fix old docs and comments

add docs audit
2026-07-29 20:49:42 +00:00 · 2026-07-28 14:31:30 +02:00 · 2026-07-28 14:31:07 +02:00
58 changed files with 400 additions and 1907 deletions
@@ -0,0 +1,113 @@
+# LeRobot Docs Audit
+
+**Status:** Fresh baseline for a new docs redesign effort · **Date:** 2026-07-27
+**Supersedes:** the June 2026 `DOCS_REDESIGN.md` proposal (lived on the unmerged, now-stale `docs/complete-docs-redesign` branch — abandoned; that branch is ~67k lines behind `main` and should not be resurrected). This document re-audits `docs/source/` from scratch against current `main`, since the docs tree grew substantially in the ~6 weeks between the two audits (many new policies, robots, and benchmarks landed).
+
+---
+
+## Snapshot (current state, in numbers)
+
+- **IA**: 15 top-level `_toctree.yml` sections, 81 `local:` entries, 0 broken toctree links.
+- **Files**: 97 total files in `docs/source/` = 80 real `.mdx` pages + 16 orphaned `policy_*_README.md` stubs (up from 14 in June) + 1 `contributing.md` (a genuine symlink to root `CONTRIBUTING.md`).
+- **Growth**: `.mdx` pages grew 49 → 80 in 6 months (+63%, ~5.2 pages/month), arriving in **bursts against a shared, hand-edited `_toctree.yml`** — e.g. 6 benchmark pages landed via 6 separate PRs within 48 hours (Apr 2026); 3 policy pages within 3 days (Jul 2026).
+- **CLI docs coverage improved sharply since June**: 17 of 18 `lerobot-*` commands now documented (only `lerobot-info`, a diagnostics helper, is not) — the June baseline was 6/17 undocumented.
+- **Decision content**: zero comparison/decision pages exist in `docs/source/` for Policies (15 options), Robots (11), Benchmarks (10), or Teleoperators — this equivalent content already exists, fully written, in root `AGENT_GUIDE.md` §6, but was never ported or linked in.
+- **Teleoperators**: dedicated toctree section has only 2 pages vs. 16-17 registered teleoperator types in `src/`, but all types do get at least one doc mention somewhere (usually buried inside a robot's hardware page) — a discoverability gap, not an absence of content.
+- **Orphan policy READMEs**: of the 16, 3 (Diffusion, TD-MPC, VQ-BeT) have **zero real published documentation anywhere** — the orphan stub is their only artifact. 13 duplicate an actively-maintained `.mdx` page under a different name. One (MolmoAct2) is proven to have silently rotted: a 39-line pre-refactor stub sits next to a 495-line maintained page that diverged 481 lines ago.
+- **Root `README.md` itself has drifted**: its "Supported Hardware" line omits SO-101 (the flagship robot) entirely; its "SoTA Models" table links several policies to thin orphan stubs instead of the richer tutorial pages that exist for them.
+- **4 concrete broken/stale copy-paste commands** verified in published pages (`act.mdx`, `il_robots.mdx` ×2 spots, `lerobot-dataset-v3.mdx`, `openarm.mdx` — the last added as recently as Jan 2026, so a fresh miss, not just aging drift).
+- **Process/CI**: no CODEOWNERS, no `_redirects.yml`, no doc-consistency or toctree-completeness check anywhere; the docs CI workflow triggers only on `paths: docs/**`, so code-only PRs get zero automated docs signal (empirically responsible for the one real lag found — OMX's docs page, 41 days after its code). Despite this, 18 of 19 spot-checked new integrations (EVO1, FastWAM, LingBot-VA, MolmoAct2, RTC, VLA-JEPA, X-VLA, WallOSS, Damiao, Hope Jr, OpenArm, reBot B601, Reachy 2, Unitree G1, RoboCasa365, VLABench, IsaacLab Arena, etc.) shipped docs in the *same commit* as the feature — organic discipline is currently strong.
+- **No content template** exists for policy or robot doc pages: heading sets/length vary 80-528 lines with almost no shared vocabulary; by contrast, benchmarks *do* have a written template (`adding_benchmarks.mdx`) and the resulting 8 pages are visibly consistent — direct proof a template is what produces consistency here.
+
+---
+
+## Strengths
+
+1. **Organic contributor discipline is currently much better than the June audit's numbers implied.** 18/19 spot-checked new policies/robots/benchmarks shipped docs in the same PR as the code; 17/18 CLI commands are documented; all teleoperator types get at least one mention. The underlying practice is healthier than the structural numbers (orphans, thin nav sections) suggest — the real problem is increasingly discoverability and consistency, not absence of effort.
+2. **The doc-builder CI pipeline (PR previews, main build, versioned release builds) is solid, standard infrastructure already in place** — nothing bespoke to build or maintain, and it's the same tooling Transformers uses.
+3. **A working, provably-effective template pattern already exists for one catalog (benchmarks)** — `adding_benchmarks.mdx`'s checklist + "at a glance" table produces 8 structurally consistent pages. This is the single best evidence in the repo for what fixes the policy/robot template gap, and it needs no new invention, just extension.
+4. **Genuine single-source-of-truth patterns already exist and work**: `docs/source/contributing.md` is a real symlink to root `CONTRIBUTING.md` (verified via `ls -la`/`find -type l`). Most policy `src/README.md` files are also symlinked into `docs/source/policy_*_README.md`, eliminating drift at that layer (though the docs-side stub itself remains an unlinked orphan — see weaknesses).
+5. **Zero broken toctree links**, and specific pages are genuinely high quality: `cheat-sheet.mdx` (spot-checked line-by-line against source, fully accurate), `bring_your_own_policies.mdx` (clear scope, working PR checklist), `installation.mdx`'s tables/OS tabs, and the SO-100/SO-101 hardware pages.
+6. **Root `README.md` already contains a usable taxonomy for policies** (Imitation Learning / RL / VLAs / World Models / Reward Models) that the flat 15-item Policies sidebar never adopted — the raw material for a better IA already exists in-repo.
+
+---
+
+## Weaknesses
+
+### High
+- **No comparison/decision-guidance page for any multi-option category** (Policies=15, Robots=11, Benchmarks=10, Teleoperators) — the exact content (decision rules, profiling snapshot) already exists in `AGENT_GUIDE.md` §6 but was never ported into `docs/source`.
+- **The landing page (`index.mdx`) provides zero navigation** — 23 lines of marketing copy and a Discord link, no path to installation, cheat-sheet, or audience-specific guidance.
+- **Teleoperators' dedicated nav section (2 pages) badly undersells real coverage** (16-17 implementations, all mentioned somewhere) — a new user browsing that sidebar section would wrongly conclude only phone/Isaac teleop exist. Content exists; it's a pure discoverability failure.
+- **Multiple concrete broken/stale CLI examples in published, frequently copy-pasted pages**: `act.mdx` tells readers to eval with `lerobot-record` while the shown code uses `lerobot-rollout`; an identical shell-breaking snippet (`\`-terminated comment swallowing a continuation) is duplicated in `il_robots.mdx` and `lerobot-dataset-v3.mdx`; `il_robots.mdx:421` uses the dead `--control.push_to_hub` flag namespace; `openarm.mdx:208-210` (added Jan 2026) gives a `lerobot-record` example with flat pre-draccus flags that don't exist on the current config classes.
+- **3 shipping policies (Diffusion, TD-MPC, VQ-BeT) have zero real published documentation** — only a thin, toctree-unreferenced stub exists for each.
+- **Root `README.md` has drifted as the project scaled**: its Supported Hardware line omits SO-101 entirely; its SoTA Models table links ACT/Diffusion/VQ-BeT/Multitask-DiT/TDMPC/GR00T/SmolVLA to thin orphan stubs instead of the richer tutorial pages that exist for several of them.
+- **No content template for policy/robot doc pages, unlike benchmarks** — heading sets and length vary 80-528 lines with almost no shared vocabulary (`molmoact2.mdx` has no Overview section at all; `smolvla.mdx` has no Overview/Architecture/Citation/License and reads as pure tutorial).
+- **Docs CI triggers only on `paths: docs/**`** — code-only PRs get zero automated docs signal; this is the mechanism directly responsible for the one measured lag (OMX's docs page shipped 41 days after its code, in a separate PR).
+- **Proof that orphan-stub drift isn't hypothetical**: `policy_molmoact2_README.md` is a stale 39-line pre-refactor snapshot sitting 481 lines behind the maintained 495-line page it once mirrored — nothing caught this silently rotting.
+
+### Medium
+- 16 orphaned `policy_*_README.md` files unreferenced by the toctree; 13 are pure duplicate cruft shadowing a maintained same-topic `.mdx` page, creating a false "two files to keep in sync" impression for contributors.
+- No `_redirects.yml` anywhere — future cleanup of the orphans/renames has no 404 safety net.
+- `installation.mdx` ends on a dangling forward-reference ("follow the link below to use LeRobot with your robot") — no link follows.
+- `cheat-sheet.mdx`'s "Policy Types" line is stale (`act, diffusion, smolvla, pi05`) against the current 15-entry Policies section, and lists `diffusion`, which has no working toctree page at all.
+- "Tutorials" (10 pages) mixes beginner, contributor, and RL-researcher content in one flat, unlabeled list.
+- The EnvHub feature family is split inconsistently across two unrelated sections (`envhub.mdx`/`envhub_leisaac.mdx` under Simulation, `envhub_isaaclab_arena.mdx` under Benchmarks) despite identical framing/opening text.
+- Admonition syntax is split GFM `[!NOTE]` vs. doc-builder `<Tip>` across sampled pages; since the site actually builds with `hf-doc-builder`, the majority pattern may not render as a styled callout at all (plausible, not independently render-verified).
+- No CODEOWNERS file anywhere — zero designated reviewer routing for `docs/source`.
+- `CONTRIBUTING.md` never links the good in-repo extension guides (`adding_benchmarks.mdx`, `bring_your_own_policies.mdx`, `integrate_hardware.mdx`) and states no docs-required policy.
+- The "docs required" PR checklist convention is applied inconsistently: Policies and Benchmarks have an explicit required-docs checklist; `integrate_hardware.mdx` (Robots/Teleoperators) has none — a plausible root cause of the Teleoperators gap above.
+- Growth repeatedly stacks simultaneous PRs against one shared, hand-edited `_toctree.yml` (6 benchmark PRs in 48h; 3 policy PRs in 3 days) — a merge/oversight risk even though no damage from it was found yet.
+- `policy_sarm_README.md` has no source of truth left to sync against at all (SARM moved `policies/`→`rewards/`, no README carried over) — pure abandoned cruft next to the real 593-line `sarm.mdx`.
+- No automated check anywhere for toctree completeness or README/mdx sync — the only CI gate is whether the doc-builder build succeeds, which doesn't catch missing coverage.
+
+### Low
+- No dedicated "Motors" section despite `motors/` being named as a distinct hardware layer in `CLAUDE.md`; `feetech.mdx`/`damiao.mdx` sit in a catch-all "Resources" section instead.
+- "Sensors" is a single-page top-level nav section (`cameras.mdx` only).
+- Policies (15), Benchmarks (10), and Robots (11) are flat, ungrouped lists with no internal sub-headings, despite `README.md` already having a usable taxonomy that could be reused.
+- "SO-101" vs. "SO101" naming is inconsistent across ~15 files, and even within a single file (`so100.mdx` uses both).
+- `hilserl.mdx` (950 lines) and `il_robots.mdx` (638 lines) each mix tutorial, reference, and troubleshooting content in one long page.
+- `reachy2_camera` has zero doc mentions anywhere; the `zmq` camera backend is documented only incidentally inside `unitree_g1.mdx` rather than centrally in `cameras.mdx`.
+- The PR template's "Documentation updated" line is a self-reported, unenforced checkbox.
+
+---
+
+## Recommendations
+
+### 1. Quick wins (small effort, ship this week, no maintainer proposal needed)
+- Fix the 4 verified broken commands: `act.mdx` (`lerobot-record`→`lerobot-rollout`), the duplicated shell-breaking snippet in `il_robots.mdx` + `lerobot-dataset-v3.mdx`, `il_robots.mdx`'s `--control.push_to_hub`→`--dataset.push_to_hub`, and `openarm.mdx`'s invalid flat-flag record example.
+- Delete the 3 fully-orphaned policy stubs (Diffusion, TD-MPC, VQ-BeT) and give each a real toctree page by promoting the existing `src/` README content.
+- Delete the remaining 13 duplicate orphan stubs and the dead `policy_sarm_README.md`.
+- Fix `installation.mdx`'s dangling ending and `cheat-sheet.mdx`'s stale policy-type list.
+- Fix root `README.md`: add SO-101 to the Supported Hardware line; repoint the SoTA Models table's links from orphan stubs to the richer existing tutorial pages.
+- Move `envhub_isaaclab_arena.mdx` into Simulation (one-line YAML change).
+- Add 2-4 orientation sentences + links to the top of `index.mdx` (installation, cheat-sheet, "I have hardware" / "I don't" / "I want to contribute") without touching its marketing framing.
+- Publish a single "Choosing a policy" page that ports the already-written decision rules from `AGENT_GUIDE.md` §6 — highest-leverage fix available.
+- Start a `docs/source/_redirects.yml` now, before the orphan cleanup above creates the first real dead links.
+- No action needed on the `contributing.md` "duplication" claim — verified it's a working symlink, not an anti-pattern.
+
+### 2. Structural / UX changes (need a proposal + maintainer buy-in, phase as separate PRs)
+- Split "Tutorials" into an explicit beginner-facing section vs. an "Advanced & Research/Contributor" section (mechanical YAML reorg, no content rewrites).
+- Add a "Teleoperators" index page listing all types with one-line descriptions and links to wherever each is actually documented today.
+- Re-group the flat Policies/Benchmarks/Robots lists into sub-headings reusing the taxonomy `README.md` already has.
+- Treat a dedicated "Motors" section, deeper sub-grouping, and closing the remaining teleoperator/robot coverage gaps as a phased backlog of independently reviewable PRs rather than one restructure.
+- Standardize SO-101/SO101 naming and (after confirming actual doc-builder rendering behavior) the admonition syntax, each as one mechanical, low-risk PR.
+- Longer-term and biggest-ticket: port more of `AGENT_GUIDE.md`'s procedural content (training duration heuristics, eval targets, data-collection tips) into the published site.
+
+### 3. Maintainability / process changes (prevent debt from reaccumulating at the current growth rate)
+- Port `adding_benchmarks.mdx`'s explicit "writing a doc page" checklist/template into `bring_your_own_policies.mdx` and `integrate_hardware.mdx`.
+- Add a CODEOWNERS entry for `docs/source/` (and ideally `_toctree.yml` specifically) so docs PRs get routed to a real reviewer.
+- Link the extension guides from `CONTRIBUTING.md` and state a docs-required policy there.
+- Add a lightweight CI/pre-commit script (not a full doc-builder run) that fails when (a) a `docs/source/*.mdx` file isn't reachable from `_toctree.yml`, or (b) a new `register_subclass` policy/robot/teleoperator/env lands with no corresponding doc file in the same diff.
+- Decide the fate of the `policy_*_README.md` symlink convention going forward: it is currently self-perpetuating because `bring_your_own_policies.mdx`'s own checklist instructs new contributors to create the file that ends up orphaned. Either fold citation/paper content into the main `.mdx` tutorial, or wire the stub into the toctree as a linked citation anchor.
+- Make the PR template's "Documentation updated" checkbox actionable (e.g., "(N/A if this PR only touches tests/CI/refactors)").
+- Defer heavier generated-registry/support-matrix tooling (Transformers/Ultralytics-style single source of truth) until closer to 1.0.
+
+---
+
+## Notes on cross-checking
+
+Five independent audit passes fed this report; two disagreements were resolved during synthesis:
+- **Toctree section count** — 15 is correct (independently parsed twice from `_toctree.yml`).
+- **`contributing.md`** — it's a working symlink, not a hand-duplicated anti-pattern; one pass's `diff`-based claim didn't survive checking `find -type l`.
+- **Orphan-README "sync mechanism"** — mostly fixed at the `src/`↔`docs` layer via symlinks, but that just moved the unresolved drift to the docs-side stub's absence from the toctree, and left old pre-symlink stubs (MolmoAct2) as dead leftovers.
+- **Teleoperators coverage** — the nav-section framing ("~2/16") and the "mentioned somewhere" framing are both true; the real gap is discoverability, not content.
@@ -68,16 +68,17 @@ ENV HOME=/home/user_lerobot \
 # issues with MuJoCo and OpenGL drivers.
 RUN uv venv --python python${PYTHON_VERSION}

-# Install third-party dependencies separately for layer caching
+# Install Python dependencies for caching
 COPY --chown=user_lerobot:user_lerobot setup.py pyproject.toml uv.lock README.md MANIFEST.in ./
-RUN uv sync --locked --extra all --no-install-project --no-cache
+COPY --chown=user_lerobot:user_lerobot src/ src/
+
+RUN uv sync --locked --extra all --no-cache

 RUN chmod +x /lerobot/.venv/lib/python${PYTHON_VERSION}/site-packages/triton/backends/nvidia/bin/ptxas

-# Copy the application source code and install the local project
+# Copy the rest of the application source code
 # Make sure to have the git-LFS files for testing
 COPY --chown=user_lerobot:user_lerobot . .
-RUN uv sync --locked --extra all --no-cache

 # Set the default command
 CMD ["/bin/bash"]
@@ -60,14 +60,15 @@ ENV HOME=/home/user_lerobot \
 # run other Python projects in the same container without dependency conflicts.
 RUN uv venv

-# Install third-party dependencies separately for layer caching
+# Install Python dependencies for caching
 COPY --chown=user_lerobot:user_lerobot setup.py pyproject.toml uv.lock README.md MANIFEST.in ./
-RUN uv sync --locked --extra all --no-install-project --no-cache
+COPY --chown=user_lerobot:user_lerobot src/ src/

-# Copy the application code and install the local project
+RUN uv sync --locked --extra all --no-cache
+
+# Copy the rest of the application code
 # Make sure to have the git-LFS files for testing
 COPY --chown=user_lerobot:user_lerobot . .
-RUN uv sync --locked --extra all --no-cache

 # Set the default command
 CMD ["/bin/bash"]
@@ -76,7 +76,7 @@ If your local computer doesn't have a powerful GPU, you can utilize Google Colab

