libero_plus",
-            "--dataset.episodes=[0]",
-            "--policy.path=lerobot/smolvla_base",
-            "--policy.load_vlm_weights=true",
-            "--policy.freeze_vision_encoder=false",
-            "--policy.train_expert_only=false",
-            "--policy.empty_cameras=1",
-            "--policy.device=cuda",
-            "--batch_size=1",
-            _LIBERO_RENAME_CAMERAS,
-        ],
-    },
-    "wall_x": {
-        "steps": 12,
-        "train_args": [
-            "--dataset.repo_id=lerobot/aloha_sim_insertion_human",
-            "--dataset.episodes=[0]",
-            "--policy.type=wall_x",
-            "--policy.pretrained_name_or_path=x-square-robot/wall-oss-flow",
-            "--policy.prediction_mode=diffusion",
-            "--policy.attn_implementation=eager",
-            "--policy.device=cuda",
-            "--batch_size=1",
-            *_PI_SGD,
-        ],
-    },
-    "xvla": {
-        "steps": 12,
-        "train_args": [
-            "--dataset.repo_id=lerobot/libero_plus",
-            "--dataset.episodes=[0]",
-            "--policy.path=lerobot/xvla-widowx",
-            "--policy.action_mode=auto",
-            "--policy.empty_cameras=1",
-            "--policy.device=cuda",
-            "--batch_size=1",
-            '--rename_map={"observation.images.front": "observation.images.image", '
-            '"observation.images.wrist": "observation.images.image2"}',
-        ],
-    },
-}
-
-
-# ---------------------------------------------------------------------------
-# TrainingProfiler — hooks the training loop.
-# ---------------------------------------------------------------------------
-
-
-def _stable_float(value: float | int | None) -> float | None:
-    return None if value is None else round(float(value), 8)
-
-
-def _as_float(value: Any) -> float:
-    if isinstance(value, Real):
-        return float(value)
-    if hasattr(value, "val"):
-        return float(value.val)
-    raise TypeError(f"Expected a real-valued metric, got {type(value).__name__}")
-
-
-def _summary(values: list[float]) -> dict[str, float | int | None]:
-    if not values:
-        return {"count": 0, "mean": None, "median": None, "min": None, "max": None}
-    return {
-        "count": len(values),
-        "mean": statistics.fmean(values),
-        "median": statistics.median(values),
-        "min": min(values),
-        "max": max(values),
-    }
-
-
-def _tensor_signature(tensor: torch.Tensor) -> dict[str, Any]:
-    """Small, stable summary of a tensor so forward-pass outputs can be
-    compared across runs without bloating the regression JSON."""
-    cpu = tensor.detach().cpu()
-    hash_tensor = cpu.float() if cpu.dtype == torch.bfloat16 else cpu
-    sig: dict[str, Any] = {
-        "shape": list(cpu.shape),
-        "dtype": str(cpu.dtype),
-        "numel": cpu.numel(),
-        "sha256": hashlib.sha256(hash_tensor.contiguous().numpy().tobytes()).hexdigest(),
-    }
-    if cpu.numel():
-        promoted = cpu.to(torch.float64) if cpu.is_floating_point() else cpu.to(torch.int64)
-        sig["sum"] = _stable_float(promoted.sum().item())
-        sig["mean"] = _stable_float(promoted.float().mean().item())
-    return sig
-
-
-def _summarize_value(value: Any) -> Any:
-    if isinstance(value, torch.Tensor):
-        return _tensor_signature(value)
-    if isinstance(value, dict):
-        return {k: _summarize_value(v) for k, v in value.items()}
-    if isinstance(value, (list, tuple)):
-        return [_summarize_value(v) for v in value]
-    if isinstance(value, (str, int, float, bool)) or value is None:
-        return value
-    return repr(value)
-
-
-def _hash_payload(payload: Any) -> str:
-    return hashlib.sha256(json.dumps(payload, sort_keys=True).encode()).hexdigest()
-
-
-def _get_profiler_device_time_us(event: Any) -> float | None:
-    return _stable_float(
-        getattr(event, "self_device_time_total", getattr(event, "self_cuda_time_total", None))
-    )
-
-
-def _write_profiler_table(profiler: Any, path: Path, *, sort_by: str, row_limit: int = 40) -> None:
-    try:
-        path.write_text(profiler.key_averages().table(sort_by=sort_by, row_limit=row_limit))
-    except Exception:
-        logger.debug("Could not write profiler table for sort_by=%s", sort_by, exc_info=True)
-
-
-def write_deterministic_forward_artifacts(
-    *,
-    policy: Any,
-    dataset: Any,
-    batch_size: int,
-    preprocessor: Any,
-    output_dir: Path,
-    device_type: str,
-) -> None:
-    """Run a seed-controlled single forward pass and dump a stable fingerprint
-    (loss/output tensor hashes + op counts) for regression detection. Keeps
-    the caller-selected module mode so ACT-with-VAE-style policies that only
-    materialize their full forward outputs in `train()` still match. Models
-    with stochastic train-mode layers still rely on the seeded RNG for stable
-    fingerprints."""