 ## Evaluating ACT

-Once training is complete, you can evaluate your ACT policy using the `lerobot-record` command with your trained policy. This will run inference and record evaluation episodes:
+Once training is complete, you can evaluate your ACT policy using the `lerobot-rollout` command with your trained policy. This will run inference and record evaluation episodes:

 ```bash
 lerobot-rollout \
@@ -136,10 +136,6 @@ config = RealSenseCameraConfig(
    height=480,
    color_mode=ColorMode.RGB,
    use_depth=True,
-    # Optional fixed color controls. Omit them to leave the current sensor settings unchanged.
-    exposure=120,
-    gain=64,
-    white_balance=4600,
    rotation=Cv2Rotation.NO_ROTATION
 )

@@ -158,15 +154,6 @@ finally:
 ```
 <!-- prettier-ignore-end -->

-Manual color controls disable the corresponding automatic exposure or white-balance mode. Their
-supported ranges vary by camera model; an invalid value raises an error at connection time that
-includes the range reported by the sensor. Requesting an unsupported control also raises an error.
-Omitted controls leave the sensor's existing automatic or manual setting unchanged. These options
-require `use_rgb=True`.
-
-On the RealSense D405, the color stream is provided by the Stereo Module, so changing manual
-exposure or gain also affects the depth stream.
-
 </hfoption>
 </hfoptions>

@@ -194,8 +194,8 @@ lerobot-record \
    --dataset.single_task="Navigate around obstacles" \
    --dataset.streaming_encoding=true \
    --dataset.encoder_threads=2 \
-    # --dataset.rgb_encoder.vcodec=auto \
    --display_data=true
+    # Optionally, set --dataset.rgb_encoder.vcodec=auto to pick a specific video codec
 ```

 Replace `your_username/dataset_name` with your Hugging Face username and a name for your dataset.
@@ -232,8 +232,8 @@ lerobot-record \
    --dataset.private=true \
    --dataset.streaming_encoding=true \
    --dataset.encoder_threads=2 \
-    # --dataset.rgb_encoder.vcodec=auto \
    --display_data=true
+    # Optionally, set --dataset.rgb_encoder.vcodec=auto to pick a specific video codec
 ```

 ### Replay
@@ -278,6 +278,6 @@ lerobot-record \
  --dataset.num_episodes=10 \
  --dataset.streaming_encoding=true \
  --dataset.encoder_threads=2 \
-  # --dataset.rgb_encoder.vcodec=auto \
  --policy.path=outputs/train/hopejr_hand/checkpoints/last/pretrained_model
+  # Optionally, set --dataset.rgb_encoder.vcodec=auto to pick a specific video codec
 ```
@@ -207,8 +207,8 @@ lerobot-record \
    --dataset.num_episodes=5 \
    --dataset.single_task="Grab the black cube" \
    --dataset.streaming_encoding=true \
-    # --dataset.rgb_encoder.vcodec=auto \
    --dataset.encoder_threads=2
+    # Optionally, set --dataset.rgb_encoder.vcodec=auto to pick a specific video codec
 ```
 </hfoption>
 <hfoption id="API example">
@@ -418,7 +418,7 @@ If you want to dive deeper into this important topic, you can check out the [blo

 ## Visualize a dataset

-If you uploaded your dataset to the hub with `--control.push_to_hub=true`, you can [visualize your dataset online](https://huggingface.co/spaces/lerobot/visualize_dataset) by copy pasting your repo id given by:
+If you uploaded your dataset to the hub with `--dataset.push_to_hub=true`, you can [visualize your dataset online](https://huggingface.co/spaces/lerobot/visualize_dataset) by copy pasting your repo id given by:

 ```bash
 echo ${HF_USER}/so101_test
@@ -44,8 +44,8 @@ lerobot-record \
  --dataset.num_episodes=5 \
  --dataset.single_task="Grab the black cube" \
  --dataset.streaming_encoding=true \
-  # --dataset.rgb_encoder.vcodec=auto \
  --dataset.encoder_threads=2
+  # Optionally, set --dataset.rgb_encoder.vcodec=auto to pick a specific video codec
 ```

 See the [recording guide](./il_robots#record-a-dataset) for more details.
@@ -205,9 +205,10 @@ lerobot-record \
    --teleop.type=openarm_leader \
    --teleop.port=can1 \
    --teleop.id=my_leader \
-    --repo-id=my_hf_username/my_openarm_dataset \
-    --fps=30 \
-    --num-episodes=10
+    --dataset.repo_id=my_hf_username/my_openarm_dataset \
+    --dataset.single_task="Grab the black cube" \
+    --dataset.fps=30 \
+    --dataset.num_episodes=10
 ```

 ## Configuration Options
@@ -161,8 +161,8 @@ lerobot-record \
    --dataset.private=true \
    --dataset.streaming_encoding=true \
    --dataset.encoder_threads=2 \
-    # --dataset.rgb_encoder.vcodec=auto \
    --display_data=true
+    # Optionally, set --dataset.rgb_encoder.vcodec=auto to pick a specific video codec
 ```

 #### Specific Options
@@ -203,8 +203,8 @@ lerobot-record \
    --dataset.private=true \
    --dataset.streaming_encoding=true \
    --dataset.encoder_threads=2 \
-    # --dataset.rgb_encoder.vcodec=auto \
    --display_data=true
+    # Optionally, set --dataset.rgb_encoder.vcodec=auto to pick a specific video codec
 ```

 ##### `--robot.use_external_commands`
@@ -100,20 +100,19 @@ Once you are logged in, you can run inference in your setup by doing:
 lerobot-rollout \
  --strategy.type=base \
  --robot.type=so101_follower \
-  --robot.port=/dev/ttyACM0 \ # <- Use your port
-  --robot.id=my_blue_follower_arm \ # <- Use your robot id
-  --robot.cameras="{ front: {type: opencv, index_or_path: 8, width: 640, height: 480, fps: 30}}" \ # <- Use your cameras
-  --task="Grasp a lego block and put it in the bin." \ # <- Use the same task description you used in your dataset recording
-  # <- RTC optional, use when running on low power hardware \
-  # --inference.type=rtc \
-  # --inference.rtc.execution_horizon=10 \
-  # --inference.rtc.max_guidance_weight=10.0 \
-  # <- Teleop optional if you want to teleoperate in between episodes \
-  # --teleop.type=so100_leader \
-  # --teleop.port=/dev/ttyACM0 \
-  # --teleop.id=my_red_leader_arm \
-  # --display_data=true #optional use if you want to see the camera stream \
-  --policy.path=HF_USER/FINETUNE_MODEL_NAME # <- Use your fine-tuned model
+  --robot.port=/dev/ttyACM0 \
+  --robot.id=my_blue_follower_arm \
+  --robot.cameras="{ front: {type: opencv, index_or_path: 8, width: 640, height: 480, fps: 30}}" \
+  --task="Grasp a lego block and put it in the bin." \
+  --policy.path=HF_USER/FINETUNE_MODEL_NAME
 ```

+Replace `--robot.port`, `--robot.id`, `--robot.cameras`, `--task`, and `--policy.path` with your own port, robot ID, camera setup, task description (matching what you used when recording your dataset), and fine-tuned model repo ID.
+
+A few optional flags you can add to the command above:
+
+- **RTC** (useful on low-power hardware): `--inference.type=rtc --inference.rtc.execution_horizon=10 --inference.rtc.max_guidance_weight=10.0`
+- **Teleoperate in between episodes**: `--teleop.type=so100_leader --teleop.port=/dev/ttyACM0 --teleop.id=my_red_leader_arm`
+- **See the camera stream**: `--display_data=true`
+
 Depending on your evaluation setup, you can configure the duration and the number of episodes to record for your evaluation suite.
@@ -494,19 +494,6 @@ ignore_errors = true
 module = "lerobot.envs.*"
 ignore_errors = false

-[[tool.mypy.overrides]]
-module = "lerobot.annotations.*"
-ignore_errors = false
-disallow_untyped_defs = true
-disallow_incomplete_defs = true
-check_untyped_defs = true
-
-[[tool.mypy.overrides]]
-module = "lerobot.transforms.*"
-ignore_errors = false
-disallow_untyped_defs = true
-disallow_incomplete_defs = true
-check_untyped_defs = true

 # [[tool.mypy.overrides]]
 # module = "lerobot.utils.*"
@@ -120,22 +120,14 @@ class OpenCVCamera(Camera):
        self.rotation: int | None = get_cv2_rotation(config.rotation)
        self.backend: int = config.backend

-        self.capture_width: int | None = None
-        self.capture_height: int | None = None
-        self._reset_connection_settings()
+        if self.height and self.width:
+            self.capture_width, self.capture_height = self.width, self.height
+            if self.rotation in [cv2.ROTATE_90_CLOCKWISE, cv2.ROTATE_90_COUNTERCLOCKWISE]:
+                self.capture_width, self.capture_height = self.height, self.width

    def __str__(self) -> str:
        return f"{self.__class__.__name__}({self.index_or_path})"

-    def _reset_connection_settings(self) -> None:
-        """Restore settings that may have been auto-detected during a failed connection."""
-        self.fps = self.config.fps
-        self.width = self.config.width
-        self.height = self.config.height
-        self.capture_width, self.capture_height = self.width, self.height
-        if self.rotation in [cv2.ROTATE_90_CLOCKWISE, cv2.ROTATE_90_COUNTERCLOCKWISE]:
-            self.capture_width, self.capture_height = self.height, self.width
-
    @property
    def is_connected(self) -> bool:
        """Checks if the camera is currently connected and opened."""
@@ -172,25 +164,17 @@ class OpenCVCamera(Camera):
                f"Failed to open {self}.Run `lerobot-find-cameras opencv` to find available cameras."
            )

-        try:
-            self._configure_capture_settings()
-            self._start_read_thread()
+        self._configure_capture_settings()
+        self._start_read_thread()

-            if warmup and self.warmup_s > 0:
-                start_time = time.time()
-                while time.time() - start_time < self.warmup_s:
-                    self.async_read(timeout_ms=self.warmup_s * 1000)
-                    time.sleep(0.1)
-                with self.frame_lock:
-                    if self.latest_frame is None:
-                        raise ConnectionError(f"{self} failed to capture frames during warmup.")
-        except BaseException:
-            try:
-                self._cleanup_resources()
-            except Exception:
-                logger.exception(f"Failed to fully clean up {self} after connect() failed.")
-            self._reset_connection_settings()
-            raise
+        if warmup and self.warmup_s > 0:
+            start_time = time.time()
+            while time.time() - start_time < self.warmup_s:
+                self.async_read(timeout_ms=self.warmup_s * 1000)
+                time.sleep(0.1)
+            with self.frame_lock:
+                if self.latest_frame is None:
+                    raise ConnectionError(f"{self} failed to capture frames during warmup.")

        logger.info(f"{self} connected.")

@@ -328,36 +312,32 @@ class OpenCVCamera(Camera):

        for target in targets_to_scan:
            camera = cv2.VideoCapture(target)
-            try:
-                if camera.isOpened():
-                    default_width = int(camera.get(cv2.CAP_PROP_FRAME_WIDTH))
-                    default_height = int(camera.get(cv2.CAP_PROP_FRAME_HEIGHT))
-                    default_fps = camera.get(cv2.CAP_PROP_FPS)
-                    default_format = camera.get(cv2.CAP_PROP_FORMAT)
+            if camera.isOpened():
+                default_width = int(camera.get(cv2.CAP_PROP_FRAME_WIDTH))
+                default_height = int(camera.get(cv2.CAP_PROP_FRAME_HEIGHT))
+                default_fps = camera.get(cv2.CAP_PROP_FPS)
+                default_format = camera.get(cv2.CAP_PROP_FORMAT)

-                    # Get FOURCC code and convert to string
-                    default_fourcc_code = camera.get(cv2.CAP_PROP_FOURCC)
-                    default_fourcc_code_int = int(default_fourcc_code)
-                    default_fourcc = "".join(
-                        [chr((default_fourcc_code_int >> 8 * i) & 0xFF) for i in range(4)]
-                    )
+                # Get FOURCC code and convert to string
+                default_fourcc_code = camera.get(cv2.CAP_PROP_FOURCC)
+                default_fourcc_code_int = int(default_fourcc_code)
+                default_fourcc = "".join([chr((default_fourcc_code_int >> 8 * i) & 0xFF) for i in range(4)])

-                    camera_info = {
-                        "name": f"OpenCV Camera @ {target}",
-                        "type": "OpenCV",
-                        "id": target,
-                        "backend_api": camera.getBackendName(),
-                        "default_stream_profile": {
-                            "format": default_format,
-                            "fourcc": default_fourcc,
-                            "width": default_width,
-                            "height": default_height,
-                            "fps": default_fps,
-                        },
-                    }
+                camera_info = {
+                    "name": f"OpenCV Camera @ {target}",
+                    "type": "OpenCV",
+                    "id": target,
+                    "backend_api": camera.getBackendName(),
+                    "default_stream_profile": {
+                        "format": default_format,
+                        "fourcc": default_fourcc,
+                        "width": default_width,
+                        "height": default_height,
+                        "fps": default_fps,
+                    },
+                }

-                    found_cameras_info.append(camera_info)
-            finally:
+                found_cameras_info.append(camera_info)
                camera.release()

        return found_cameras_info
@@ -516,26 +496,6 @@ class OpenCVCamera(Camera):
            self.latest_timestamp = None
            self.new_frame_event.clear()

-    def _cleanup_resources(self) -> None:
-        """Stop background reads and release the capture, including after partial setup."""
-        read_thread = self.thread
-        videocapture = self.videocapture
-
-        try:
-            self._stop_read_thread()
-        finally:
-            self.videocapture = None
-            try:
-                if videocapture is not None:
-                    videocapture.release()
-            finally:
-                # Releasing the device may unblock a hardware read that outlived
-                # the first bounded join in _stop_read_thread().
-                if read_thread is not None and read_thread.is_alive():
-                    read_thread.join(timeout=2.0)
-                    if read_thread.is_alive():  # pragma: no cover
-                        logger.warning(f"{self} read thread remained alive after releasing the capture.")
-
    @check_if_not_connected
    def async_read(self, timeout_ms: float = 200) -> NDArray[Any]:
        """
@@ -626,6 +586,16 @@ class OpenCVCamera(Camera):
        if not self.is_connected and self.thread is None:
            raise DeviceNotConnectedError(f"{self} not connected.")

-        self._cleanup_resources()
+        if self.thread is not None:
+            self._stop_read_thread()
+
+        if self.videocapture is not None:
+            self.videocapture.release()
+            self.videocapture = None
+
+        with self.frame_lock:
+            self.latest_frame = None
+            self.latest_timestamp = None
+            self.new_frame_event.clear()

        logger.info(f"{self} disconnected.")
@@ -121,9 +121,6 @@ class RealSenseCamera(Camera):

        self.config = config

-        self.width: int | None = config.width
-        self.height: int | None = config.height
-
        if config.serial_number_or_name.isdigit():
            self.serial_number = config.serial_number_or_name
        else:
@@ -134,9 +131,6 @@ class RealSenseCamera(Camera):
        self.use_rgb = config.use_rgb
        self.use_depth = config.use_depth
        self.warmup_s = config.warmup_s
-        self.exposure: int | None = config.exposure
-        self.gain: int | None = config.gain
-        self.white_balance: int | None = config.white_balance

        self.rs_pipeline: rs.pipeline | None = None
        self.rs_profile: rs.pipeline_profile | None = None
@@ -151,23 +145,14 @@ class RealSenseCamera(Camera):

        self.rotation: int | None = get_cv2_rotation(config.rotation)

-        self.capture_width: int | None = None
-        self.capture_height: int | None = None
-        self._reset_connection_settings()
+        if self.height and self.width:
+            self.capture_width, self.capture_height = self.width, self.height
+            if self.rotation in [cv2.ROTATE_90_CLOCKWISE, cv2.ROTATE_90_COUNTERCLOCKWISE]:
+                self.capture_width, self.capture_height = self.height, self.width

    def __str__(self) -> str:
        return f"{self.__class__.__name__}({self.serial_number})"

-    def _reset_connection_settings(self) -> None:
-        """Restore settings that may have been auto-detected during a failed connection."""
-        self.fps = self.config.fps
-        self.width = self.config.width
-        self.height = self.config.height
-        self.warmup_s = self.config.warmup_s
-        self.capture_width, self.capture_height = self.width, self.height
-        if self.rotation in [cv2.ROTATE_90_CLOCKWISE, cv2.ROTATE_90_COUNTERCLOCKWISE]:
-            self.capture_width, self.capture_height = self.height, self.width
-
    @property
    def is_connected(self) -> bool:
        """Checks if the camera pipeline is started and streams are active."""
@@ -187,8 +172,7 @@ class RealSenseCamera(Camera):

        Raises:
            DeviceAlreadyConnectedError: If the camera is already connected.
-            ValueError: If the configuration is invalid, a requested sensor option is unsupported,
-                or a requested sensor value is invalid.
+            ValueError: If the configuration is invalid (e.g., missing serial/name, name not unique).
            ConnectionError: If the camera is found but fails to start the pipeline or no RealSense devices are detected at all.
            RuntimeError: If the pipeline starts but fails to apply requested settings.
        """
@@ -206,31 +190,22 @@ class RealSenseCamera(Camera):
                f"Failed to open {self}.Run `lerobot-find-cameras realsense` to find available cameras."
            ) from e

-        try:
-            self._configure_capture_settings()
-            self._configure_sensor_options()
-            self._start_read_thread()
+        self._configure_capture_settings()
+        self._start_read_thread()

-            # NOTE(Steven/Caroline): Enforcing at least one second of warmup as RS cameras need a bit of time before the first read. If we don't wait, the first read from the warmup will raise.
-            self.warmup_s = max(self.warmup_s, 1)
+        # NOTE(Steven/Caroline): Enforcing at least one second of warmup as RS cameras need a bit of time before the first read. If we don't wait, the first read from the warmup will raise.
+        self.warmup_s = max(self.warmup_s, 1)

-            warmup_read = self.async_read if self.use_rgb else self.async_read_depth
-            start_time = time.time()
-            while time.time() - start_time < self.warmup_s:
-                warmup_read(timeout_ms=self.warmup_s * 1000)
-                time.sleep(0.1)
-            with self.frame_lock:
-                if (self.use_rgb and self.latest_color_frame is None) or (
-                    self.use_depth and self.latest_depth_frame is None
-                ):
-                    raise ConnectionError(f"{self} failed to capture frames during warmup.")
-        except BaseException:
-            try:
-                self._cleanup_resources()
-            except Exception:
-                logger.exception(f"Failed to fully clean up {self} after connect() failed.")
-            self._reset_connection_settings()
-            raise
+        warmup_read = self.async_read if self.use_rgb else self.async_read_depth
+        start_time = time.time()
+        while time.time() - start_time < self.warmup_s:
+            warmup_read(timeout_ms=self.warmup_s * 1000)
+            time.sleep(0.1)
+        with self.frame_lock:
+            if (self.use_rgb and self.latest_color_frame is None) or (
+                self.use_depth and self.latest_depth_frame is None
+            ):
+                raise ConnectionError(f"{self} failed to capture frames during warmup.")

        logger.info(f"{self} connected.")