-    if len(dataset) == 0:
-        raise ValueError("Cannot build a reference batch from an empty dataset.")
-    indices = [i % len(dataset) for i in range(batch_size)]
-    reference_batch = default_collate([dataset[i] for i in indices])
-    # Mirror the uint8 → float32/255 conversion the train loop applies after
-    # the dataloader (PR #3406). The dataset ships camera frames as uint8 for
-    # faster transport, but policies like SmolVLA/xVLA run bilinear
-    # interpolation on images which doesn't support Byte tensors.
-    camera_keys = tuple(getattr(getattr(dataset, "meta", None), "camera_keys", ()) or ())
-    if not camera_keys:
-        camera_keys = tuple(
-            key
-            for key, value in reference_batch.items()
-            if key.startswith("observation.images.") and isinstance(value, torch.Tensor)
-        )
-    for cam_key in camera_keys:
-        if cam_key in reference_batch and reference_batch[cam_key].dtype == torch.uint8:
-            reference_batch[cam_key] = reference_batch[cam_key].to(dtype=torch.float32) / 255.0
-    reference_batch = preprocessor(reference_batch)
-
-    activities = [torch.profiler.ProfilerActivity.CPU]
-    if device_type == "cuda":
-        activities.append(torch.profiler.ProfilerActivity.CUDA)
-
-    with torch.random.fork_rng(devices=[] if device_type != "cuda" else None):
-        torch.manual_seed(0)
-        if device_type == "cuda":
-            torch.cuda.manual_seed_all(0)
-        with torch.no_grad(), torch.profiler.profile(activities=activities) as prof:
-            loss, output_dict = policy.forward(reference_batch)
-
-    operators = sorted(
-        (
-            {
-                "key": e.key,
-                "count": e.count,
-                "cpu_time_total_us": _stable_float(getattr(e, "cpu_time_total", None)),
-                **(
-                    {"self_cuda_time_total_us": _get_profiler_device_time_us(e)}
-                    if device_type == "cuda"
-                    else {}
-                ),
-            }
-            for e in prof.key_averages()
-        ),
-        key=lambda e: e["key"],
-    )
-    outputs = {"loss": _summarize_value(loss), "output_dict": _summarize_value(output_dict)}
-    payload = {
-        "seed": 0,
-        "reference_batch_size": batch_size,
-        "operator_fingerprint": _hash_payload([(o["key"], o["count"]) for o in operators]),
-        "output_fingerprint": _hash_payload(outputs),
-        "operators": operators,
-        "outputs": outputs,
-    }
-    output_dir.mkdir(parents=True, exist_ok=True)
-    (output_dir / "deterministic_forward.json").write_text(json.dumps(payload, indent=2, sort_keys=True))
-    sort_by = "self_cuda_time_total" if device_type == "cuda" else "cpu_time_total"
-    _write_profiler_table(prof, output_dir / "deterministic_forward_ops.txt", sort_by=sort_by)
-
-
-class TrainingProfiler:
-    """Self-contained profiling hooks for the training loop.
-
-    The training script interacts via ``start()``, ``section()``, ``step()``,
-    ``finalize()``, and (optionally) ``record_deterministic_forward()`` — a
-    ~7-line surface.
-    """
-
-    _SCHEDULE_WAIT = 1
-    _SCHEDULE_WARMUP = 2
-    _SCHEDULE_ACTIVE = 6
-
-    def __init__(self, mode: str, output_dir: Path, device: torch.device) -> None:
-        self._mode = mode
-        self._output_dir = output_dir
-        self._output_dir.mkdir(parents=True, exist_ok=True)
-        self._device = device
-        # Inline timing state — no separate collector class.