@@ -364,111 +339,6 @@ class RealSenseCamera(Camera):
        self.new_frame_event.clear()
        return self._async_read(timeout_ms=10000, read_depth=read_depth)

-    def _get_color_sensor(self) -> "rs.sensor":
-        """Returns the sensor that controls the color stream.
-
-        Most RealSense cameras expose "RGB Camera" for color. The D405 has no
-        separate RGB module — its color stream comes from "Stereo Module".
-        We try RGB Camera first, then fall back to Stereo Module.
-        """
-        if self.rs_profile is None:
-            raise RuntimeError(f"{self}: rs_profile must be initialized before use.")
-
-        device = self.rs_profile.get_device()
-        sensors = {s.get_info(rs.camera_info.name): s for s in device.query_sensors()}
-
-        for name in ("RGB Camera", "Stereo Module"):
-            if name in sensors:
-                return sensors[name]
-
-        available = list(sensors.keys())
-        raise RuntimeError(f"{self}: no color sensor found. Available sensors: {available}")
-
-    def _set_sensor_option(self, sensor: "rs.sensor", option: "rs.option", value: float, label: str) -> None:
-        """Sets a sensor option, re-raising range errors with actionable diagnostics."""
-        try:
-            sensor.set_option(option, value)
-        except Exception as e:
-            range_info = ""
-            try:
-                option_range = sensor.get_option_range(option)
-                range_info = (
-                    f" (supported range: min={option_range.min}, max={option_range.max}, "
-                    f"step={option_range.step}, default={option_range.default})"
-                )
-            except Exception:
-                range_info = " (option range unavailable)"
-            raise ValueError(
-                f"{self}: failed to set {label} to {value}{range_info}. Original error: {e}"
-            ) from e
-
-    def _configure_sensor_options(self) -> None:
-        """Applies manual sensor options (exposure, gain, white balance) to the color sensor.
-
-        When exposure or gain is set, auto-exposure is disabled first. When white_balance
-        is set, auto white balance is disabled first. An omitted option is left unchanged,
-        and configuration is skipped entirely if all options are omitted.
-
-        Raises:
-            ValueError: If the sensor does not support a requested option or a requested
-                value is invalid. Invalid-value errors include the option name, requested
-                value, and supported range when available.
-        """
-        if self.exposure is None and self.gain is None and self.white_balance is None:
-            return
-
-        color_sensor = self._get_color_sensor()
-
-        requested_options = (
-            (rs.option.exposure, self.exposure, "exposure"),
-            (rs.option.gain, self.gain, "gain"),
-            (rs.option.white_balance, self.white_balance, "white balance"),
-        )
-        unsupported_options = [
-            label
-            for option, value, label in requested_options
-            if value is not None and not color_sensor.supports(option)
-        ]
-        if unsupported_options:
-            raise ValueError(
-                f"{self}: color sensor does not support requested manual options: {unsupported_options}."
-            )
-
-        manual_exposure_requested = self.exposure is not None or self.gain is not None
-        if manual_exposure_requested:
-            if color_sensor.supports(rs.option.enable_auto_exposure):
-                self._set_sensor_option(color_sensor, rs.option.enable_auto_exposure, 0, "auto-exposure")
-                logger.info(f"{self} auto-exposure disabled.")
-            else:
-                logger.warning(
-                    f"{self} sensor does not support disabling auto-exposure; "
-                    "applying manual exposure/gain directly."
-                )
-
-        if self.exposure is not None:
-            self._set_sensor_option(color_sensor, rs.option.exposure, self.exposure, "exposure")
-            logger.info(f"{self} exposure set to {self.exposure}.")
-
-        if self.gain is not None:
-            self._set_sensor_option(color_sensor, rs.option.gain, self.gain, "gain")
-            logger.info(f"{self} gain set to {self.gain}.")
-
-        if self.white_balance is not None:
-            if color_sensor.supports(rs.option.enable_auto_white_balance):
-                self._set_sensor_option(
-                    color_sensor, rs.option.enable_auto_white_balance, 0, "auto white balance"
-                )
-                logger.info(f"{self} auto white balance disabled.")
-            else:
-                logger.warning(
-                    f"{self} sensor does not support disabling auto white balance; "
-                    "applying manual white balance directly."
-                )
-            self._set_sensor_option(
-                color_sensor, rs.option.white_balance, self.white_balance, "white balance"
-            )
-            logger.info(f"{self} white balance set to {self.white_balance}.")
-
    @check_if_not_connected
    def read_depth(self, timeout_ms: int = 200) -> NDArray[Any]:
        """
@@ -671,27 +541,6 @@ class RealSenseCamera(Camera):
            self.latest_timestamp = None
            self.new_frame_event.clear()

-    def _cleanup_resources(self) -> None:
-        """Stop background reads and stop the pipeline, including after partial setup."""
-        read_thread = self.thread
-        rs_pipeline = self.rs_pipeline
-
-        try:
-            self._stop_read_thread()
-        finally:
-            self.rs_pipeline = None
-            self.rs_profile = None
-            try:
-                if rs_pipeline is not None:
-                    rs_pipeline.stop()
-            finally:
-                # Stopping the pipeline may unblock a hardware read that outlived
-                # the first bounded join in _stop_read_thread().
-                if read_thread is not None and read_thread.is_alive():
-                    read_thread.join(timeout=2.0)
-                    if read_thread.is_alive():  # pragma: no cover
-                        logger.warning(f"{self} read thread remained alive after stopping the pipeline.")
-
    def _async_read(self, timeout_ms: float, read_depth: bool = False) -> NDArray[Any]:
        """Shared helper for :meth:`async_read`/:meth:`async_read_depth`: return the latest buffered frame."""
        if self.thread is None or not self.thread.is_alive():
@@ -835,5 +684,18 @@ class RealSenseCamera(Camera):
                f"Attempted to disconnect {self}, but it appears already disconnected."
            )

-        self._cleanup_resources()
+        if self.thread is not None:
+            self._stop_read_thread()
+
+        if self.rs_pipeline is not None:
+            self.rs_pipeline.stop()
+            self.rs_pipeline = None
+            self.rs_profile = None
+
+        with self.frame_lock:
+            self.latest_color_frame = None
+            self.latest_depth_frame = None
+            self.latest_timestamp = None
+            self.new_frame_event.clear()
+
        logger.info(f"{self} disconnected.")
@@ -46,17 +46,6 @@ class RealSenseCameraConfig(CameraConfig):
        use_depth: Whether to enable depth stream. Defaults to False.
        rotation: Image rotation setting (0°, 90°, 180°, or 270°). Defaults to no rotation.
        warmup_s: Time reading frames before returning from connect (in seconds)
-        exposure: Manual exposure value for the color sensor. When set, auto-exposure is
-            disabled and this fixed value is used. Valid ranges are camera-model specific
-            and reported if the value is rejected. Defaults to None (leave unchanged).
-        gain: Manual gain value for the color sensor. When set, auto-exposure is disabled
-            and this fixed gain is used, which also freezes exposure at its current value
-            when no exposure is configured. Valid ranges are camera-model specific and
-            reported if the value is rejected. Defaults to None (leave unchanged).
-        white_balance: Manual white balance value for the color sensor. When set, auto
-            white balance is disabled and this fixed value is used. Valid ranges are
-            camera-model specific and reported if the value is rejected. Defaults to None
-            (leave unchanged).

    Note:
        - Either name or serial_number must be specified.
@@ -72,9 +61,6 @@ class RealSenseCameraConfig(CameraConfig):
    use_depth: bool = False
    rotation: Cv2Rotation = Cv2Rotation.NO_ROTATION
    warmup_s: int = 1
-    exposure: int | None = None
-    gain: int | None = None
-    white_balance: int | None = None

    def __post_init__(self) -> None:
        self.color_mode = ColorMode(self.color_mode)
@@ -83,18 +69,6 @@ class RealSenseCameraConfig(CameraConfig):
        if not self.use_rgb and not self.use_depth:
            raise ValueError("At least one of `use_rgb` or `use_depth` must be enabled.")

-        manual_color_options = {
-            "exposure": self.exposure,
-            "gain": self.gain,
-            "white_balance": self.white_balance,
-        }
-        configured_color_options = [name for name, value in manual_color_options.items() if value is not None]
-        if configured_color_options and not self.use_rgb:
-            raise ValueError(
-                "Manual color sensor options require `use_rgb=True`. "
-                f"Configured options: {configured_color_options}."
-            )
-
        values = (self.fps, self.width, self.height)
        if any(v is not None for v in values) and any(v is None for v in values):
            raise ValueError(
@@ -71,19 +71,13 @@ class DatasetRecordConfig:
    # Number of threads per encoder instance. None = auto (codec default).
    # Lower values reduce CPU usage, maps to 'lp' (via svtav1-params) for libsvtav1 and 'threads' for h264/hevc..
    encoder_threads: int | None = None
-    # Skip appending the date-time tag to repo_id, keeping the user-provided name as-is
-    # (e.g. self-managed versioned names intended for a later `lerobot-edit-dataset merge`).
-    no_stamp: bool = False

    def stamp_repo_id(self) -> None:
        """Append a date-time tag to ``repo_id`` so each recording session gets a unique name.

        Must be called explicitly at dataset *creation* time — not on resume,
        where the existing ``repo_id`` (already stamped) must be preserved.
-        No-op when ``no_stamp`` is set, preserving a user-managed ``repo_id``.
        """
-        if self.no_stamp:
-            return
        if self.repo_id:
            timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
            self.repo_id = f"{self.repo_id}_{timestamp}"
@@ -188,8 +188,8 @@ class LeRobotDatasetMetadata:
    def _load_metadata(self):
        self.info = load_info(self.root)
        check_version_compatibility(self.repo_id, self._version, CODEBASE_VERSION)
-        self.tasks = load_tasks(self.root) if self.total_tasks > 0 else None
-        self.episodes = load_episodes(self.root) if self.total_episodes > 0 else None
+        self.tasks = load_tasks(self.root)
+        self.episodes = load_episodes(self.root)
        self.stats = load_stats(self.root)

    def ensure_readable(self) -> None:
@@ -384,12 +384,7 @@ class LiberoEnv(gym.Env):

    def close(self):
        if self._env is not None:
-            try:
-                self._env.close()
-            finally:
-                # LIBERO deletes its inner env on close, so this wrapper must
-                # be recreated before the next reset.
-                self._env = None
+            self._env.close()


 def _make_env_fns(
@@ -384,9 +384,7 @@ class RoboTwinEnv(gym.Env):

        self._env: Any | None = None  # deferred — created on first reset() inside worker
        self._step_count: int = 0
-        self._black_frame: np.ndarray = np.zeros(
-            (self.observation_height, self.observation_width, 3), dtype=np.uint8
-        )
+        self._black_frame = np.zeros((self.observation_height, self.observation_width, 3), dtype=np.uint8)

        image_spaces = {
            cam: spaces.Box(
@@ -373,7 +373,7 @@ class VLABenchEnv(gym.Env):

        if action.shape[0] != 7:
            # Unknown layout — fall back to zero-pad so the sim doesn't crash.
-            padded: np.ndarray = np.zeros(ctrl_dim, dtype=np.float64)
+            padded = np.zeros(ctrl_dim, dtype=np.float64)
            padded[: min(action.shape[0], ctrl_dim)] = action[:ctrl_dim]
            return padded

@@ -122,9 +122,6 @@ MODEL_ENCODING_TABLE = {
    "xm430-w350": X_SERIES_ENCODINGS_TABLE,
    "xm540-w270": X_SERIES_ENCODINGS_TABLE,
    "xc430-w150": X_SERIES_ENCODINGS_TABLE,
-    "xh540-w150": X_SERIES_ENCODINGS_TABLE,
-    "xc330-t288": X_SERIES_ENCODINGS_TABLE,
-    "xc330-t181": X_SERIES_ENCODINGS_TABLE,
 }

 # {model: model_resolution}
@@ -137,9 +134,6 @@ MODEL_RESOLUTION = {
    "xm430-w350": 4096,
    "xm540-w270": 4096,
    "xc430-w150": 4096,
-    "xh540-w150": 4096,
-    "xc330-t288": 4096,
-    "xc330-t181": 4096,
 }

 # {model: model_number}
@@ -151,9 +145,6 @@ MODEL_NUMBER_TABLE = {
    "xm430-w350": 1020,
    "xm540-w270": 1120,
    "xc430-w150": 1070,
-    "xh540-w150": 1110,
-    "xc330-t288": 1220,
-    "xc330-t181": 1210,
 }

 # {model: available_operating_modes}
@@ -165,9 +156,6 @@ MODEL_OPERATING_MODES = {
    "xm430-w350": [0, 1, 3, 4, 5, 16],
    "xm540-w270": [0, 1, 3, 4, 5, 16],
    "xc430-w150": [1, 3, 4, 16],
-    "xh540-w150": [0, 1, 3, 4, 5, 16],
-    "xc330-t288": [0, 1, 3, 4, 5, 16],
-    "xc330-t181": [0, 1, 3, 4, 5, 16],
 }

 MODEL_CONTROL_TABLE = {
@@ -178,9 +166,6 @@ MODEL_CONTROL_TABLE = {
    "xm430-w350": X_SERIES_CONTROL_TABLE,
    "xm540-w270": X_SERIES_CONTROL_TABLE,
    "xc430-w150": X_SERIES_CONTROL_TABLE,
-    "xh540-w150": X_SERIES_CONTROL_TABLE,
-    "xc330-t288": X_SERIES_CONTROL_TABLE,
-    "xc330-t181": X_SERIES_CONTROL_TABLE,
 }

 MODEL_BAUDRATE_TABLE = {
@@ -191,9 +176,6 @@ MODEL_BAUDRATE_TABLE = {
    "xm430-w350": X_SERIES_BAUDRATE_TABLE,
    "xm540-w270": X_SERIES_BAUDRATE_TABLE,
    "xc430-w150": X_SERIES_BAUDRATE_TABLE,
-    "xh540-w150": X_SERIES_BAUDRATE_TABLE,
-    "xc330-t288": X_SERIES_BAUDRATE_TABLE,
-    "xc330-t181": X_SERIES_BAUDRATE_TABLE,
 }

 AVAILABLE_BAUDRATES = [
@@ -44,19 +44,12 @@ from lerobot.utils.constants import (
    POLICY_PREPROCESSOR_DEFAULT_NAME,
 )
 from lerobot.utils.feature_utils import dataset_to_policy_features
-from lerobot.utils.import_utils import _peft_available, require_package

 from .evo1.configuration_evo1 import Evo1Config
 from .groot.configuration_groot import GrootConfig
 from .pretrained import PreTrainedPolicy
 from .utils import validate_visual_features_consistency

-if TYPE_CHECKING or _peft_available:
-    from peft import PeftConfig, PeftModel
-else:
-    PeftConfig = None
-    PeftModel = None
-

 def _reconnect_relative_absolute_steps(
    preprocessor: PolicyProcessorPipeline, postprocessor: PolicyProcessorPipeline
@@ -341,15 +334,12 @@ def make_policy(
        # Load a pretrained PEFT model on top of the policy. The pretrained path points to the folder/repo
        # of the adapter and the adapter's config contains the path to the base policy. So we need the
        # adapter config first, then load the correct policy and then apply PEFT.
-        require_package("peft", extra="peft")
+        from peft import PeftConfig, PeftModel

        logging.info("Loading policy's PEFT adapter.")

        peft_pretrained_path = str(cfg.pretrained_path)
-        peft_config = PeftConfig.from_pretrained(
-            peft_pretrained_path,
-            revision=cfg.pretrained_revision,
-        )
+        peft_config = PeftConfig.from_pretrained(peft_pretrained_path)

        kwargs["pretrained_name_or_path"] = peft_config.base_model_name_or_path
        if not kwargs["pretrained_name_or_path"]:
@@ -360,14 +350,9 @@ def make_policy(
                "the adapter was trained."
            )

-        kwargs["revision"] = peft_config.revision
        policy = policy_cls.from_pretrained(**kwargs)
        policy = PeftModel.from_pretrained(
-            policy,
-            peft_pretrained_path,
-            config=peft_config,
-            revision=cfg.pretrained_revision,
-            is_trainable=True,
+            policy, peft_pretrained_path, config=peft_config, is_trainable=True
        )

    else:
@@ -37,19 +37,13 @@ def is_image_feature(key: str) -> bool:
@dataclass
 class ConcurrencyConfig:
    """Configuration for the concurrency of the actor and learner.
-
    Possible values are:
    - "threads": Use threads for the actor and learner.
    - "processes": Use processes for the actor and learner.
-
-    ``multiprocessing_context`` selects the process-wide start method when
-    processes are used. Set it to ``None`` to preserve Python's default or a
-    method already selected by the embedding application.
    """

    actor: str = "threads"
    learner: str = "threads"
-    multiprocessing_context: str | None = "spawn"


@dataclass
@@ -43,22 +43,11 @@ from torch.distributions import Beta

 from lerobot.policies.pretrained import PreTrainedPolicy
 from lerobot.utils.constants import ACTION
-from lerobot.utils.import_utils import (
-    _peft_available,
-    _scipy_available,
-    _transformers_available,
-    require_package,
-)
+from lerobot.utils.import_utils import _scipy_available, _transformers_available, require_package

 from ..rtc.modeling_rtc import RTCProcessor
 from .configuration_molmoact2 import MolmoAct2Config

-if TYPE_CHECKING or _peft_available:
-    from peft import LoraConfig, get_peft_model
-else:
-    LoraConfig = None
-    get_peft_model = None
-
 logger = logging.getLogger(__name__)


@@ -1742,11 +1731,13 @@ class MolmoAct2Policy(PreTrainedPolicy):

    def _build_inner_lora_config(self):
        require_package("peft", extra="molmoact2")
+        from peft import LoraConfig

        return LoraConfig(**self._get_inner_peft_targets())

    def _apply_lora_adapters(self) -> None:
        require_package("peft", extra="molmoact2")
+        from peft import get_peft_model

        peft_config = self._build_inner_lora_config()
        self._validate_peft_config(peft_config)
@@ -34,22 +34,14 @@ from lerobot.configs import PreTrainedConfig
 from lerobot.configs.train import TrainPipelineConfig
 from lerobot.utils.device_utils import resolve_safetensors_device
 from lerobot.utils.hub import HubMixin
-from lerobot.utils.import_utils import _peft_available, require_package

 from .utils import log_model_loading_keys

-if TYPE_CHECKING or _peft_available:
-    from peft import PEFT_TYPE_TO_CONFIG_MAPPING, PeftType, get_peft_model
-else:
-    PEFT_TYPE_TO_CONFIG_MAPPING = None
-    PeftType = None
-    get_peft_model = None
+T = TypeVar("T", bound="PreTrainedPolicy")

 if TYPE_CHECKING:
    from lerobot.datasets.dataset_metadata import LeRobotDatasetMetadata

-T = TypeVar("T", bound="PreTrainedPolicy")
-

 def _build_card_context(
    cfg: TrainPipelineConfig | None,
@@ -392,7 +384,7 @@ class PreTrainedPolicy(nn.Module, HubMixin, abc.ABC):
            peft_cli_overrides: Optional dict of CLI overrides (method_type, target_modules, r, etc.)
                These are merged with policy defaults to build the final config.
        """
-        require_package("peft", extra="peft")
+        from peft import get_peft_model

        # If user provided a complete config, use it directly (with overrides)
        if peft_config is not None:
@@ -463,7 +455,7 @@ class PreTrainedPolicy(nn.Module, HubMixin, abc.ABC):
        Returns:
            Preprocessed dict with renamed keys and init_type mapped to method-specific key.
        """
-        require_package("peft", extra="peft")
+        from peft import PeftType

        cli_overrides = cli_overrides.copy()

@@ -488,7 +480,7 @@ class PreTrainedPolicy(nn.Module, HubMixin, abc.ABC):

    def _build_peft_config(self, cli_overrides: dict):
        """Build a PEFT config from policy defaults and CLI overrides."""
-        require_package("peft", extra="peft")
+        from peft import PEFT_TYPE_TO_CONFIG_MAPPING, PeftType

        # Determine PEFT method type (default to LORA)
        method_type_str = cli_overrides.get("method_type") or "lora"
@@ -515,7 +507,7 @@ class PreTrainedPolicy(nn.Module, HubMixin, abc.ABC):

    def _apply_peft_cli_overrides(self, peft_config, cli_overrides: dict):
        """Apply CLI overrides to an existing PEFT config."""
-        require_package("peft", extra="peft")
+        from peft import PEFT_TYPE_TO_CONFIG_MAPPING, PeftType

        # Get method type from existing config or CLI override
        method_type_str = cli_overrides.get("method_type")
@@ -132,20 +132,10 @@ class MapDeltaActionToRobotActionStep(RobotActionProcessorStep):
    def transform_features(
        self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]
    ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]:
-        for axis in ["x", "y", "z"]:
+        for axis in ["x", "y", "z", "gripper"]:
            features[PipelineFeatureType.ACTION].pop(f"delta_{axis}", None)
-        features[PipelineFeatureType.ACTION].pop("gripper", None)

-        for feat in [
-            "enabled",
-            "target_x",
-            "target_y",
-            "target_z",
-            "target_wx",
-            "target_wy",
-            "target_wz",
-            "gripper_vel",
-        ]:
+        for feat in ["enabled", "target_x", "target_y", "target_z", "target_wx", "target_wy", "target_wz"]:
            features[PipelineFeatureType.ACTION][f"{feat}"] = PolicyFeature(
                type=FeatureType.ACTION, shape=(1,)
            )
@@ -91,7 +91,7 @@ from lerobot.robots import so_follower  # noqa: F401
 from lerobot.teleoperators import gamepad, so_leader  # noqa: F401
 from lerobot.teleoperators.utils import TeleopEvents
 from lerobot.utils.device_utils import get_safe_torch_device
-from lerobot.utils.process import ProcessSignalHandler, ensure_multiprocessing_start_method
+from lerobot.utils.process import ProcessSignalHandler
 from lerobot.utils.random_utils import set_seed
 from lerobot.utils.robot_utils import precise_sleep
 from lerobot.utils.transition import (
@@ -124,7 +124,9 @@ def actor_cli(cfg: TrainRLServerPipelineConfig):
    cfg.validate()
    display_pid = False
    if not use_threads(cfg):
-        ensure_multiprocessing_start_method(cfg.policy.concurrency.multiprocessing_context)
+        import torch.multiprocessing as mp
+
+        mp.set_start_method("spawn")
        display_pid = True

    # Create logs directory to ensure it exists
@@ -102,7 +102,7 @@ from lerobot.utils.constants import (
 )
 from lerobot.utils.device_utils import get_safe_torch_device
 from lerobot.utils.io_utils import load_json, write_json
-from lerobot.utils.process import ProcessSignalHandler, ensure_multiprocessing_start_method
+from lerobot.utils.process import ProcessSignalHandler
 from lerobot.utils.random_utils import set_seed
 from lerobot.utils.utils import (
    format_big_number,
@@ -123,7 +123,9 @@ def train_cli(cfg: TrainRLServerPipelineConfig):
    # Fail fast with a friendly error if the optional ``hilserl`` extra is missing.
    require_package("grpcio", extra="hilserl", import_name="grpc")
    if not use_threads(cfg):
-        ensure_multiprocessing_start_method(cfg.policy.concurrency.multiprocessing_context)
+        import torch.multiprocessing as mp
+
+        mp.set_start_method("spawn")

    # Use the job_name from the config
    train(
@@ -46,12 +46,6 @@ class SOFollowerConfig:
    position_i_coefficient: int = 0
    position_d_coefficient: int = 32

-    # Number of extra attempts when a `sync_read` of the motors fails. Feetech buses can occasionally
-    # return a corrupted status packet ("Incorrect status packet!"), especially when several joints move
-    # at once, which otherwise aborts the control loop. Retries are immediate (no sleep) and only happen on
-    # failure, so the steady-state read cost is unchanged.
-    num_read_retries: int = 2
-

@RobotConfig.register_subclass("so101_follower")
@RobotConfig.register_subclass("so100_follower")
@@ -180,7 +180,7 @@ class SOFollower(Robot):
    def get_observation(self) -> RobotObservation:
        # Read arm position
        start = time.perf_counter()
-        obs_dict = self.bus.sync_read("Present_Position", num_retry=self.config.num_read_retries)
+        obs_dict = self.bus.sync_read("Present_Position")
        obs_dict = {f"{motor}.pos": val for motor, val in obs_dict.items()}
        dt_ms = (time.perf_counter() - start) * 1e3
        logger.debug(f"{self} read state: {dt_ms:.1f}ms")
@@ -221,7 +221,7 @@ class SOFollower(Robot):
        # Cap goal position when too far away from present position.
        # /!\ Slower fps expected due to reading from the follower.
        if self.config.max_relative_target is not None:
-            present_pos = self.bus.sync_read("Present_Position", num_retry=self.config.num_read_retries)
+            present_pos = self.bus.sync_read("Present_Position")
            goal_present_pos = {key: (g_pos, present_pos[key]) for key, g_pos in goal_pos.items()}
            goal_pos = ensure_safe_goal_position(goal_present_pos, self.config.max_relative_target)

@@ -24,7 +24,6 @@ from __future__ import annotations
 import logging
 from dataclasses import dataclass, field
 from threading import Event
-from typing import TYPE_CHECKING

 import torch

@@ -48,7 +47,6 @@ from lerobot.processor.relative_action_processor import RelativeActionsProcessor
 from lerobot.robots import make_robot_from_config
 from lerobot.teleoperators import Teleoperator, make_teleoperator_from_config
 from lerobot.utils.feature_utils import combine_feature_dicts, hw_to_dataset_features
-from lerobot.utils.import_utils import _peft_available, require_package

 from .configs import BaseStrategyConfig, DAggerStrategyConfig, RolloutConfig
 from .inference import (
@@ -59,12 +57,6 @@ from .inference import (
 )
 from .robot_wrapper import ThreadSafeRobot

-if TYPE_CHECKING or _peft_available:
-    from peft import PeftConfig, PeftModel
-else:
-    PeftConfig = None
-    PeftModel = None
-
 logger = logging.getLogger(__name__)


@@ -179,7 +171,7 @@ def _load_pretrained_policy(policy_config: PreTrainedConfig) -> PreTrainedPolicy
            revision=pretrained_revision,
        )

-    require_package("peft", extra="peft")
+    from peft import PeftConfig, PeftModel

    peft_path = policy_config.pretrained_path
    peft_config = PeftConfig.from_pretrained(peft_path, revision=pretrained_revision)
@@ -302,22 +294,12 @@ def build_rollout_context(
    # ``observation_features`` values are either a tuple (camera shape) or the
    # ``float`` type itself used as a sentinel for scalar motor features —
    # see ``dict[str, type | tuple]`` annotation on ``Robot.observation_features``.
-    # Keep cameras (tuple) plus both joint-position (.pos) and base-velocity (.vel)
-    # scalar state features. LeKiwi's observation.state is 9-dim (6 arm .pos +
-    # x/y/theta.vel) and the policy was trained/normalized on all 9; the old .pos-only
-    # filter fed a 6-dim state into a 9-dim normalizer → RuntimeError (size 6 vs 9).
-    # Pure-arm robots have no .vel state keys, so this is a no-op for them.
    observation_features_hw = {
        k: v
        for k, v in all_obs_features.items()
-        if isinstance(v, tuple) or (v is float and k.endswith((".pos", ".vel")))
+        if isinstance(v, tuple) or (v is float and k.endswith(".pos"))
    }
-    # Keep both joint-position (.pos) and base-velocity (.vel) action features so
-    # mobile manipulators command the base too (e.g. LeKiwi: 6 arm .pos +
-    # x/y/theta.vel = 9-dim action). Pure-arm robots have no .vel keys, so this is
-    # a no-op for them. Without the .vel keys the base velocities are silently
-    # dropped from dataset_features[ACTION]/ordered_action_keys and the base never moves.
-    action_features_hw = {k: v for k, v in robot.action_features.items() if k.endswith((".pos", ".vel"))}
+    action_features_hw = {k: v for k, v in robot.action_features.items() if k.endswith(".pos")}

    # The action side is always needed: sync inference reads action names from
    # ``dataset_features[ACTION]`` to map policy tensors back to robot actions.
@@ -36,7 +36,6 @@ python src/lerobot/scripts/augment_dataset_quantile_stats.py \
 import argparse
 import concurrent.futures
 import logging
-import os
 from pathlib import Path

 import numpy as np
@@ -53,7 +52,6 @@ from lerobot.datasets import (
    get_feature_stats,
    write_stats,
 )
-from lerobot.datasets.compute_stats import sample_indices
 from lerobot.utils.utils import init_logging


@@ -79,14 +77,12 @@ def has_quantile_stats(stats: dict[str, dict] | None, quantile_list_keys: list[s
    return False


-def process_single_episode(dataset: LeRobotDataset, episode_idx: int, use_sampling: bool = True) -> dict:
+def process_single_episode(dataset: LeRobotDataset, episode_idx: int) -> dict:
    """Process a single episode and return its statistics.

    Args:
        dataset: The LeRobot dataset
        episode_idx: Index of the episode to process
-        use_sampling: If True, sub-sample image/video frames per episode to bound
-            memory. If False, use every frame (exact, higher memory).

    Returns:
        Dictionary containing episode statistics
@@ -96,31 +92,16 @@ def process_single_episode(dataset: LeRobotDataset, episode_idx: int, use_sampli
    start_idx = dataset.meta.episodes[episode_idx]["dataset_from_index"]
    end_idx = dataset.meta.episodes[episode_idx]["dataset_to_index"]

-    episode_len = end_idx - start_idx
-
-    # Images/video are the memory hog, so sub-sample those frames per episode;
-    # numeric columns are cheap, so read them in full (exact).
-    image_keys = [k for k in dataset.features if dataset.features[k]["dtype"] in ("image", "video")]
-    numeric_keys = [
-        k for k in dataset.features if dataset.features[k]["dtype"] not in ("image", "video", "string")
-    ]
-
    collected_data: dict[str, list] = {}
+    for idx in range(start_idx, end_idx):
+        item = dataset[idx]
+        for key, value in item.items():
+            if key not in dataset.features:
+                continue

-    # Numeric features: every frame, read directly from the underlying table.
-    if numeric_keys:
-        numeric_cols = dataset.hf_dataset.select_columns(numeric_keys)[start_idx:end_idx]
-        for key in numeric_keys:
-            collected_data[key] = [torch.as_tensor(v) for v in numeric_cols[key]]
-
-    # Image/video features: decode only a sampled subset of frames.
-    if image_keys:
-        sampled_offsets = sample_indices(episode_len) if use_sampling else list(range(episode_len))
-        for offset in sampled_offsets:
-            item = dataset[start_idx + offset]
-            for key in image_keys:
-                if key in item:
-                    collected_data.setdefault(key, []).append(item[key])
+            if key not in collected_data:
+                collected_data[key] = []
+            collected_data[key].append(value)

    ep_stats = {}
    for key, data_list in collected_data.items():
@@ -150,13 +131,11 @@ def process_single_episode(dataset: LeRobotDataset, episode_idx: int, use_sampli
    return ep_stats


-def compute_quantile_stats_for_dataset(dataset: LeRobotDataset, use_sampling: bool = True) -> dict[str, dict]:
+def compute_quantile_stats_for_dataset(dataset: LeRobotDataset) -> dict[str, dict]:
    """Compute quantile statistics for all episodes in the dataset.

    Args:
        dataset: The LeRobot dataset to compute statistics for
-        use_sampling: If True, sub-sample image/video frames per episode to bound
-            memory. If False, use every frame (exact, higher memory).

    Returns:
        Dictionary containing aggregated statistics with quantiles
@@ -174,15 +153,15 @@ def compute_quantile_stats_for_dataset(dataset: LeRobotDataset, use_sampling: bo
    if has_videos:
        logging.info("Dataset contains video keys - using sequential processing for thread safety")
        for episode_idx in tqdm(range(dataset.num_episodes), desc="Processing episodes"):
-            ep_stats = process_single_episode(dataset, episode_idx, use_sampling)
+            ep_stats = process_single_episode(dataset, episode_idx)
            episode_stats_list.append(ep_stats)
    else:
        logging.info("Dataset has no video keys - using parallel processing for better performance")
-        max_workers = min(dataset.num_episodes, int(os.environ.get("LEROBOT_STATS_MAX_WORKERS", 16)))
+        max_workers = min(dataset.num_episodes, 16)

        with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:
            future_to_episode = {
-                executor.submit(process_single_episode, dataset, episode_idx, use_sampling): episode_idx
+                executor.submit(process_single_episode, dataset, episode_idx): episode_idx
                for episode_idx in range(dataset.num_episodes)
            }

@@ -209,7 +188,6 @@ def augment_dataset_with_quantile_stats(
    repo_id: str,
    root: str | Path | None = None,
    overwrite: bool = False,
-    use_sampling: bool = True,
 ) -> None:
    """Augment a dataset with quantile statistics if they are missing.

@@ -217,8 +195,6 @@ def augment_dataset_with_quantile_stats(
        repo_id: Repository ID of the dataset
        root: Local root directory for the dataset
        overwrite: Overwrite existing quantile statistics if they already exist
-        use_sampling: If True, sub-sample image/video frames per episode to bound
-            memory. If False, use every frame (exact, higher memory).
    """
    logging.info(f"Loading dataset: {repo_id}")
    dataset = LeRobotDataset(
@@ -232,7 +208,7 @@ def augment_dataset_with_quantile_stats(

    logging.info("Dataset does not contain quantile statistics. Computing them now...")

-    new_stats = compute_quantile_stats_for_dataset(dataset, use_sampling=use_sampling)
+    new_stats = compute_quantile_stats_for_dataset(dataset)

    logging.info("Updating dataset metadata with new quantile statistics")
    dataset.meta.stats = new_stats
@@ -272,14 +248,6 @@ def main():
        action="store_true",
        help="Overwrite existing quantile statistics if they already exist",
    )
-    parser.add_argument(
-        "--no-sampling",
-        action="store_true",
-        help=(
-            "Compute stats over every frame (exact, higher memory). By default, "
-            "image/video frames are sub-sampled per episode to bound memory."
-        ),
-    )

    args = parser.parse_args()
    root = Path(args.root) if args.root else None
@@ -290,7 +258,6 @@ def main():
        repo_id=args.repo_id,
        root=root,
        overwrite=args.overwrite,
-        use_sampling=not args.no_sampling,
    )


@@ -94,8 +94,6 @@ from lerobot.datasets.video_utils import concatenate_video_files, get_video_dura
 from lerobot.utils.constants import HF_LEROBOT_HOME
 from lerobot.utils.utils import flatten_dict, init_logging

-logger = logging.getLogger(__name__)
-
 V21 = "v2.1"
 V30 = "v3.0"

@@ -478,11 +476,11 @@ def convert_dataset(
    # First check if the dataset already has a v3.0 version
    if root is None and not force_conversion:
        try:
-            logger.info("Trying to download v3.0 version of the dataset from the hub...")
+            print("Trying to download v3.0 version of the dataset from the hub...")
            snapshot_download(repo_id, repo_type="dataset", revision=V30, local_dir=HF_LEROBOT_HOME / repo_id)
            return
        except Exception:
-            logger.info("Dataset does not have an uploaded v3.0 version. Continuing with conversion.")
+            print("Dataset does not have an uploaded v3.0 version. Continuing with conversion.")