-        self._total_update_s: list[float] = []
-        self._dataloading_s: list[float] = []
-        self._section_s: dict[str, list[float]] = {}
-        self._memory: list[dict[str, int]] = []
-        self._torch = self._build_torch_profiler()
-        logger.info("Profiling enabled. Artifacts will be written to %s", output_dir)
-
-    def _build_torch_profiler(self) -> Any:
-        activities = [torch.profiler.ProfilerActivity.CPU]
-        if self._device.type == "cuda":
-            activities.append(torch.profiler.ProfilerActivity.CUDA)
-        trace_dir = self._output_dir / "torch_traces"
-        trace_dir.mkdir(parents=True, exist_ok=True)
-
-        def _on_trace_ready(p: Any) -> None:
-            if self._mode == "trace":
-                p.export_chrome_trace(str(trace_dir / f"trace_step_{p.step_num}.json"))
-
-        return torch.profiler.profile(
-            activities=activities,
-            schedule=torch.profiler.schedule(
-                wait=self._SCHEDULE_WAIT,
-                warmup=self._SCHEDULE_WARMUP,
-                active=self._SCHEDULE_ACTIVE,
-                repeat=1,
-            ),
-            on_trace_ready=_on_trace_ready,
-            record_shapes=True,
-            profile_memory=True,
-            with_flops=True,
-        )
-
-    @classmethod
-    def from_cfg(cls, cfg: Any, device: torch.device) -> TrainingProfiler:
-        output = cfg.profile_output_dir or (Path(cfg.output_dir) / "profiling")
-        return cls(mode=cfg.profile_mode, output_dir=Path(output), device=device)
-
-    def record_deterministic_forward(
-        self, *, policy: Any, dataset: Any, batch_size: int, preprocessor: Any
-    ) -> None:
-        logger.info("Recording deterministic forward-pass artifacts")
-        write_deterministic_forward_artifacts(
-            policy=policy,
-            dataset=dataset,
-            batch_size=batch_size,
-            preprocessor=preprocessor,
-            output_dir=self._output_dir,
-            device_type=self._device.type,
-        )
-        if self._device.type == "cuda":
-            torch.cuda.empty_cache()
-
-    def start(self) -> None:
-        if self._device.type == "cuda":
-            torch.cuda.reset_peak_memory_stats(self._device)
-        self._torch.__enter__()
-
-    @contextmanager
-    def section(self, name: str) -> Iterator[None]:
-        """Time a region of the training step. Syncs on CUDA so the
-        duration reflects GPU work, not just kernel-launch latency."""
-        if self._device.type == "cuda":
-            torch.cuda.synchronize(self._device)
-        t0 = time.perf_counter()
-        try:
-            yield
-        finally:
-            if self._device.type == "cuda":
-                torch.cuda.synchronize(self._device)
-            self._section_s.setdefault(name, []).append(time.perf_counter() - t0)
-
-    def step(self, step_num: int, train_tracker: Any) -> None:
-        self._total_update_s.append(_as_float(train_tracker.update_s))
-        self._dataloading_s.append(_as_float(train_tracker.dataloading_s))
-        if self._device.type == "cuda":
-            self._memory.append(
-                {
-                    "step": step_num,
-                    "allocated_bytes": torch.cuda.memory_allocated(self._device),
-                    "reserved_bytes": torch.cuda.memory_reserved(self._device),
-                }
-            )
-        self._torch.step()
-
-    def finalize(self) -> None:
-        self._torch.__exit__(None, None, None)
-        payload: dict[str, Any] = {
-            "profile_mode": self._mode,
-            "total_update_s": _summary(self._total_update_s),
-            "dataloading_s": _summary(self._dataloading_s),
-            "memory_timeline": self._memory,
-        }
-        for name, values in self._section_s.items():
-            payload[f"{name}_s"] = _summary(values)
-        if self._device.type == "cuda":
-            payload["peak_memory_allocated_bytes"] = torch.cuda.max_memory_allocated(self._device)
-            payload["peak_memory_reserved_bytes"] = torch.cuda.max_memory_reserved(self._device)
-        (self._output_dir / "step_timing_summary.json").write_text(
-            json.dumps(payload, indent=2, sort_keys=True)
-        )
-
-        tables_dir = self._output_dir / "torch_tables"
-        tables_dir.mkdir(parents=True, exist_ok=True)
-        _write_profiler_table(self._torch, tables_dir / "cpu_time_total.txt", sort_by="cpu_time_total")
-        _write_profiler_table(self._torch, tables_dir / "cpu_memory.txt", sort_by="self_cpu_memory_usage")
-        _write_profiler_table(self._torch, tables_dir / "flops.txt", sort_by="flops")
-        if self._device.type == "cuda":
-            _write_profiler_table(
-                self._torch, tables_dir / "cuda_time_total.txt", sort_by="self_cuda_time_total"
-            )
-            _write_profiler_table(
-                self._torch, tables_dir / "cuda_memory.txt", sort_by="self_cuda_memory_usage"
-            )
-
-
-# ---------------------------------------------------------------------------
-# CI orchestrator. Spawns `lerobot-train` per policy, collects the
-# artifacts, (optionally) uploads to the HF Hub results dataset.