    # Set root based on whether local dataset path is provided
    use_local_dataset = False
@@ -490,7 +488,7 @@ def convert_dataset(
    if root.exists():
        validate_local_dataset_version(root)
        use_local_dataset = True
-        logger.info(f"Using local dataset at {root}")
+        print(f"Using local dataset at {root}")

    old_root = root.parent / f"{root.name}_old"
    new_root = root.parent / f"{root.name}_v30"
@@ -525,7 +523,7 @@ def convert_dataset(
        try:
            hub_api.delete_tag(repo_id, tag=CODEBASE_VERSION, repo_type="dataset")
        except (HTTPError, RevisionNotFoundError) as e:
-            logger.warning(f"tag={CODEBASE_VERSION} probably doesn't exist. Skipping exception ({e})")
+            print(f"tag={CODEBASE_VERSION} probably doesn't exist. Skipping exception ({e})")
            pass
        hub_api.delete_files(
            delete_patterns=["data/chunk*/episode_*", "meta/*.jsonl", "videos/chunk*"],
@@ -154,14 +154,14 @@ def _push_to_hub(root: Path, cfg: AnnotationPipelineConfig) -> None:
    repo_id = cfg.new_repo_id or cfg.repo_id
    commit_message = cfg.push_commit_message or "Add steerable annotations (lerobot-annotate)"
    api = HfApi()
-    logger.info(f"[lerobot-annotate] creating/locating dataset repo {repo_id}...")
+    print(f"[lerobot-annotate] creating/locating dataset repo {repo_id}...", flush=True)
    api.create_repo(
        repo_id=repo_id,
        repo_type="dataset",
        private=cfg.push_private,
        exist_ok=True,
    )
-    logger.info(f"[lerobot-annotate] uploading {root} -> {repo_id}...")
+    print(f"[lerobot-annotate] uploading {root} -> {repo_id}...", flush=True)
    commit_info = api.upload_folder(
        folder_path=str(root),
        repo_id=repo_id,
@@ -172,7 +172,7 @@ def _push_to_hub(root: Path, cfg: AnnotationPipelineConfig) -> None:
        # at the source dataset; a fresh card is generated below instead.
        ignore_patterns=[".annotate_staging/**", "**/.DS_Store", "README.md"],
    )
-    logger.info(f"[lerobot-annotate] uploaded to https://huggingface.co/datasets/{repo_id}")
+    print(f"[lerobot-annotate] uploaded to https://huggingface.co/datasets/{repo_id}", flush=True)

    dataset_info = load_info(root)
    card = create_lerobot_dataset_card(dataset_info=dataset_info, license="apache-2.0", repo_id=repo_id)
@@ -200,13 +200,14 @@ def _push_to_hub(root: Path, cfg: AnnotationPipelineConfig) -> None:
        with suppress(RevisionNotFoundError):
            api.delete_tag(repo_id, tag=version_tag, repo_type="dataset")
        api.create_tag(**tag_kwargs)
-        logger.info(f"[lerobot-annotate] tagged {repo_id} as {version_tag}")
+        print(f"[lerobot-annotate] tagged {repo_id} as {version_tag}", flush=True)
    except Exception as exc:  # noqa: BLE001
-        logger.warning(
+        print(
            f"[lerobot-annotate] WARNING: could not create tag {version_tag!r} on {repo_id}: {exc}. "
            "Dataset is uploaded but ``LeRobotDataset`` won't be able to load it until it's tagged. "
            "Run: from huggingface_hub import HfApi; "
-            f"HfApi().create_tag({repo_id!r}, tag={version_tag!r}, repo_type='dataset', exist_ok=True)"
+            f"HfApi().create_tag({repo_id!r}, tag={version_tag!r}, repo_type='dataset', exist_ok=True)",
+            flush=True,
        )


@@ -89,8 +89,6 @@ from lerobot.datasets import LeRobotDataset
 from lerobot.utils.constants import ACTION, DONE, OBS_STATE, REWARD, SUCCESS
 from lerobot.utils.utils import init_logging

-logger = logging.getLogger(__name__)
-
 DEFAULT_FOXGLOVE_PORT = 8765
 DEFAULT_RERUN_PORT = 9090

@@ -301,7 +299,7 @@ def visualize_dataset(
            while True:
                time.sleep(1)
        except KeyboardInterrupt:
-            logger.info("Ctrl-C received. Exiting.")
+            print("Ctrl-C received. Exiting.")


 def main():
@@ -62,7 +62,7 @@ from dataclasses import asdict
 from functools import partial
 from pathlib import Path
 from pprint import pformat
-from typing import TYPE_CHECKING, Any, TypedDict
+from typing import Any, TypedDict

 import einops
 import gymnasium as gym
@@ -87,7 +87,7 @@ from lerobot.processor import PolicyProcessorPipeline
 from lerobot.types import PolicyAction
 from lerobot.utils.constants import ACTION, DONE, OBS_IMAGE, OBS_IMAGES, OBS_STR, REWARD
 from lerobot.utils.device_utils import get_safe_torch_device
-from lerobot.utils.import_utils import _peft_available, register_third_party_plugins, require_package
+from lerobot.utils.import_utils import register_third_party_plugins
 from lerobot.utils.io_utils import write_video
 from lerobot.utils.random_utils import set_seed
 from lerobot.utils.utils import (
@@ -95,14 +95,6 @@ from lerobot.utils.utils import (
    inside_slurm,
 )

-if TYPE_CHECKING or _peft_available:
-    from peft import PeftModel
-else:
-    PeftModel = None
-
-
-logger = logging.getLogger(__name__)
-

 def _env_features_to_dataset_features(env_features: dict) -> dict:
    """Convert EnvConfig.features to the dict format expected by LeRobotDataset.create()."""
@@ -452,11 +444,13 @@ def eval_policy(
        exc = ValueError(
            f"Policy of type 'PreTrainedPolicy' is expected, but type '{type(policy)}' was provided."
        )
-        if not _peft_available:
-            raise exc
-        require_package("peft", extra="peft")
-        if not isinstance(policy, PeftModel):
-            raise exc
+        try:
+            from peft import PeftModel
+
+            if not isinstance(policy, PeftModel):
+                raise exc
+        except ImportError:
+            raise exc from None

    start = time.time()
    # Preserve the mode for direct callers. eval_policy_all scopes the mode
@@ -564,7 +558,7 @@ def eval_policy(
        if seeds:
            all_seeds.extend(seeds)
        else:
-            all_seeds.extend([None] * env.num_envs)
+            all_seeds.append(None)

        # FIXME: episode_data is either None or it doesn't exist
        if return_episode_data:
@@ -802,13 +796,13 @@ def eval_main(cfg: EvalPipelineConfig):
            recording_repo_id=cfg.eval.recording_repo_id,
            recording_private=cfg.eval.recording_private,
        )
-        logger.info("Overall Aggregated Metrics:")
-        logger.info(info["overall"])
+        print("Overall Aggregated Metrics:")
+        print(info["overall"])

        # Print per-suite stats
        for task_group, task_group_info in info.items():
-            logger.info(f"\nAggregated Metrics for {task_group}:")
-            logger.info(task_group_info)
+            print(f"\nAggregated Metrics for {task_group}:")
+            print(task_group_info)
    # Close all vec envs
    close_envs(envs)

@@ -40,7 +40,6 @@ from PIL import Image
 from lerobot.cameras import ColorMode
 from lerobot.cameras.opencv import OpenCVCamera, OpenCVCameraConfig
 from lerobot.cameras.realsense import RealSenseCamera, RealSenseCameraConfig
-from lerobot.utils.utils import init_logging

 logger = logging.getLogger(__name__)

@@ -286,8 +285,6 @@ def save_images_from_all_cameras(


 def main():
-    init_logging()
-
    parser = argparse.ArgumentParser(
        description="Unified camera utility script for listing cameras and capturing images."
    )
@@ -165,7 +165,6 @@ from lerobot.robots import (  # noqa: F401
    earthrover_mini_plus,
    hope_jr,
    koch_follower,
-    lekiwi,
    omx_follower,
    openarm_follower,
    reachy2,
@@ -22,8 +22,7 @@ import dataclasses
 import logging
 import sys
 import time
-from collections.abc import Iterator
-from contextlib import contextmanager, nullcontext
+from contextlib import nullcontext
 from pprint import pformat
 from typing import TYPE_CHECKING, Any

@@ -58,7 +57,7 @@ from lerobot.optim.factory import make_optimizer_and_scheduler
 from lerobot.policies import PreTrainedPolicy, make_policy, make_pre_post_processors
 from lerobot.rewards import make_reward_pre_post_processors
 from lerobot.utils.collate import lerobot_collate_fn
-from lerobot.utils.import_utils import _peft_available, register_third_party_plugins, require_package
+from lerobot.utils.import_utils import register_third_party_plugins
 from lerobot.utils.logging_utils import AverageMeter, MetricsTracker
 from lerobot.utils.random_utils import set_seed
 from lerobot.utils.utils import (
@@ -69,28 +68,9 @@ from lerobot.utils.utils import (
    inside_slurm,
 )

-if TYPE_CHECKING or _peft_available:
-    from peft import PeftModel
-else:
-    PeftModel = None
-
 from .lerobot_eval import eval_policy_all


-@contextmanager
-def _make_eval_envs(cfg: TrainPipelineConfig) -> Iterator[dict[str, dict[int, Any]]]:
-    """Create evaluation environments for one run and always dispose of them."""
-    envs = make_env(
-        cfg.env,
-        n_envs=cfg.eval.batch_size,
-        use_async_envs=cfg.eval.use_async_envs,
-    )
-    try:
-        yield envs
-    finally:
-        close_envs(envs)
-
-
 def _dataloader_worker_kwargs(cfg: TrainPipelineConfig) -> dict[str, Any]:
    """Return worker-only DataLoader options, disabling them for single-process loading."""
    workers_enabled = cfg.num_workers > 0
@@ -227,6 +207,8 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
    if cfg.job.is_remote:
        return submit_to_hf(cfg)

+    from lerobot.utils.import_utils import require_package
+
    require_package("accelerate", extra="training")
    from accelerate import Accelerator
    from accelerate.utils import DistributedDataParallelKwargs, DistributedType
@@ -295,6 +277,14 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
    if not is_main_process:
        dataset, eval_dataset = make_train_eval_datasets(cfg)

+    # Create environment used for evaluating checkpoints during training on simulation data.
+    # On real-world data, no need to create an environment as evaluations are done outside train.py,
+    # using the eval.py instead, with gym_dora environment and dora-rs.
+    eval_env = None
+    if cfg.env_eval_freq > 0 and cfg.env is not None and is_main_process:
+        logging.info("Creating env")
+        eval_env = make_env(cfg.env, n_envs=cfg.eval.batch_size, use_async_envs=cfg.eval.use_async_envs)
+
    if cfg.is_reward_model_training:
        if is_main_process:
            logging.info("Creating reward model")
@@ -322,7 +312,7 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
    if cfg.peft is not None:
        if cfg.is_reward_model_training:
            raise ValueError("PEFT is only supported for policy training. ")
-        require_package("peft", extra="peft")
+        from peft import PeftModel

        if isinstance(policy, PeftModel):
            logging.info("PEFT adapter already loaded from checkpoint, skipping wrap_with_peft.")
@@ -702,7 +692,7 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
            if is_main_process:
                step_id = get_step_identifier(step, cfg.steps)
                logging.info(f"Eval policy at step {step}")
-                with _make_eval_envs(cfg) as eval_env, torch.no_grad(), accelerator.autocast():
+                with torch.no_grad(), accelerator.autocast():
                    eval_info = eval_policy_all(
                        envs=eval_env,  # dict[suite][task_id] -> vec_env
                        policy=accelerator.unwrap_model(policy),
@@ -750,6 +740,9 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
    if is_main_process:
        progbar.close()

+    if eval_env:
+        close_envs(eval_env)
+
    is_fsdp = accelerator.distributed_type == DistributedType.FSDP
    model_state_dict = accelerator.get_state_dict(policy) if is_fsdp else None
    if is_main_process:
@@ -45,7 +45,6 @@ lerobot-train-tokenizer \
 """

 import json
-import logging
 from dataclasses import dataclass
 from pathlib import Path
 from typing import TYPE_CHECKING
@@ -64,9 +63,6 @@ else:
 from lerobot.configs import NormalizationMode, parser
 from lerobot.datasets import LeRobotDataset
 from lerobot.utils.constants import ACTION, OBS_STATE
-from lerobot.utils.utils import init_logging
-
-logger = logging.getLogger(__name__)


@dataclass
@@ -278,8 +274,11 @@ def process_episode(args):

        return action_chunks

-    except Exception:
-        logger.exception("Error processing episode %s", ep_idx)
+    except Exception as e:
+        print(f"Error processing episode {ep_idx}: {e}")
+        import traceback
+
+        traceback.print_exc()
        return None


@@ -301,10 +300,10 @@ def train_fast_tokenizer(
    Returns:
        Trained FAST tokenizer
    """
-    logger.info(f"Training FAST tokenizer on {len(action_chunks)} action chunks...")
-    logger.info(f"Action chunk shape: {action_chunks.shape}")
-    logger.info(f"Vocab size: {vocab_size}")
-    logger.info(f"DCT scale: {scale}")
+    print(f"Training FAST tokenizer on {len(action_chunks)} action chunks...")
+    print(f"Action chunk shape: {action_chunks.shape}")
+    print(f"Vocab size: {vocab_size}")
+    print(f"DCT scale: {scale}")

    # download the tokenizer source code (not pretrained weights)
    # we'll train a new tokenizer on our own data
@@ -315,7 +314,7 @@ def train_fast_tokenizer(

    # train the new tokenizer on our action data using .fit()
    # this trains the BPE tokenizer on DCT coefficients
-    logger.info("Training new tokenizer (this may take a few minutes)...")
+    print("Training new tokenizer (this may take a few minutes)...")
    tokenizer = base_tokenizer.fit(
        action_data_list,
        scale=scale,
@@ -323,21 +322,21 @@ def train_fast_tokenizer(
        time_horizon=action_chunks.shape[1],  # action_horizon
        action_dim=action_chunks.shape[2],  # encoded dimensions
    )
-    logger.info("✓ Tokenizer training complete!")
+    print("✓ Tokenizer training complete!")

    # validate it works
    sample_chunk = action_chunks[0]
    encoded = tokenizer(sample_chunk[None])[0]
    if isinstance(encoded, list):
        encoded = np.array(encoded)
-    logger.info(f"Sample encoding: {len(encoded)} tokens for chunk shape {sample_chunk.shape}")
+    print(f"Sample encoding: {len(encoded)} tokens for chunk shape {sample_chunk.shape}")

    return tokenizer


 def compute_compression_stats(tokenizer, action_chunks: np.ndarray):
    """Compute compression statistics."""
-    logger.info("\nComputing compression statistics...")
+    print("\nComputing compression statistics...")

    # sample for stats (use max 1000 chunks for speed)
    sample_size = min(1000, len(action_chunks))
@@ -367,12 +366,12 @@ def compute_compression_stats(tokenizer, action_chunks: np.ndarray):
        "max_token_length": float(np.max(token_lengths)),
    }

-    logger.info("Compression Statistics:")
-    logger.info(f"  Average compression ratio: {stats['compression_ratio']:.2f}x")
-    logger.info(f"  Mean token length: {stats['mean_token_length']:.1f}")
-    logger.info(f"  P99 token length: {stats['p99_token_length']:.0f}")
-    logger.info(f"  Min token length: {stats['min_token_length']:.0f}")
-    logger.info(f"  Max token length: {stats['max_token_length']:.0f}")
+    print("Compression Statistics:")
+    print(f"  Average compression ratio: {stats['compression_ratio']:.2f}x")
+    print(f"  Mean token length: {stats['mean_token_length']:.1f}")
+    print(f"  P99 token length: {stats['p99_token_length']:.0f}")
+    print(f"  Min token length: {stats['min_token_length']:.0f}")
+    print(f"  Max token length: {stats['max_token_length']:.0f}")

    return stats

@@ -386,9 +385,9 @@ def train_tokenizer(cfg: TokenizerTrainingConfig):
        cfg: TokenizerTrainingConfig dataclass with all configuration parameters
    """
    # load dataset
-    logger.info(f"Loading dataset: {cfg.repo_id}")
+    print(f"Loading dataset: {cfg.repo_id}")
    dataset = LeRobotDataset(repo_id=cfg.repo_id, root=cfg.root)
-    logger.info(f"Dataset loaded: {dataset.num_episodes} episodes, {dataset.num_frames} frames")
+    print(f"Dataset loaded: {dataset.num_episodes} episodes, {dataset.num_frames} frames")

    # parse normalization mode
    try:
@@ -398,7 +397,7 @@ def train_tokenizer(cfg: TokenizerTrainingConfig):
            f"Invalid normalization_mode: {cfg.normalization_mode}. "
            f"Must be one of: {', '.join([m.value for m in NormalizationMode])}"
        ) from err
-    logger.info(f"Normalization mode: {norm_mode.value}")
+    print(f"Normalization mode: {norm_mode.value}")

    # parse encoded dimensions
    encoded_dim_ranges = []
@@ -407,38 +406,38 @@ def train_tokenizer(cfg: TokenizerTrainingConfig):
        encoded_dim_ranges.append((start, end))

    total_encoded_dims = sum(end - start for start, end in encoded_dim_ranges)
-    logger.info(f"Encoding {total_encoded_dims} dimensions: {cfg.encoded_dims}")
+    print(f"Encoding {total_encoded_dims} dimensions: {cfg.encoded_dims}")

    # parse relative dimensions
    relative_dim_list = None
    if cfg.relative_dims is not None and cfg.relative_dims.strip():
        relative_dim_list = [int(d.strip()) for d in cfg.relative_dims.split(",")]
-        logger.info(f"Relative dimensions: {relative_dim_list}")
+        print(f"Relative dimensions: {relative_dim_list}")
    else:
-        logger.info("No relative dimensions specified")
+        print("No relative dimensions specified")

-    logger.info(f"Use relative transform: {cfg.use_relative_transform}")
+    print(f"Use relative transform: {cfg.use_relative_transform}")
    if cfg.use_relative_transform and (relative_dim_list is None or len(relative_dim_list) == 0):
-        logger.warning(
+        print(
            "Warning: use_relative_transform=True but no relative_dims specified. "
            "No relative transform will be applied."
        )

-    logger.info(f"Action horizon: {cfg.action_horizon}")
-    logger.info(f"State key: {cfg.state_key}")
+    print(f"Action horizon: {cfg.action_horizon}")
+    print(f"State key: {cfg.state_key}")

    # determine episodes to process
    num_episodes = dataset.num_episodes
    if cfg.max_episodes is not None:
        num_episodes = min(cfg.max_episodes, num_episodes)

-    logger.info(f"Processing {num_episodes} episodes...")
+    print(f"Processing {num_episodes} episodes...")