-# ---------------------------------------------------------------------------
-
-
-@dataclass(frozen=True)
-class UploadTarget:
-    local_path: Path
-    path_in_repo: str
-
-
-@dataclass(frozen=True)
-class UploadResult:
-    uploaded_paths: dict[str, str]
-    pr_url: str | None = None
-
-
-def _utc_timestamp_slug(now: datetime | None = None) -> str:
-    return (now or datetime.now(UTC)).strftime("%Y%m%dT%H%M%SZ")
-
-
-def _hub_file_url(repo_id: str, path_in_repo: str, *, revision: str = "main") -> str:
-    return f"https://huggingface.co/datasets/{repo_id}/resolve/{revision}/{path_in_repo}"
-
-
-def parse_discussion_num(pr_url: str | None) -> int | None:
-    if not pr_url:
-        return None
-    m = re.search(r"/discussions/(\d+)$", pr_url)
-    return int(m.group(1)) if m else None
-
-
-def upload_targets(
-    repo_id: str,
-    targets: list[UploadTarget],
-    *,
-    token: str | None = None,
-    commit_message: str | None = None,
-    create_pr: bool = False,
-) -> UploadResult:
-    api = HfApi(token=token)
-    commit = api.create_commit(
-        repo_id=repo_id,
-        repo_type="dataset",
-        operations=[
-            CommitOperationAdd(path_in_repo=t.path_in_repo, path_or_fileobj=str(t.local_path))
-            for t in targets
-        ],
-        commit_message=commit_message or f"Upload {len(targets)} profiling artifacts",
-        revision="main",
-        create_pr=create_pr,
-    )
-    pr_num = parse_discussion_num(commit.pr_url)
-    revision = f"refs/pr/{pr_num}" if (create_pr and pr_num) else "main"
-    return UploadResult(
-        uploaded_paths={
-            t.path_in_repo: _hub_file_url(repo_id, t.path_in_repo, revision=revision) for t in targets
-        },
-        pr_url=commit.pr_url,
-    )
-
-
-def build_train_command(policy: str, run_dir: Path, profile_mode: str) -> list[str]:
-    spec = POLICY_SPECS[policy]
-    return [
-        "uv",
-        "run",
-        "lerobot-train",
-        *spec["train_args"],
-        f"--output_dir={run_dir / 'train'}",
-        f"--steps={spec['steps']}",
-        "--eval_freq=0",
-        "--save_checkpoint=false",
-        f"--save_freq={spec['steps']}",
-        "--wandb.enable=false",
-        "--policy.push_to_hub=false",
-        "--num_workers=0",
-        "--log_freq=1",
-        f"--profile_mode={profile_mode}",
-        f"--profile_output_dir={run_dir / 'profiling'}",
-    ]
-
-
-def build_artifact_index(
-    *, repo_id: str, run_dir: Path, policy_name: str, run_id: str
-) -> tuple[dict[str, Any], dict[str, Any], list[UploadTarget], str]:
-    """Scan the run directory and categorize files into
-    (stdout/stderr, torch_tables/*, torch_traces/*, everything else under profiling/).
-    Returns (paths, urls, upload targets, row path in repo)."""
-    row_path_in_repo = f"rows/{policy_name}/{run_id}.json"
-    root = f"artifacts/{policy_name}/{run_id}"
-    paths: dict[str, Any] = {
-        "row": row_path_in_repo,
-        "profiling_files": {},
-        "torch_tables": {},
-        "trace_files": {},
-    }
-    urls: dict[str, Any] = {
-        "row": _hub_file_url(repo_id, row_path_in_repo),
-        "profiling_files": {},
-        "torch_tables": {},
-        "trace_files": {},
-    }
-    targets: list[UploadTarget] = []
-
-    for name in ("stdout.txt", "stderr.txt"):
-        p = run_dir / name
-        if p.exists():
-            key = name.removesuffix(".txt")
-            repo = f"{root}/{name}"
-            paths[key] = repo
-            urls[key] = _hub_file_url(repo_id, repo)
-            targets.append(UploadTarget(p, repo))
-
-    profiling_dir = run_dir / "profiling"
-    if profiling_dir.exists():
-        for p in sorted(profiling_dir.rglob("*")):
-            if not p.is_file():
-                continue
-            rel = str(p.relative_to(run_dir))
-            repo = f"{root}/{rel}"
-            paths["profiling_files"][rel] = repo
-            urls["profiling_files"][rel] = _hub_file_url(repo_id, repo)
-            targets.append(UploadTarget(p, repo))
-            if p.name == "step_timing_summary.json":
-                paths["step_timing_summary"] = repo
-                urls["step_timing_summary"] = _hub_file_url(repo_id, repo)
-            elif "torch_tables" in p.parts:
-                paths["torch_tables"][p.name] = repo
-                urls["torch_tables"][p.name] = _hub_file_url(repo_id, repo)
-            elif "torch_traces" in p.parts:
-                paths["trace_files"][p.name] = repo
-                urls["trace_files"][p.name] = _hub_file_url(repo_id, repo)
-
-    return paths, urls, targets, row_path_in_repo
-
-
-def upload_profile_run(
-    *,
-    repo_id: str,
-    row_path: Path,
-    row_path_in_repo: str,
-    artifact_targets: list[UploadTarget],
-    create_pr: bool = False,
-) -> UploadResult:
-    return upload_targets(
-        repo_id=repo_id,
-        targets=[*artifact_targets, UploadTarget(row_path, row_path_in_repo)],
-        commit_message=f"Add model profiling row {row_path_in_repo}",
-        create_pr=create_pr,
-    )
-
-
-def _load_json(path: Path) -> dict[str, Any]:
-    return json.loads(path.read_text()) if path.exists() else {}
-
-
-def parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser(description=__doc__)
-    parser.add_argument("--policies", nargs="*", default=None)
-    parser.add_argument("--output_dir", type=Path, required=True)
-    parser.add_argument("--hub_org", default="lerobot")
-    parser.add_argument("--results_repo", default="model-profiling-history")
-    parser.add_argument("--publish", action="store_true")
-    parser.add_argument("--profile_mode", choices=["summary", "trace"], default="trace")
-    parser.add_argument("--git_commit", default="")
-    parser.add_argument("--git_ref", default="")
-    parser.add_argument("--pr_number", default="")
-    return parser.parse_args()
-
-
-def main() -> int:
-    args = parse_args()
-    selected = args.policies or list(POLICY_SPECS)
-    unknown = sorted(set(selected) - set(POLICY_SPECS))
-    if unknown:
-        raise ValueError(f"Unknown profiling policies: {', '.join(unknown)}")
-
-    args.output_dir.mkdir(parents=True, exist_ok=True)
-    repo_id = args.results_repo if "/" in args.results_repo else f"{args.hub_org}/{args.results_repo}"
-    git_exe = shutil.which("git")
-    if not git_exe:
-        raise RuntimeError("git not found in PATH")
-    git_commit = args.git_commit or subprocess.check_output([git_exe, "rev-parse", "HEAD"], text=True).strip()
-    pr_number = int(args.pr_number) if str(args.pr_number).strip() else None
-    exit_code = 0
-
-    for policy in selected:
-        run_id = f"{_utc_timestamp_slug()}__{policy}"
-        run_dir = args.output_dir / policy / run_id
-        run_dir.mkdir(parents=True, exist_ok=True)
-        cmd = build_train_command(policy, run_dir, args.profile_mode)
-
-        t0 = time.perf_counter()
-        result = subprocess.run(cmd, capture_output=True, text=True)
-        wall_s = time.perf_counter() - t0
-
-        (run_dir / "stdout.txt").write_text(result.stdout)
-        (run_dir / "stderr.txt").write_text(result.stderr)
-        if result.returncode != 0:
-            exit_code = 1
-
-        paths, urls, upload_list, row_in_repo = build_artifact_index(
-            repo_id=repo_id, run_dir=run_dir, policy_name=policy, run_id=run_id
-        )
-        row: dict[str, Any] = {
-            "schema_version": 1,
-            "created_at": datetime.now(UTC).isoformat(),
-            "run_id": run_id,
-            "policy": policy,
-            "git_commit": git_commit,
-            "git_ref": args.git_ref or None,
-            "pr_number": pr_number,
-            "status": "success" if result.returncode == 0 else "failed",
-            "return_code": result.returncode,
-            "profile_mode": args.profile_mode,
-            "wall_time_s": wall_s,
-            "spec": {
-                "steps": POLICY_SPECS[policy]["steps"],
-                "train_args": POLICY_SPECS[policy]["train_args"],
-            },
-            "step_timing_summary": _load_json(run_dir / "profiling" / "step_timing_summary.json"),
-            "deterministic_forward": _load_json(run_dir / "profiling" / "deterministic_forward.json"),
-            "artifact_paths": paths,
-            "artifact_urls": urls,
-            "stderr_tail": result.stderr.splitlines()[-20:],
-        }
-
-        row_path = run_dir / "profiling_row.json"
-        row_path.write_text(json.dumps(row, indent=2, sort_keys=True))
-
-        if args.publish:
-            try:
-                uploaded = upload_profile_run(
-                    repo_id=repo_id,
-                    row_path=row_path,
-                    row_path_in_repo=row_in_repo,
-                    artifact_targets=upload_list,
-                    create_pr=pr_number is not None,
-                )
-            except HfHubHTTPError as exc:
-                row.update({"publish_status": "failed", "publish_error": str(exc)})
-            else:
-                row.update(
-                    {
-                        "publish_status": "success",
-                        "uploaded_paths": uploaded.uploaded_paths,
-                        "publish_pr_url": uploaded.pr_url,
-                        "publish_pr_number": parse_discussion_num(uploaded.pr_url),
-                    }
-                )
-            row_path.write_text(json.dumps(row, indent=2, sort_keys=True))
-
-        print(json.dumps(row, indent=2, sort_keys=True))
-
-    return exit_code
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())

tests/artifacts/policies/aloha_sim_insertion_human_act_1000_steps/actions.safetensors

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tests/artifacts/policies/aloha_sim_insertion_human_act_1000_steps/grad_stats.safetensors

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tests/artifacts/policies/aloha_sim_insertion_human_act_1000_steps/output_dict.safetensors

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tests/artifacts/policies/aloha_sim_insertion_human_act_1000_steps/param_stats.safetensors

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tests/test_model_profiling.py

@@ -1,348 +0,0 @@
-#!/usr/bin/env python
-
-# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-
-from __future__ import annotations
-
-import argparse
-import json
-import subprocess
-from pathlib import Path
-
-import pytest
-import torch
-from huggingface_hub.errors import HfHubHTTPError
-
-from lerobot.utils import model_profiling as mp
-
-# ---------------------------------------------------------------------------
-# Policy spec matrix
-# ---------------------------------------------------------------------------
-
-
-def test_policy_specs_cover_expected_policies():
-    assert set(mp.POLICY_SPECS) == {
-        "act",
-        "diffusion",
-        "groot",
-        "multi_task_dit",
-        "pi0",
-        "pi0_fast",
-        "pi05",
-        "smolvla",
-        "wall_x",
-        "xvla",
-    }
-    # Sanity: excluded policies should stay out of the matrix.