    # process episodes sequentially (to avoid pickling issues with dataset)
    all_chunks = []
    for ep_idx in range(num_episodes):
        if ep_idx % 10 == 0:
-            logger.info(f"  Processing episode {ep_idx}/{num_episodes}...")
+            print(f"  Processing episode {ep_idx}/{num_episodes}...")

        chunks = process_episode(
            (
@@ -456,19 +455,19 @@ def train_tokenizer(cfg: TokenizerTrainingConfig):

    # concatenate all chunks
    all_chunks = np.concatenate(all_chunks, axis=0)
-    logger.info(f"Collected {len(all_chunks)} action chunks")
+    print(f"Collected {len(all_chunks)} action chunks")

    # extract only encoded dimensions FIRST (before normalization)
    encoded_chunks = []
    for start, end in encoded_dim_ranges:
        encoded_chunks.append(all_chunks[:, :, start:end])
    encoded_chunks = np.concatenate(encoded_chunks, axis=-1)  # [N, H, D_encoded]
-    logger.info(f"Extracted {encoded_chunks.shape[-1]} encoded dimensions")
+    print(f"Extracted {encoded_chunks.shape[-1]} encoded dimensions")

    # apply normalization to encoded dimensions
-    logger.info("\nBefore normalization - overall stats:")
-    logger.info(f"  Min: {np.min(encoded_chunks):.4f}, Max: {np.max(encoded_chunks):.4f}")
-    logger.info(f"  Mean: {np.mean(encoded_chunks):.4f}, Std: {np.std(encoded_chunks):.4f}")
+    print("\nBefore normalization - overall stats:")
+    print(f"  Min: {np.min(encoded_chunks):.4f}, Max: {np.max(encoded_chunks):.4f}")
+    print(f"  Mean: {np.mean(encoded_chunks):.4f}, Std: {np.std(encoded_chunks):.4f}")

    # get normalization stats from dataset
    norm_stats = dataset.meta.stats
@@ -490,9 +489,9 @@ def train_tokenizer(cfg: TokenizerTrainingConfig):
                    encoded_stats[stat_name] = stat_array[encoded_dim_indices]

        if encoded_stats:
-            logger.info(f"\nNormalization stats for encoded dimensions (mode: {norm_mode.value}):")
+            print(f"\nNormalization stats for encoded dimensions (mode: {norm_mode.value}):")
            for stat_name, stat_values in encoded_stats.items():
-                logger.info(
+                print(
                    f"  {stat_name}: shape={stat_values.shape}, "
                    f"range=[{np.min(stat_values):.4f}, {np.max(stat_values):.4f}]"
                )
@@ -500,27 +499,27 @@ def train_tokenizer(cfg: TokenizerTrainingConfig):
            # apply normalization based on mode
            try:
                encoded_chunks = apply_normalization(encoded_chunks, encoded_stats, norm_mode, eps=1e-8)
-                logger.info(f"\nApplied {norm_mode.value} normalization")
+                print(f"\nApplied {norm_mode.value} normalization")
            except ValueError as e:
-                logger.warning(f"Warning: {e}. Using raw actions without normalization.")
+                print(f"Warning: {e}. Using raw actions without normalization.")

-            logger.info("\nAfter normalization - overall stats:")
-            logger.info(f"  Min: {np.min(encoded_chunks):.4f}, Max: {np.max(encoded_chunks):.4f}")
-            logger.info(f"  Mean: {np.mean(encoded_chunks):.4f}, Std: {np.std(encoded_chunks):.4f}")
+            print("\nAfter normalization - overall stats:")
+            print(f"  Min: {np.min(encoded_chunks):.4f}, Max: {np.max(encoded_chunks):.4f}")
+            print(f"  Mean: {np.mean(encoded_chunks):.4f}, Std: {np.std(encoded_chunks):.4f}")

-            logger.info("\nPer-dimension stats (after normalization):")
+            print("\nPer-dimension stats (after normalization):")
            for d in range(encoded_chunks.shape[-1]):
                dim_data = encoded_chunks[:, :, d]
-                logger.info(
+                print(
                    f"  Dim {d}: min={np.min(dim_data):7.4f}, max={np.max(dim_data):7.4f}, "
                    f"mean={np.mean(dim_data):7.4f}, std={np.std(dim_data):7.4f}"
                )
        else:
-            logger.warning("Warning: Could not extract stats for encoded dimensions, using raw actions")
+            print("Warning: Could not extract stats for encoded dimensions, using raw actions")
    else:
-        logger.warning("Warning: No normalization stats found in dataset, using raw actions")
+        print("Warning: No normalization stats found in dataset, using raw actions")

-    logger.info(f"Encoded chunks shape: {encoded_chunks.shape}")
+    print(f"Encoded chunks shape: {encoded_chunks.shape}")

    # train FAST tokenizer
    tokenizer = train_fast_tokenizer(
@@ -562,8 +561,8 @@ def train_tokenizer(cfg: TokenizerTrainingConfig):
    with open(output_path / "metadata.json", "w") as f:
        json.dump(metadata, f, indent=2)

-    logger.info(f"\nSaved FAST tokenizer to {output_path}")
-    logger.info(f"Metadata: {json.dumps(metadata, indent=2)}")
+    print(f"\nSaved FAST tokenizer to {output_path}")
+    print(f"Metadata: {json.dumps(metadata, indent=2)}")

    # push to Hugging Face Hub if requested
    if cfg.push_to_hub:
@@ -571,10 +570,10 @@ def train_tokenizer(cfg: TokenizerTrainingConfig):
        hub_repo_id = cfg.hub_repo_id
        if hub_repo_id is None:
            hub_repo_id = output_path.name
-            logger.info(f"\nNo hub_repo_id provided, using: {hub_repo_id}")
+            print(f"\nNo hub_repo_id provided, using: {hub_repo_id}")

-        logger.info(f"\nPushing tokenizer to Hugging Face Hub: {hub_repo_id}")
-        logger.info(f"   Private: {cfg.hub_private}")
+        print(f"\nPushing tokenizer to Hugging Face Hub: {hub_repo_id}")
+        print(f"   Private: {cfg.hub_private}")

        try:
            # use the tokenizer's push_to_hub method
@@ -594,15 +593,14 @@ def train_tokenizer(cfg: TokenizerTrainingConfig):
                commit_message="Upload tokenizer metadata",
            )

-            logger.info(f"Successfully pushed tokenizer to: https://huggingface.co/{hub_repo_id}")
+            print(f"Successfully pushed tokenizer to: https://huggingface.co/{hub_repo_id}")
        except Exception as e:
-            logger.error(f"Error pushing to hub: {e}")
-            logger.error("   Make sure you're logged in with `huggingface-cli login`")
+            print(f"Error pushing to hub: {e}")
+            print("   Make sure you're logged in with `huggingface-cli login`")


 def main():
    """CLI entry point that parses arguments and runs the tokenizer training."""
-    init_logging()
    train_tokenizer()


@@ -29,12 +29,6 @@ class SOLeaderConfig:
    # Whether to use degrees for angles
    use_degrees: bool = True

-    # Number of extra attempts when a `sync_read` of the motors fails. Feetech buses can occasionally
-    # return a corrupted status packet ("Incorrect status packet!"), especially when several joints move
-    # at once, which otherwise aborts the teleoperation loop. Retries are immediate (no sleep) and only
-    # happen on failure, so the steady-state read cost is unchanged.
-    num_read_retries: int = 2
-

@TeleoperatorConfig.register_subclass("so101_leader")
@TeleoperatorConfig.register_subclass("so100_leader")
@@ -145,7 +145,7 @@ class SOLeader(Teleoperator):
    @check_if_not_connected
    def get_action(self) -> dict[str, float]:
        start = time.perf_counter()
-        action = self.bus.sync_read("Present_Position", num_retry=self.config.num_read_retries)
+        action = self.bus.sync_read("Present_Position")
        action = {f"{motor}.pos": val for motor, val in action.items()}
        dt_ms = (time.perf_counter() - start) * 1e3
        logger.debug(f"{self} read action: {dt_ms:.1f}ms")
@@ -13,34 +13,18 @@
 # limitations under the License.

 from .transforms import (
-    CoarseDropout,
-    GammaCorrection,
-    GaussianNoise,
-    GaussianPatchBrightness,
    ImageTransformConfig,
    ImageTransforms,
    ImageTransformsConfig,
-    JPEGCompression,
-    MotionBlur,
-    PlanckianJitter,
-    RandomShadow,
    RandomSubsetApply,
    SharpnessJitter,
    make_transform_from_config,
 )

 __all__ = [
-    "CoarseDropout",
-    "GammaCorrection",
-    "GaussianNoise",
-    "GaussianPatchBrightness",
    "ImageTransformConfig",
    "ImageTransforms",
    "ImageTransformsConfig",
-    "JPEGCompression",
-    "MotionBlur",
-    "PlanckianJitter",
-    "RandomShadow",
    "RandomSubsetApply",
    "SharpnessJitter",
    "make_transform_from_config",
@@ -14,13 +14,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import collections
-import math
 from collections.abc import Callable, Sequence
 from dataclasses import dataclass, field
 from typing import Any

 import torch
-from torchvision.io import decode_image, encode_jpeg
 from torchvision.transforms import v2
 from torchvision.transforms.v2 import (
    Transform,
@@ -43,7 +41,7 @@ class RandomSubsetApply(Transform):

    def __init__(
        self,
-        transforms: Sequence[Callable[..., Any]],
+        transforms: Sequence[Callable],
        p: list[float] | None = None,
        n_subset: int | None = None,
        random_order: bool = False,
@@ -52,7 +50,7 @@ class RandomSubsetApply(Transform):
        if not isinstance(transforms, Sequence):
            raise TypeError("Argument transforms should be a sequence of callables")
        if p is None:
-            p = [1.0] * len(transforms)
+            p = [1] * len(transforms)
        elif len(p) != len(transforms):
            raise ValueError(
                f"Length of p doesn't match the number of transforms: {len(p)} != {len(transforms)}"
@@ -71,7 +69,7 @@ class RandomSubsetApply(Transform):
        self.n_subset = n_subset
        self.random_order = random_order

-        self.selected_transforms: list[Callable[..., Any]] = []
+        self.selected_transforms = None

    def forward(self, *inputs: Any) -> Any:
        needs_unpacking = len(inputs) > 1
@@ -121,7 +119,7 @@ class SharpnessJitter(Transform):
        super().__init__()
        self.sharpness = self._check_input(sharpness)

-    def _check_input(self, sharpness: float | Sequence[float]) -> tuple[float, float]:
+    def _check_input(self, sharpness):
        if isinstance(sharpness, (int | float)):
            if sharpness < 0:
                raise ValueError("If sharpness is a single number, it must be non negative.")
@@ -146,471 +144,6 @@ class SharpnessJitter(Transform):
        return self._call_kernel(F.adjust_sharpness, inpt, sharpness_factor=sharpness_factor)