-    for excluded in ("sac", "sarm", "tdmpc", "vqbet", "reward_classifier"):
-        assert excluded not in mp.POLICY_SPECS
-
-
-def test_pretrained_libero_specs_match_expected_camera_keys_and_normalization():
-    base_rgb_rename = (
-        '--rename_map={"observation.images.front": "observation.images.base_0_rgb", '
-        '"observation.images.wrist": "observation.images.left_wrist_0_rgb"}'
-    )
-    for name in ("pi0", "pi0_fast", "pi05"):
-        assert base_rgb_rename in mp.POLICY_SPECS[name]["train_args"]
-    assert any(
-        arg.startswith('--policy.normalization_mapping={"ACTION": "MEAN_STD"')
-        for arg in mp.POLICY_SPECS["pi05"]["train_args"]
-    )
-    assert (
-        '--rename_map={"observation.images.front": "observation.images.camera1", '
-        '"observation.images.wrist": "observation.images.camera2"}'
-        in mp.POLICY_SPECS["smolvla"]["train_args"]
-    )
-
-
-# ---------------------------------------------------------------------------
-# CI orchestrator helpers
-# ---------------------------------------------------------------------------
-
-
-def test_build_train_command_includes_profiling_outputs(tmp_path):
-    cmd = mp.build_train_command("act", tmp_path / "run", "trace")
-    assert cmd[:3] == ["uv", "run", "lerobot-train"]
-    assert any(a.startswith("--output_dir=") for a in cmd)
-    assert any(a.startswith("--profile_output_dir=") for a in cmd)
-    assert "--profile_mode=trace" in cmd
-    assert "--eval_freq=0" in cmd
-
-
-def test_build_artifact_index_collects_tables_and_traces(tmp_path):
-    run_dir = tmp_path / "act" / "20260415T000000Z__act"
-    profiling = run_dir / "profiling"
-    (profiling / "torch_tables").mkdir(parents=True)
-    (profiling / "torch_traces").mkdir(parents=True)
-    (profiling / "step_timing_summary.json").write_text("{}")
-    (profiling / "deterministic_forward.json").write_text(
-        json.dumps({"operator_fingerprint": "ops", "output_fingerprint": "out"})
-    )
-    (profiling / "torch_tables" / "cpu_time_total.txt").write_text("cpu table")
-    (profiling / "torch_traces" / "trace_step_9.json").write_text("{}")
-    (run_dir / "stdout.txt").write_text("stdout")
-    (run_dir / "stderr.txt").write_text("stderr")
-
-    paths, urls, targets, row_in_repo = mp.build_artifact_index(
-        repo_id="lerobot/model-profiling-history",
-        run_dir=run_dir,
-        policy_name="act",
-        run_id="20260415T000000Z__act",
-    )
-
-    assert row_in_repo == "rows/act/20260415T000000Z__act.json"
-    assert paths["stdout"].endswith("/stdout.txt")
-    assert paths["step_timing_summary"].endswith("/profiling/step_timing_summary.json")
-    assert "cpu_time_total.txt" in paths["torch_tables"]
-    assert "trace_step_9.json" in paths["trace_files"]
-    assert urls["row"].startswith("https://huggingface.co/datasets/lerobot/model-profiling-history/")
-    # stdout + stderr + 4 profiling files
-    assert len(targets) == 6
-
-
-def test_upload_targets_batches_preview_publish_into_single_hf_pr(monkeypatch, tmp_path):
-    local_path = tmp_path / "profiling_row.json"
-    local_path.write_text("{}")
-    captured: dict[str, object] = {}
-
-    class _FakeCommit:
-        pr_url = "https://huggingface.co/datasets/lerobot/model-profiling-history/discussions/42"
-
-    class _FakeApi:
-        def __init__(self, token=None):
-            captured["token"] = token
-
-        def create_commit(self, **kwargs):
-            captured.update(kwargs)
-            return _FakeCommit()
-
-    monkeypatch.setattr(mp, "HfApi", _FakeApi)
-
-    result = mp.upload_targets(
-        repo_id="lerobot/model-profiling-history",
-        targets=[mp.UploadTarget(local_path, "rows/act/run.json")],
-        create_pr=True,
-        token="hf_test_token",
-    )
-
-    assert captured["repo_id"] == "lerobot/model-profiling-history"
-    assert captured["repo_type"] == "dataset"
-    assert captured["create_pr"] is True
-    assert result.pr_url == _FakeCommit.pr_url
-    assert result.uploaded_paths["rows/act/run.json"].endswith("/resolve/refs/pr/42/rows/act/run.json")
-
-
-def test_parse_discussion_num_handles_hf_discussion_urls():
-    assert (
-        mp.parse_discussion_num(
-            "https://huggingface.co/datasets/lerobot/model-profiling-history/discussions/42"
-        )
-        == 42
-    )
-    assert mp.parse_discussion_num("https://huggingface.co/datasets/lerobot/model-profiling-history") is None
-    assert mp.parse_discussion_num(None) is None
-
-
-# ---------------------------------------------------------------------------
-# main() smoke tests
-# ---------------------------------------------------------------------------
-
-
-@pytest.fixture
-def fake_args(tmp_path):
-    """Shared argparse namespace for main() smoke tests — overridden per-test."""