-class GaussianNoise(Transform):
-    """Add Gaussian noise to simulate camera sensor noise.
-
-    Models readout noise from ADC quantization, which increases in low-light conditions.
-    Common in real-robot setups where wrist cameras operate in suboptimal lighting.
-
-    Args:
-        std: Range (min, max) for noise standard deviation in pixel-value scale (0-255).
-    """
-
-    def __init__(self, std: float | Sequence[float] = (5.0, 25.0)) -> None:
-        super().__init__()
-        if isinstance(std, (int, float)):
-            self.std = (0.0, float(std))
-        elif isinstance(std, Sequence) and len(std) == 2:
-            self.std = (float(std[0]), float(std[1]))
-        else:
-            raise TypeError("std must be a number or a sequence with length 2.")
-        if not 0.0 <= self.std[0] <= self.std[1]:
-            raise ValueError(f"std must satisfy 0 <= min <= max, but got {self.std}.")
-
-    def make_params(self, flat_inputs: list[Any]) -> dict[str, Any]:
-        return {
-            "std": torch.empty(1).uniform_(self.std[0], self.std[1]).item(),
-            "seed": torch.randint(0, torch.iinfo(torch.int64).max, ()).item(),
-        }
-
-    def transform(self, inpt: Any, params: dict[str, Any]) -> Any:
-        if isinstance(inpt, torch.Tensor) and inpt.is_floating_point():
-            generator = torch.Generator(device=inpt.device).manual_seed(params["seed"])
-            noise = torch.randn(inpt.shape, device=inpt.device, dtype=inpt.dtype, generator=generator)
-            return (inpt + noise * (params["std"] / 255.0)).clamp(0.0, 1.0)
-        return inpt
-
-
-class MotionBlur(Transform):
-    """Apply directional motion blur to simulate fast robot or object movement.
-
-    Generates a 1D averaging kernel along a random direction, applied via depthwise convolution.
-
-    Args:
-        kernel_size: An odd kernel size or a range containing at least one odd kernel size.
-    """
-
-    def __init__(self, kernel_size: int | Sequence[int] = (3, 11)) -> None:
-        super().__init__()
-        if isinstance(kernel_size, int):
-            self.kernel_size = (kernel_size, kernel_size)
-        elif isinstance(kernel_size, Sequence) and len(kernel_size) == 2:
-            self.kernel_size = (int(kernel_size[0]), int(kernel_size[1]))
-        else:
-            raise TypeError("kernel_size must be an int or a sequence with length 2.")
-        if not 1 <= self.kernel_size[0] <= self.kernel_size[1]:
-            raise ValueError(f"kernel_size must satisfy 1 <= min <= max, but got {self.kernel_size}.")
-        self._first_odd_kernel_size = self.kernel_size[0] + (self.kernel_size[0] + 1) % 2
-        if self._first_odd_kernel_size > self.kernel_size[1]:
-            raise ValueError(f"kernel_size range must contain an odd value, but got {self.kernel_size}.")
-
-    def make_params(self, flat_inputs: list[Any]) -> dict[str, Any]:
-        num_odd_sizes = (self.kernel_size[1] - self._first_odd_kernel_size) // 2 + 1
-        size_index = int(torch.randint(0, num_odd_sizes, ()).item())
-        ks = self._first_odd_kernel_size + 2 * size_index
-        angle = torch.empty(1).uniform_(0, 360).item()
-        return {"kernel_size": ks, "angle": angle}
-
-    def transform(self, inpt: Any, params: dict[str, Any]) -> Any:
-        if not isinstance(inpt, torch.Tensor) or not inpt.is_floating_point():
-            return inpt
-        if inpt.ndim < 3:
-            raise ValueError(f"MotionBlur expects [..., C, H, W] input, but got shape {inpt.shape}.")
-
-        kernel_size = params["kernel_size"]
-        radius = kernel_size // 2
-        angle = math.radians(params["angle"])
-        positions = torch.linspace(-radius, radius, kernel_size, device=inpt.device)
-        x_coords = (positions * math.cos(angle)).round().to(torch.long) + radius
-        y_coords = (positions * math.sin(angle)).round().to(torch.long) + radius
-        kernel = torch.zeros((kernel_size, kernel_size), device=inpt.device, dtype=inpt.dtype)
-        kernel[y_coords, x_coords] = 1
-        kernel /= kernel.sum()
-
-        channels, height, width = inpt.shape[-3:]
-        flat_input = inpt.reshape(-1, channels, height, width)
-        depthwise_kernel = kernel.expand(channels, 1, kernel_size, kernel_size)
-        padded = torch.nn.functional.pad(flat_input, (radius,) * 4, mode="replicate")
-        output = torch.nn.functional.conv2d(padded, depthwise_kernel, groups=channels)
-        return output.reshape(inpt.shape).clamp(0.0, 1.0)
-
-
-class JPEGCompression(Transform):
-    """Simulate JPEG compression artifacts (block artifacts, color banding).
-
-    Models quality degradation from video compression in network-streamed camera feeds.
-
-    Args:
-        quality: Range (min, max) for JPEG quality factor (lower = more artifacts).
-    """
-
-    def __init__(self, quality: int | Sequence[int] = (15, 75)) -> None:
-        super().__init__()
-        if isinstance(quality, int):
-            self.quality = (quality, quality)
-        elif isinstance(quality, Sequence) and len(quality) == 2:
-            self.quality = (int(quality[0]), int(quality[1]))
-        else:
-            raise TypeError("quality must be an int or a sequence with length 2.")
-        if not 1 <= self.quality[0] <= self.quality[1] <= 100:
-            raise ValueError(f"quality must satisfy 1 <= min <= max <= 100, but got {self.quality}.")
-
-    def make_params(self, flat_inputs: list[Any]) -> dict[str, Any]:
-        return {"quality": int(torch.randint(self.quality[0], self.quality[1] + 1, (1,)).item())}
-
-    def transform(self, inpt: Any, params: dict[str, Any]) -> Any:
-        if not isinstance(inpt, torch.Tensor) or not inpt.is_floating_point():
-            return inpt
-        if inpt.ndim < 3:
-            raise ValueError(f"JPEGCompression expects [..., C, H, W] input, but got shape {inpt.shape}.")
-
-        channels, height, width = inpt.shape[-3:]
-        if channels not in (1, 3):
-            raise ValueError(f"JPEGCompression expects 1 or 3 channels, but got {channels}.")
-
-        flat_input = inpt.reshape(-1, channels, height, width)
-        flat_uint8 = (flat_input.clamp(0.0, 1.0) * 255).round().to(torch.uint8).cpu()
-        decoded_frames = [
-            decode_image(encode_jpeg(frame, quality=params["quality"])) for frame in flat_uint8.unbind()
-        ]
-        output = torch.stack(decoded_frames).to(device=inpt.device, dtype=inpt.dtype) / 255.0
-        return output.reshape(inpt.shape)
-
-
-class GaussianPatchBrightness(Transform):
-    """Apply spatially-varying brightness with Gaussian patches.
-
-    Simulates uneven overhead lighting, spotlights, and shadow patches commonly
-    encountered in real robot workspaces with multiple light sources.
-
-    Args:
-        num_patches: Range (min, max) for number of brightness patches.
-        sigma_range: Range for Gaussian sigma as fraction of image size.
-        factor_range: Range for brightness factor (< 1 darkens, > 1 brightens).
-    """
-
-    def __init__(
-        self,
-        num_patches: int | Sequence[int] = (1, 4),
-        sigma_range: Sequence[float] = (0.05, 0.25),
-        factor_range: Sequence[float] = (0.4, 1.6),
-    ) -> None:
-        super().__init__()
-        if isinstance(num_patches, int):
-            self.num_patches = (num_patches, num_patches)
-        elif isinstance(num_patches, Sequence) and len(num_patches) == 2:
-            self.num_patches = (int(num_patches[0]), int(num_patches[1]))
-        else:
-            raise TypeError("num_patches must be an int or a sequence with length 2.")
-        if not 1 <= self.num_patches[0] <= self.num_patches[1]:
-            raise ValueError(f"num_patches must satisfy 1 <= min <= max, but got {self.num_patches}.")
-        if not isinstance(sigma_range, Sequence) or len(sigma_range) != 2:
-            raise TypeError("sigma_range must be a sequence with length 2.")
-        self.sigma_range = (float(sigma_range[0]), float(sigma_range[1]))
-        if not 0.0 < self.sigma_range[0] <= self.sigma_range[1]:
-            raise ValueError(f"sigma_range must satisfy 0 < min <= max, but got {self.sigma_range}.")
-        if not isinstance(factor_range, Sequence) or len(factor_range) != 2:
-            raise TypeError("factor_range must be a sequence with length 2.")
-        self.factor_range = (float(factor_range[0]), float(factor_range[1]))
-        if not 0.0 <= self.factor_range[0] <= self.factor_range[1]:
-            raise ValueError(f"factor_range must satisfy 0 <= min <= max, but got {self.factor_range}.")
-
-    def make_params(self, flat_inputs: list[Any]) -> dict[str, Any]:
-        n = int(torch.randint(self.num_patches[0], self.num_patches[1] + 1, (1,)).item())
-        return {
-            "centers": torch.rand(n, 2).tolist(),
-            "sigmas": torch.empty(n).uniform_(self.sigma_range[0], self.sigma_range[1]).tolist(),
-            "factors": torch.empty(n).uniform_(self.factor_range[0], self.factor_range[1]).tolist(),
-        }
-
-    def transform(self, inpt: Any, params: dict[str, Any]) -> Any:
-        if not isinstance(inpt, torch.Tensor) or not inpt.is_floating_point():
-            return inpt
-        h, w = inpt.shape[-2:]
-        mask = torch.ones(h, w, device=inpt.device, dtype=inpt.dtype)
-        grid_y = torch.linspace(0, 1, h, device=inpt.device, dtype=inpt.dtype)
-        grid_x = torch.linspace(0, 1, w, device=inpt.device, dtype=inpt.dtype)
-        yy, xx = torch.meshgrid(grid_y, grid_x, indexing="ij")
-        for (cy, cx), sigma, factor in zip(
-            params["centers"], params["sigmas"], params["factors"], strict=True
-        ):
-            gauss = torch.exp(-((yy - cy) ** 2 + (xx - cx) ** 2) / (2 * sigma**2))
-            mask = mask * (1.0 + (factor - 1.0) * gauss)
-        broadcast_shape = (1,) * (inpt.ndim - 2) + (h, w)
-        return (inpt * mask.reshape(broadcast_shape)).clamp(0.0, 1.0)
-
-
-class RandomShadow(Transform):
-    """Add random vertical band shadow with smooth edges.
-
-    Simulates cast shadows from objects or people near the robot workspace.
-    Symmetric: randomly brightens or darkens to prevent BatchNorm stats shift.
-
-    Args:
-        opacity: Range (min, max) for shadow/highlight opacity.
-    """
-
-    def __init__(self, opacity: float | Sequence[float] = (0.3, 0.6)) -> None:
-        super().__init__()
-        if isinstance(opacity, (int, float)):
-            self.opacity = (float(opacity), float(opacity))
-        elif isinstance(opacity, Sequence) and len(opacity) == 2:
-            self.opacity = (float(opacity[0]), float(opacity[1]))
-        else:
-            raise TypeError("opacity must be a number or a sequence with length 2.")
-        if not 0.0 <= self.opacity[0] <= self.opacity[1] <= 1.0:
-            raise ValueError(f"opacity must satisfy 0 <= min <= max <= 1, but got {self.opacity}.")
-
-    def make_params(self, flat_inputs: list[Any]) -> dict[str, Any]:
-        return {
-            "opacity": torch.empty(1).uniform_(self.opacity[0], self.opacity[1]).item(),
-            "start": torch.rand(1).item(),
-            "width": torch.empty(1).uniform_(1 / 3, 2 / 3).item(),
-            "direction": -1.0 if torch.rand(1).item() < 0.5 else 1.0,
-        }
-
-    def transform(self, inpt: Any, params: dict[str, Any]) -> Any:
-        if not isinstance(inpt, torch.Tensor) or not inpt.is_floating_point():
-            return inpt
-        if inpt.ndim < 3:
-            raise ValueError(f"RandomShadow expects [..., C, H, W] input, but got shape {inpt.shape}.")
-
-        h, w = inpt.shape[-2:]
-        band_width = max(1, min(w, round(params["width"] * w)))
-        x_start = round(params["start"] * (w - band_width))
-        x_end = x_start + band_width
-        mask = torch.ones(h, w, device=inpt.device, dtype=inpt.dtype)
-        mask[:, x_start:x_end] = 1.0 + params["direction"] * params["opacity"]
-
-        smoothing_size = min(8, h, w)
-        if smoothing_size > 1:
-            batched_mask = mask[None, None]
-            small = torch.nn.functional.avg_pool2d(batched_mask, smoothing_size, stride=smoothing_size)
-            mask = torch.nn.functional.interpolate(small, size=(h, w), mode="bilinear", align_corners=False)[
-                0, 0
-            ]
-
-        broadcast_shape = (1,) * (inpt.ndim - 2) + (h, w)
-        return (inpt * mask.reshape(broadcast_shape)).clamp(0.0, 1.0)
-
-
-class CoarseDropout(Transform):
-    """Drop random rectangular patches to simulate partial occlusion.
-
-    Models objects, hands, or cables passing through the camera field of view
-    during robot manipulation.
-
-    Args:
-        max_holes: Maximum number of rectangular patches to drop.
-        max_height_frac: Maximum patch height as fraction of image height.
-        max_width_frac: Maximum patch width as fraction of image width.
-        fill_value: Value to fill dropped regions with.
-    """
-
-    def __init__(
-        self,
-        max_holes: int = 8,
-        max_height_frac: float = 0.07,
-        max_width_frac: float = 0.07,
-        fill_value: float = 0.0,
-    ) -> None:
-        super().__init__()
-        if not isinstance(max_holes, int):
-            raise TypeError("max_holes must be an int.")
-        if max_holes < 1:
-            raise ValueError(f"max_holes must be at least 1, but got {max_holes}.")
-        if not 0.0 < max_height_frac <= 1.0:
-            raise ValueError(f"max_height_frac must be in (0, 1], but got {max_height_frac}.")
-        if not 0.0 < max_width_frac <= 1.0:
-            raise ValueError(f"max_width_frac must be in (0, 1], but got {max_width_frac}.")
-        if not 0.0 <= fill_value <= 1.0:
-            raise ValueError(f"fill_value must be in [0, 1], but got {fill_value}.")
-        self.max_holes = max_holes
-        self.max_height_frac = max_height_frac
-        self.max_width_frac = max_width_frac
-        self.fill_value = fill_value
-
-    def make_params(self, flat_inputs: list[Any]) -> dict[str, Any]:
-        n = int(torch.randint(1, self.max_holes + 1, (1,)).item())
-        sizes = torch.rand(n, 2)
-        sizes[:, 0] *= self.max_height_frac
-        sizes[:, 1] *= self.max_width_frac
-        return {"sizes": sizes.tolist(), "positions": torch.rand(n, 2).tolist()}
-
-    def transform(self, inpt: Any, params: dict[str, Any]) -> Any:
-        if not isinstance(inpt, torch.Tensor) or not inpt.is_floating_point():
-            return inpt
-        if inpt.ndim < 3:
-            raise ValueError(f"CoarseDropout expects [..., C, H, W] input, but got shape {inpt.shape}.")
-
-        h, w = inpt.shape[-2:]
-        result = inpt.clone()
-        for (height_frac, width_frac), (y_frac, x_frac) in zip(
-            params["sizes"], params["positions"], strict=True
-        ):
-            hole_h = max(1, min(h, round(height_frac * h)))
-            hole_w = max(1, min(w, round(width_frac * w)))
-            y = round(y_frac * (h - hole_h))
-            x = round(x_frac * (w - hole_w))
-            result[..., y : y + hole_h, x : x + hole_w] = self.fill_value
-        return result
-
-
-class GammaCorrection(Transform):
-    """Apply random gamma correction to simulate exposure variation.
-
-    Models different camera auto-exposure settings and sensor response curves.
-    Uses log-symmetric sampling so brightening and darkening are equally likely,
-    preventing BatchNorm statistics shift.
-
-    Args:
-        gamma: Range (min, max) for gamma value. Values < 1 brighten, > 1 darken.
-    """
-
-    def __init__(self, gamma: float | Sequence[float] = (0.5, 2.0)) -> None:
-        super().__init__()
-        if isinstance(gamma, (int, float)):
-            gamma = float(gamma)
-            if gamma <= 0:
-                raise ValueError(f"gamma must be positive, but got {gamma}.")
-            self.gamma = (min(gamma, 1.0 / gamma), max(gamma, 1.0 / gamma))
-        elif isinstance(gamma, Sequence) and len(gamma) == 2:
-            self.gamma = (float(gamma[0]), float(gamma[1]))
-        else:
-            raise TypeError("gamma must be a number or a sequence with length 2.")
-        if not 0.0 < self.gamma[0] <= self.gamma[1]:
-            raise ValueError(f"gamma must satisfy 0 < min <= max, but got {self.gamma}.")
-
-    def make_params(self, flat_inputs: list[Any]) -> dict[str, Any]:
-        log_lo = math.log(self.gamma[0])
-        log_hi = math.log(self.gamma[1])
-        gamma = math.exp(torch.empty(1).uniform_(log_lo, log_hi).item())
-        return {"gamma": gamma}
-
-    def transform(self, inpt: Any, params: dict[str, Any]) -> Any:
-        if isinstance(inpt, torch.Tensor) and inpt.is_floating_point():
-            return inpt.pow(params["gamma"]).clamp(0.0, 1.0)
-        return inpt
-
-
-# From the paper authors' MIT-licensed reference implementation:
-# https://github.com/TheZino/PlanckianJitter
-_PLANCKIAN_BLACKBODY_COEFFICIENTS = (
-    (0.6743, 0.4029, 0.0013),
-    (0.6281, 0.4241, 0.1665),
-    (0.5919, 0.4372, 0.2513),
-    (0.5623, 0.4457, 0.3154),
-    (0.5376, 0.4515, 0.3672),
-    (0.5163, 0.4555, 0.4103),
-    (0.4979, 0.4584, 0.4468),
-    (0.4816, 0.4604, 0.4782),
-    (0.4672, 0.4619, 0.5053),
-    (0.4542, 0.4630, 0.5289),
-    (0.4426, 0.4638, 0.5497),
-    (0.4320, 0.4644, 0.5681),
-    (0.4223, 0.4648, 0.5844),
-    (0.4135, 0.4651, 0.5990),
-    (0.4054, 0.4653, 0.6121),
-    (0.3980, 0.4654, 0.6239),
-    (0.3911, 0.4655, 0.6346),
-    (0.3847, 0.4656, 0.6444),
-    (0.3787, 0.4656, 0.6532),
-    (0.3732, 0.4656, 0.6613),
-    (0.3680, 0.4655, 0.6688),
-    (0.3632, 0.4655, 0.6756),
-    (0.3586, 0.4655, 0.6820),
-    (0.3544, 0.4654, 0.6878),
-    (0.3503, 0.4653, 0.6933),
-)
-_PLANCKIAN_MIN_TEMPERATURE = 3_000
-_PLANCKIAN_MAX_TEMPERATURE = 15_000
-_PLANCKIAN_TEMPERATURE_STEP = 500
-
-
-class PlanckianJitter(Transform):
-    """Simulate color temperature shift along the Planckian locus.
-
-    Samples one black-body temperature and applies the corresponding correlated red
-    and blue channel scaling while preserving the green channel. Coefficients between
-    the tabulated 500 K intervals are linearly interpolated.
-
-    Reference: Zini et al., "Planckian Jitter", CVPR 2022 Workshop.
-
-    Args:
-        temperature: A fixed color temperature or range in Kelvin. Supported values
-            are between 3000 K and 15000 K.
-    """
-
-    def __init__(self, temperature: int | Sequence[int] = (3_000, 15_000)) -> None:
-        super().__init__()
-        if isinstance(temperature, int):
-            self.temperature = (temperature, temperature)
-        elif isinstance(temperature, Sequence) and len(temperature) == 2:
-            self.temperature = (int(temperature[0]), int(temperature[1]))
-        else:
-            raise TypeError("temperature must be an int or a sequence with length 2.")
-        if not (
-            _PLANCKIAN_MIN_TEMPERATURE
-            <= self.temperature[0]
-            <= self.temperature[1]
-            <= _PLANCKIAN_MAX_TEMPERATURE
-        ):
-            raise ValueError(
-                "temperature must satisfy "
-                f"{_PLANCKIAN_MIN_TEMPERATURE} <= min <= max <= {_PLANCKIAN_MAX_TEMPERATURE}, "
-                f"but got {self.temperature}."
-            )
-
-    def make_params(self, flat_inputs: list[Any]) -> dict[str, Any]:
-        temperature = int(torch.randint(self.temperature[0], self.temperature[1] + 1, ()).item())
-        return {"temperature": temperature}
-
-    def transform(self, inpt: Any, params: dict[str, Any]) -> Any:
-        if not isinstance(inpt, torch.Tensor) or not inpt.is_floating_point():
-            return inpt
-        if inpt.ndim < 3 or inpt.shape[-3] != 3:
-            raise ValueError(f"PlanckianJitter expects [..., 3, H, W] input, but got shape {inpt.shape}.")
-
-        table_position = (params["temperature"] - _PLANCKIAN_MIN_TEMPERATURE) / _PLANCKIAN_TEMPERATURE_STEP
-        left_index = math.floor(table_position)
-        right_index = min(left_index + 1, len(_PLANCKIAN_BLACKBODY_COEFFICIENTS) - 1)
-        interpolation_weight = table_position - left_index
-
-        left = torch.tensor(
-            _PLANCKIAN_BLACKBODY_COEFFICIENTS[left_index],
-            device=inpt.device,
-            dtype=inpt.dtype,
-        )
-        right = torch.tensor(
-            _PLANCKIAN_BLACKBODY_COEFFICIENTS[right_index],
-            device=inpt.device,
-            dtype=inpt.dtype,
-        )
-        coefficients = torch.lerp(left, right, interpolation_weight)
-        scale = torch.stack(
-            (
-                coefficients[0] / coefficients[1],
-                coefficients.new_tensor(1.0),
-                coefficients[2] / coefficients[1],
-            )
-        )
-        broadcast_shape = (1,) * (inpt.ndim - 3) + (3, 1, 1)
-        return (inpt * scale.reshape(broadcast_shape)).clamp(0.0, 1.0)
-
-
-_CUSTOM_TRANSFORMS: dict[str, type[Transform]] = {
-    "SharpnessJitter": SharpnessJitter,
-    "GaussianNoise": GaussianNoise,
-    "MotionBlur": MotionBlur,
-    "JPEGCompression": JPEGCompression,
-    "GaussianPatchBrightness": GaussianPatchBrightness,
-    "RandomShadow": RandomShadow,
-    "CoarseDropout": CoarseDropout,
-    "GammaCorrection": GammaCorrection,
-    "PlanckianJitter": PlanckianJitter,
-}
-
-
@dataclass
 class ImageTransformConfig:
    """
@@ -682,18 +215,17 @@ class ImageTransformsConfig:
    )


-def make_transform_from_config(cfg: ImageTransformConfig) -> Transform:
-    if cfg.type in _CUSTOM_TRANSFORMS:
-        return _CUSTOM_TRANSFORMS[cfg.type](**cfg.kwargs)
+def make_transform_from_config(cfg: ImageTransformConfig):
+    if cfg.type == "SharpnessJitter":
+        return SharpnessJitter(**cfg.kwargs)

    transform_cls = getattr(v2, cfg.type, None)
    if isinstance(transform_cls, type) and issubclass(transform_cls, Transform):
        return transform_cls(**cfg.kwargs)

-    valid_custom = ", ".join(sorted(_CUSTOM_TRANSFORMS.keys()))
    raise ValueError(
        f"Transform '{cfg.type}' is not valid. It must be a class in "
-        f"torchvision.transforms.v2 or one of: {valid_custom}."
+        f"torchvision.transforms.v2 or 'SharpnessJitter'."
    )


@@ -704,8 +236,8 @@ class ImageTransforms(Transform):
        super().__init__()
        self._cfg = cfg

-        self.weights: list[float] = []
-        self.transforms: dict[str, Transform] = {}
+        self.weights = []
+        self.transforms = {}
        for tf_name, tf_cfg in cfg.tfs.items():
            if tf_cfg.weight <= 0.0:
                continue
@@ -16,39 +16,11 @@
 # limitations under the License.

 import logging
-import multiprocessing
 import os
 import signal
 import sys


-def ensure_multiprocessing_start_method(start_method: str | None) -> None:
-    """Set a multiprocessing start method once, or verify the existing method matches.
-
-    Passing ``None`` leaves Python's process-wide default untouched. This is useful
-    when LeRobot is embedded in an application that owns multiprocessing setup.
-    """
-    if start_method is None:
-        return
-
-    available_methods = multiprocessing.get_all_start_methods()
-    if start_method not in available_methods:
-        raise ValueError(
-            f"Multiprocessing start method must be one of {available_methods} on this platform, "
-            f"got {start_method!r}."
-        )
-
-    current_method = multiprocessing.get_start_method(allow_none=True)
-    if current_method is None:
-        multiprocessing.set_start_method(start_method)
-    elif current_method != start_method:
-        raise RuntimeError(
-            f"Multiprocessing start method is already {current_method!r}; cannot change it to "
-            f"{start_method!r}. Set the configured multiprocessing context to null to keep the "
-            "application's existing method, or launch LeRobot in a fresh process."
-        )
-
-
 class ProcessSignalHandler:
    """Utility class to attach graceful shutdown signal handlers.

@@ -133,13 +133,10 @@ def say(text: str, blocking: bool = False):
    else:
        raise RuntimeError("Unsupported operating system for text-to-speech.")