-    return argparse.Namespace(
-        policies=["act"],
-        output_dir=tmp_path / "results",
-        hub_org="lerobot",
-        results_repo="model-profiling-history",
-        publish=False,
-        profile_mode="summary",
-        git_commit="",
-        git_ref="codex/model-profiling",
-        pr_number="3389",
-    )
-
-
-def _stub_train_subprocess(mp_module, *, returncode: int = 0, write_artifacts: bool = True):
-    """Build a fake subprocess.run that writes the profiling artifacts main() expects."""
-
-    def _fake_run(cmd, capture_output, text):
-        assert capture_output is True
-        assert text is True
-        profile_dir = Path(next(a.split("=", 1)[1] for a in cmd if a.startswith("--profile_output_dir=")))
-        profile_dir.mkdir(parents=True, exist_ok=True)
-        if write_artifacts:
-            (profile_dir / "torch_tables").mkdir(parents=True, exist_ok=True)
-            (profile_dir / "step_timing_summary.json").write_text(
-                json.dumps({"total_update_s": {"count": 1, "mean": 0.3}, "peak_memory_allocated_bytes": 1024})
-            )
-            (profile_dir / "deterministic_forward.json").write_text(
-                json.dumps(
-                    {"operator_fingerprint": "ops-fingerprint", "output_fingerprint": "output-fingerprint"}
-                )
-            )
-            (profile_dir / "torch_tables" / "cpu_time_total.txt").write_text("cpu time table")
-        return subprocess.CompletedProcess(cmd, returncode, "stdout ok", "")
-
-    return _fake_run
-
-
-def test_main_smoke_writes_row(monkeypatch, fake_args):
-    monkeypatch.setattr(mp, "parse_args", lambda: fake_args)
-    monkeypatch.setattr(mp.subprocess, "check_output", lambda *a, **k: "deadbeef\n")
-    monkeypatch.setattr(mp.subprocess, "run", _stub_train_subprocess(mp))
-
-    assert mp.main() == 0
-
-    row_paths = list(fake_args.output_dir.rglob("profiling_row.json"))
-    assert len(row_paths) == 1
-    row = json.loads(row_paths[0].read_text())
-    assert row["policy"] == "act"
-    assert row["status"] == "success"
-    assert row["git_commit"] == "deadbeef"
-    assert row["git_ref"] == "codex/model-profiling"
-    assert row["pr_number"] == 3389
-    assert row["step_timing_summary"]["total_update_s"]["mean"] == 0.3
-    assert row["deterministic_forward"]["operator_fingerprint"] == "ops-fingerprint"
-
-
-def test_main_records_publish_failure_without_failing(monkeypatch, fake_args):
-    fake_args.publish = True
-    fake_args.git_commit = "deadbeef"
-    monkeypatch.setattr(mp, "parse_args", lambda: fake_args)
-    monkeypatch.setattr(mp.subprocess, "run", _stub_train_subprocess(mp, write_artifacts=False))
-
-    def _fail_upload(**kwargs):
-        resp = type("Resp", (), {"status_code": 403, "headers": {}, "request": None})()
-        raise HfHubHTTPError("403 Forbidden: Authorization error.", response=resp)
-
-    monkeypatch.setattr(mp, "upload_profile_run", _fail_upload)
-
-    assert mp.main() == 0
-    row = json.loads(next(fake_args.output_dir.rglob("profiling_row.json")).read_text())
-    assert row["status"] == "success"
-    assert row["publish_status"] == "failed"
-    assert "Authorization error" in row["publish_error"]
-
-
-def test_main_returns_nonzero_when_training_subprocess_fails(monkeypatch, fake_args):
-    monkeypatch.setattr(mp, "parse_args", lambda: fake_args)
-    monkeypatch.setattr(mp.subprocess, "check_output", lambda *a, **k: "deadbeef\n")
-    monkeypatch.setattr(mp.subprocess, "run", _stub_train_subprocess(mp, returncode=3))
-
-    assert mp.main() == 1
-
-    row = json.loads(next(fake_args.output_dir.rglob("profiling_row.json")).read_text())
-    assert row["status"] == "failed"
-    assert row["return_code"] == 3
-
-
-# ---------------------------------------------------------------------------
-# TrainingProfiler behavior
-# ---------------------------------------------------------------------------
-
-
-def test_deterministic_forward_artifacts_preserve_policy_mode(tmp_path):
-    class _TrainingOnlyPolicy(torch.nn.Module):
-        def __init__(self):
-            super().__init__()
-            self.forward_calls = 0
-
-        def forward(self, batch):
-            self.forward_calls += 1
-            assert self.training
-            return batch["value"].sum(), {"value": batch["value"]}
-
-    dataset = [{"value": torch.tensor([1.0, 2.0])}]
-    policy = _TrainingOnlyPolicy()
-    policy.train()
-
-    mp.write_deterministic_forward_artifacts(
-        policy=policy,
-        dataset=dataset,
-        batch_size=2,
-        preprocessor=lambda b: b,
-        output_dir=tmp_path,
-        device_type="cpu",
-    )
-
-    payload = json.loads((tmp_path / "deterministic_forward.json").read_text())
-    assert policy.training is True
-    assert policy.forward_calls == 1
-    assert payload["reference_batch_size"] == 2
-    assert "operator_fingerprint" in payload
-    assert payload["outputs"]["loss"]["numel"] == 1
-
-
-def test_deterministic_forward_artifacts_infers_image_keys_without_dataset_meta(tmp_path):
-    class _ImagePolicy(torch.nn.Module):
-        def forward(self, batch):
-            image = batch["observation.images.front"]
-            assert image.dtype == torch.float32
-            assert torch.all((image >= 0.0) & (image <= 1.0))
-            return image.sum(), {"image": image}
-
-    dataset = [{"observation.images.front": torch.tensor([[[0, 255]]], dtype=torch.uint8)}]
-
-    mp.write_deterministic_forward_artifacts(
-        policy=_ImagePolicy(),
-        dataset=dataset,
-        batch_size=1,
-        preprocessor=lambda b: b,
-        output_dir=tmp_path,
-        device_type="cpu",
-    )
-
-    payload = json.loads((tmp_path / "deterministic_forward.json").read_text())
-    assert payload["outputs"]["loss"]["numel"] == 1
-    assert payload["outputs"]["output_dict"]["image"]["dtype"] == "torch.float32"
-
-
-def test_training_profiler_section_records_forward_backward_optimizer(tmp_path):
-    profiler = mp.TrainingProfiler(mode="summary", output_dir=tmp_path, device=torch.device("cpu"))
-    profiler.start()
-    for _ in range(3):
-        with profiler.section("forward"):
-            pass
-        with profiler.section("backward"):
-            pass
-        with profiler.section("optimizer"):
-            pass
-    profiler.step(1, argparse.Namespace(update_s=0.5, dataloading_s=0.01))
-    profiler.finalize()
-
-    payload = json.loads((tmp_path / "step_timing_summary.json").read_text())
-    assert payload["forward_s"]["count"] == 3
-    assert payload["backward_s"]["count"] == 3
-    assert payload["optimizer_s"]["count"] == 3
-    assert payload["total_update_s"]["mean"] == 0.5
-
-
-def test_training_profiler_accepts_metric_like_values(tmp_path):
-    class _MetricLike:
-        def __init__(self, v):
-            self.val = v
-
-    profiler = mp.TrainingProfiler(mode="summary", output_dir=tmp_path, device=torch.device("cpu"))
-    profiler.start()
-    profiler.step(1, argparse.Namespace(update_s=_MetricLike(0.6), dataloading_s=_MetricLike(0.05)))
-    profiler.finalize()
-
-    payload = json.loads((tmp_path / "step_timing_summary.json").read_text())
-    assert payload["total_update_s"]["mean"] == 0.6
-    assert payload["dataloading_s"]["mean"] == 0.05
-
-
-def test_profiler_device_time_uses_generic_attr_first():
-    class _Event:
-        self_device_time_total = 12.3456
-
-    assert mp._get_profiler_device_time_us(_Event()) == 12.3456

uv.lock

@@ -2,30 +2,39 @@ version = 1
 revision = 2
 requires-python = ">=3.12"
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