-    try:
-        if blocking:
-            subprocess.run(cmd, check=True, timeout=5)
-        else:
-            subprocess.Popen(cmd, creationflags=subprocess.CREATE_NO_WINDOW if system == "Windows" else 0)
-    except (FileNotFoundError, subprocess.TimeoutExpired) as e:
-        logging.warning("Text-to-speech command failed: %s | Error: %s", cmd, e)
+    if blocking:
+        subprocess.run(cmd, check=True)
+    else:
+        subprocess.Popen(cmd, creationflags=subprocess.CREATE_NO_WINDOW if system == "Windows" else 0)


 def log_say(text: str, play_sounds: bool = True, blocking: bool = False):
@@ -20,7 +20,7 @@
 # ```

 from pathlib import Path
-from unittest.mock import MagicMock, patch
+from unittest.mock import patch

 import cv2
 import numpy as np
@@ -123,73 +123,6 @@ def test_invalid_width_connect():
        camera.connect(warmup=False)


-def test_connect_cleans_up_after_settings_failure_and_allows_retry():
-    config = OpenCVCameraConfig(index_or_path=DEFAULT_PNG_FILE_PATH, warmup_s=0)
-    camera = OpenCVCamera(config)
-    opened_captures = []
-
-    def fail_settings():
-        opened_captures.append(camera.videocapture)
-        raise RuntimeError("settings failed")
-
-    with (
-        patch.object(camera, "_configure_capture_settings", side_effect=fail_settings),
-        pytest.raises(RuntimeError, match="settings failed"),
-    ):
-        camera.connect(warmup=False)
-
-    assert camera.videocapture is None
-    assert camera.thread is None
-    assert not camera.is_connected
-    assert opened_captures[0] is not None
-    assert not opened_captures[0].isOpened()
-
-    camera.connect(warmup=False)
-    assert camera.is_connected
-    camera.disconnect()
-
-
-def test_connect_cleans_up_after_warmup_failure_and_allows_retry():
-    config = OpenCVCameraConfig(index_or_path=DEFAULT_PNG_FILE_PATH, warmup_s=1)
-    camera = OpenCVCamera(config)
-    read_threads = []
-
-    def fail_warmup(*_args, **_kwargs):
-        read_threads.append(camera.thread)
-        raise TimeoutError("no frame")
-
-    with (
-        patch.object(camera, "async_read", side_effect=fail_warmup),
-        pytest.raises(TimeoutError, match="no frame"),
-    ):
-        camera.connect()
-
-    assert camera.videocapture is None
-    assert camera.thread is None
-    assert not camera.is_connected
-    assert read_threads[0] is not None
-    assert not read_threads[0].is_alive()
-
-    camera.connect(warmup=False)
-    assert camera.is_connected
-    camera.disconnect()
-
-
-def test_find_cameras_releases_unopened_handles():
-    module_path = OpenCVCamera.__module__
-    unopened_capture = MagicMock()
-    unopened_capture.isOpened.return_value = False
-
-    with (
-        patch(f"{module_path}.platform.system", return_value="Darwin"),
-        patch(f"{module_path}.MAX_OPENCV_INDEX", 1),
-        patch(f"{module_path}.cv2.VideoCapture", return_value=unopened_capture),
-    ):
-        assert OpenCVCamera.find_cameras() == []
-
-    unopened_capture.release.assert_called_once_with()
-
-
@pytest.mark.parametrize("index_or_path", TEST_IMAGE_PATHS, ids=TEST_IMAGE_SIZES)
 def test_read(index_or_path):
    config = OpenCVCameraConfig(index_or_path=index_or_path, warmup_s=0)
@@ -20,7 +20,7 @@
 # ```

 from pathlib import Path
-from unittest.mock import MagicMock, patch
+from unittest.mock import patch

 import numpy as np
 import pytest
@@ -30,8 +30,6 @@ from lerobot.utils.errors import DeviceAlreadyConnectedError, DeviceNotConnected

 pytest.importorskip("pyrealsense2")

-import pyrealsense2 as rs
-
 from lerobot.cameras.realsense import RealSenseCamera, RealSenseCameraConfig

 TEST_ARTIFACTS_DIR = Path(__file__).parent.parent / "artifacts" / "cameras"
@@ -63,17 +61,6 @@ def test_abc_implementation():
    _ = RealSenseCamera(config)


-@pytest.mark.parametrize("option", ["exposure", "gain", "white_balance"])
-def test_manual_color_option_requires_rgb(option):
-    with pytest.raises(ValueError, match="use_rgb=True"):
-        RealSenseCameraConfig(
-            serial_number_or_name="042",
-            use_rgb=False,
-            use_depth=True,
-            **{option: 100},
-        )
-
-
 def test_connect():
    config = RealSenseCameraConfig(serial_number_or_name="042", warmup_s=0)

@@ -96,27 +83,6 @@ def test_connect_invalid_camera_path(patch_realsense):
        camera.connect(warmup=False)


-def test_connect_cleans_up_when_sensor_configuration_fails():
-    config = RealSenseCameraConfig(serial_number_or_name="042", exposure=120)
-    camera = RealSenseCamera(config)
-    pipeline = MagicMock()
-    pipeline.start.return_value = MagicMock()
-
-    with (
-        patch("lerobot.cameras.realsense.camera_realsense.rs.pipeline", return_value=pipeline),
-        patch.object(camera, "_configure_rs_pipeline_config"),
-        patch.object(camera, "_configure_capture_settings"),
-        patch.object(camera, "_configure_sensor_options", side_effect=ValueError("invalid exposure")),
-        pytest.raises(ValueError, match="invalid exposure"),
-    ):
-        camera.connect(warmup=False)
-
-    pipeline.stop.assert_called_once_with()
-    assert camera.rs_pipeline is None
-    assert camera.rs_profile is None
-    assert not camera.is_connected
-
-
 def test_invalid_width_connect():
    config = RealSenseCameraConfig(serial_number_or_name="042", width=99999, height=480, fps=30)
    camera = RealSenseCamera(config)
@@ -125,33 +91,6 @@ def test_invalid_width_connect():
        camera.connect(warmup=False)


-def test_connect_cleans_up_after_warmup_failure_and_allows_retry():
-    config = RealSenseCameraConfig(serial_number_or_name="042", width=640, height=480, fps=30)
-    camera = RealSenseCamera(config)
-    read_threads = []
-
-    def fail_warmup(*_args, **_kwargs):
-        read_threads.append(camera.thread)
-        raise TimeoutError("no frame")
-
-    with (
-        patch.object(camera, "async_read", side_effect=fail_warmup),
-        pytest.raises(TimeoutError, match="no frame"),
-    ):
-        camera.connect()
-
-    assert camera.rs_pipeline is None
-    assert camera.rs_profile is None
-    assert camera.thread is None
-    assert not camera.is_connected
-    assert read_threads[0] is not None
-    assert not read_threads[0].is_alive()
-
-    camera.connect(warmup=False)
-    assert camera.is_connected
-    camera.disconnect()
-
-
 def test_read():
    config = RealSenseCameraConfig(serial_number_or_name="042", width=640, height=480, fps=30, warmup_s=0)
    with RealSenseCamera(config) as camera:
@@ -289,203 +228,6 @@ def test_read_latest_too_old():
            _ = camera.read_latest(max_age_ms=0)  # immediately too old


-def _make_mock_sensor(name: str, supported_options: set | None = None) -> MagicMock:
-    """Build a fake rs.sensor that reports a name and a configurable supported-options set."""
-    supported = supported_options if supported_options is not None else set()
-    sensor = MagicMock()
-    sensor.get_info.return_value = name
-    sensor.supports.side_effect = lambda opt: opt in supported
-    return sensor
-
-
-def _attach_mock_color_sensor(camera: RealSenseCamera, sensor: MagicMock) -> None:
-    """Wire camera.rs_profile so _get_color_sensor finds the given sensor."""
-    profile = MagicMock()
-    device = MagicMock()
-    device.query_sensors.return_value = [sensor]
-    profile.get_device.return_value = device
-    camera.rs_profile = profile
-
-
-def test_get_color_sensor_prefers_rgb_camera():
-    config = RealSenseCameraConfig(serial_number_or_name="042")
-    camera = RealSenseCamera(config)
-
-    rgb = _make_mock_sensor("RGB Camera")
-    stereo = _make_mock_sensor("Stereo Module")
-    profile = MagicMock()
-    device = MagicMock()
-    device.query_sensors.return_value = [stereo, rgb]
-    profile.get_device.return_value = device
-    camera.rs_profile = profile
-
-    assert camera._get_color_sensor() is rgb
-
-
-def test_get_color_sensor_falls_back_to_stereo_module():
-    """D405 has no separate RGB module; color comes from Stereo Module."""
-    config = RealSenseCameraConfig(serial_number_or_name="042")
-    camera = RealSenseCamera(config)
-
-    stereo = _make_mock_sensor("Stereo Module")
-    _attach_mock_color_sensor(camera, stereo)
-
-    assert camera._get_color_sensor() is stereo
-
-
-def test_get_color_sensor_raises_with_available_sensors():
-    config = RealSenseCameraConfig(serial_number_or_name="042")
-    camera = RealSenseCamera(config)
-
-    other = _make_mock_sensor("Motion Module")
-    _attach_mock_color_sensor(camera, other)
-
-    with pytest.raises(RuntimeError, match="Motion Module"):
-        camera._get_color_sensor()
-
-
-def test_configure_sensor_options_skipped_when_none():
-    config = RealSenseCameraConfig(serial_number_or_name="042")
-    camera = RealSenseCamera(config)
-
-    with patch.object(RealSenseCamera, "_get_color_sensor") as mock_get:
-        camera._configure_sensor_options()
-        mock_get.assert_not_called()
-
-
-def test_configure_sensor_options_applies_all_values():
-    config = RealSenseCameraConfig(serial_number_or_name="042", exposure=120, gain=64, white_balance=4600)
-    camera = RealSenseCamera(config)
-
-    sensor = _make_mock_sensor(
-        "RGB Camera",
-        supported_options={
-            rs.option.enable_auto_exposure,
-            rs.option.exposure,
-            rs.option.gain,
-            rs.option.enable_auto_white_balance,
-            rs.option.white_balance,
-        },
-    )
-    _attach_mock_color_sensor(camera, sensor)
-
-    camera._configure_sensor_options()
-
-    sensor.set_option.assert_any_call(rs.option.enable_auto_exposure, 0)
-    sensor.set_option.assert_any_call(rs.option.exposure, 120)
-    sensor.set_option.assert_any_call(rs.option.gain, 64)
-    sensor.set_option.assert_any_call(rs.option.enable_auto_white_balance, 0)
-    sensor.set_option.assert_any_call(rs.option.white_balance, 4600)
-
-
-@pytest.mark.parametrize(
-    ("config_field", "option", "label"),
-    [
-        ("exposure", rs.option.exposure, "exposure"),
-        ("gain", rs.option.gain, "gain"),
-        ("white_balance", rs.option.white_balance, "white balance"),
-    ],
-)
-def test_configure_sensor_options_raises_when_requested_option_is_unsupported(config_field, option, label):
-    config = RealSenseCameraConfig(serial_number_or_name="042", **{config_field: 100})
-    camera = RealSenseCamera(config)
-
-    sensor = _make_mock_sensor("RGB Camera", supported_options=set())
-    _attach_mock_color_sensor(camera, sensor)
-
-    with pytest.raises(ValueError, match=label):
-        camera._configure_sensor_options()
-
-    sensor.supports.assert_any_call(option)
-    sensor.set_option.assert_not_called()
-
-
-@pytest.mark.parametrize(
-    ("config_field", "option", "value"),
-    [
-        ("exposure", rs.option.exposure, 120),
-        ("gain", rs.option.gain, 64),
-    ],
-)
-def test_configure_sensor_options_exposure_or_gain_disables_auto_exposure(config_field, option, value):
-    """white_balance=None should not touch auto white balance."""
-    config = RealSenseCameraConfig(serial_number_or_name="042", **{config_field: value})
-    camera = RealSenseCamera(config)
-
-    sensor = _make_mock_sensor(
-        "RGB Camera",
-        supported_options={rs.option.enable_auto_exposure, option},
-    )
-    _attach_mock_color_sensor(camera, sensor)
-
-    camera._configure_sensor_options()
-
-    calls = [call.args for call in sensor.set_option.call_args_list]
-    assert (rs.option.enable_auto_exposure, 0) in calls
-    assert (option, value) in calls
-    for opt, _ in calls:
-        assert opt != rs.option.enable_auto_white_balance
-        assert opt != rs.option.white_balance
-
-
-def test_configure_sensor_options_warns_when_auto_exposure_control_is_unsupported(caplog):
-    config = RealSenseCameraConfig(serial_number_or_name="042", exposure=120)
-    camera = RealSenseCamera(config)
-
-    sensor = _make_mock_sensor("RGB Camera", supported_options={rs.option.exposure})
-    _attach_mock_color_sensor(camera, sensor)
-
-    with caplog.at_level("WARNING"):
-        camera._configure_sensor_options()
-
-    sensor.set_option.assert_called_once_with(rs.option.exposure, 120)
-    assert "does not support disabling auto-exposure" in caplog.text
-
-
-def test_configure_sensor_options_warns_when_auto_white_balance_control_is_unsupported(caplog):
-    config = RealSenseCameraConfig(serial_number_or_name="042", white_balance=4600)
-    camera = RealSenseCamera(config)
-
-    sensor = _make_mock_sensor("RGB Camera", supported_options={rs.option.white_balance})
-    _attach_mock_color_sensor(camera, sensor)
-
-    with caplog.at_level("WARNING"):
-        camera._configure_sensor_options()
-
-    sensor.set_option.assert_called_once_with(rs.option.white_balance, 4600)
-    assert "does not support disabling auto white balance" in caplog.text
-
-
-def test_configure_sensor_options_out_of_range_raises_value_error():
-    """set_option errors should be re-raised as ValueError with range diagnostics."""
-    config = RealSenseCameraConfig(serial_number_or_name="042", exposure=999999)
-    camera = RealSenseCamera(config)
-
-    sensor = _make_mock_sensor(
-        "RGB Camera",
-        supported_options={rs.option.enable_auto_exposure, rs.option.exposure},
-    )
-
-    def fake_set_option(option, value):
-        if option == rs.option.exposure:
-            raise RuntimeError("value out of range")
-
-    sensor.set_option.side_effect = fake_set_option
-
-    option_range = MagicMock(min=1, max=10000, step=1, default=156)
-    sensor.get_option_range.return_value = option_range
-
-    _attach_mock_color_sensor(camera, sensor)
-
-    with pytest.raises(ValueError, match="exposure") as exc_info:
-        camera._configure_sensor_options()
-
-    msg = str(exc_info.value)
-    assert "999999" in msg
-    assert "min=1" in msg
-    assert "max=10000" in msg
-
-
@pytest.mark.parametrize(
    "rotation",
    [
@@ -1,104 +0,0 @@
-# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import numpy as np
-import pytest
-
-pytest.importorskip("datasets", reason="datasets is required (install lerobot[dataset])")
-
-from lerobot.scripts.augment_dataset_quantile_stats import (
-    compute_quantile_stats_for_dataset,
-    has_quantile_stats,
-)
-
-
-def _numeric_keys(dataset):
-    return [k for k, v in dataset.features.items() if v["dtype"] not in ("image", "video", "string")]
-
-
-def _image_keys(dataset):
-    return [k for k, v in dataset.features.items() if v["dtype"] in ("image", "video")]
-
-
-def test_numeric_stats_are_unaffected_by_sampling(tmp_path, lerobot_dataset_factory):
-    """Sampling only touches image/video frames; numeric features are read in
-    full either way, so their stats must be identical with and without sampling."""
-    dataset = lerobot_dataset_factory(
-        root=tmp_path / "ds", total_episodes=2, total_frames=400, use_videos=False
-    )
-
-    exact = compute_quantile_stats_for_dataset(dataset, use_sampling=False)
-    sampled = compute_quantile_stats_for_dataset(dataset, use_sampling=True)
-
-    numeric_keys = _numeric_keys(dataset)
-    assert numeric_keys, "fixture should expose numeric features"
-    for key in numeric_keys:
-        if key not in exact:
-            continue
-        for stat in ("mean", "std", "q01", "q50", "q99"):
-            if stat in exact[key]:
-                np.testing.assert_allclose(
-                    sampled[key][stat],
-                    exact[key][stat],
-                    rtol=1e-6,
-                    atol=1e-6,
-                    err_msg=f"numeric feature '{key}' stat '{stat}' changed under sampling",
-                )
-
-
-def test_image_sampling_reduces_data_but_keeps_stats_close(tmp_path, lerobot_dataset_factory):
-    """For images, sampling should reduce the number of samples considered while
-    keeping the resulting statistics close to the exact ones."""
-    dataset = lerobot_dataset_factory(
-        root=tmp_path / "ds", total_episodes=2, total_frames=400, use_videos=False
-    )
-
-    exact = compute_quantile_stats_for_dataset(dataset, use_sampling=False)
-    sampled = compute_quantile_stats_for_dataset(dataset, use_sampling=True)
-
-    image_keys = _image_keys(dataset)
-    assert image_keys, "fixture should expose at least one image feature"
-    for key in image_keys:
-        # sampling actually looked at fewer pixels
-        assert sampled[key]["count"][0] < exact[key]["count"][0]
-        # but per-channel mean stays close
-        np.testing.assert_allclose(
-            sampled[key]["mean"],
-            exact[key]["mean"],
-            rtol=0.15,
-            err_msg=f"image feature '{key}' mean drifted too far under sampling",
-        )
-
-
-def test_short_episodes_use_all_frames(tmp_path, lerobot_dataset_factory):
-    """With episodes shorter than the sampling floor, sampling is a no-op and
-    must produce exactly the same stats as the exact path."""
-    dataset = lerobot_dataset_factory(
-        root=tmp_path / "ds", total_episodes=2, total_frames=40, use_videos=False
-    )
-
-    exact = compute_quantile_stats_for_dataset(dataset, use_sampling=False)
-    sampled = compute_quantile_stats_for_dataset(dataset, use_sampling=True)
-
-    for key in _image_keys(dataset):
-        assert sampled[key]["count"][0] == exact[key]["count"][0]
-
-
-def test_quantile_stats_present_after_compute(tmp_path, lerobot_dataset_factory):
-    """The computed stats should contain quantile keys for the dataset."""
-    dataset = lerobot_dataset_factory(
-        root=tmp_path / "ds", total_episodes=2, total_frames=200, use_videos=False
-    )
-    stats = compute_quantile_stats_for_dataset(dataset, use_sampling=True)
-    assert has_quantile_stats(stats)
@@ -28,17 +28,9 @@ from lerobot.scripts.lerobot_imgtransform_viz import (
    save_each_transform,
 )
 from lerobot.transforms import (
-    CoarseDropout,
-    GammaCorrection,
-    GaussianNoise,
-    GaussianPatchBrightness,
    ImageTransformConfig,
    ImageTransforms,
    ImageTransformsConfig,
-    JPEGCompression,
-    MotionBlur,
-    PlanckianJitter,
-    RandomShadow,
    RandomSubsetApply,
    SharpnessJitter,
    make_transform_from_config,
@@ -463,153 +455,3 @@ def test_save_each_transform(img_tensor_factory, tmp_path):
            assert (transform_dir / file_name).exists(), (
                f"{file_name} was not found in {transform} directory."
            )
-
-
-# --- Tests for robotics-relevant augmentations ---
-
-ROBOTICS_TRANSFORMS = [
-    ("GaussianNoise", GaussianNoise, {"std": (5.0, 25.0)}),
-    ("MotionBlur", MotionBlur, {"kernel_size": (3, 11)}),
-    ("JPEGCompression", JPEGCompression, {"quality": (15, 75)}),
-    ("GaussianPatchBrightness", GaussianPatchBrightness, {}),
-    ("RandomShadow", RandomShadow, {"opacity": (0.3, 0.6)}),
-    ("CoarseDropout", CoarseDropout, {"max_holes": 8}),
-    ("GammaCorrection", GammaCorrection, {"gamma": (0.5, 2.0)}),
-    ("PlanckianJitter", PlanckianJitter, {"temperature": (3_000, 15_000)}),
-]
-
-
-@pytest.mark.parametrize("name,cls,kwargs", ROBOTICS_TRANSFORMS, ids=[t[0] for t in ROBOTICS_TRANSFORMS])
-def test_robotics_transform_shape_preserved(name, cls, kwargs, img_tensor_factory):
-    img = img_tensor_factory()
-    tf = cls(**kwargs)
-    out = tf(img)
-    assert out.shape == img.shape, f"{name} changed shape: {img.shape} -> {out.shape}"
-
-
-@pytest.mark.parametrize("name,cls,kwargs", ROBOTICS_TRANSFORMS, ids=[t[0] for t in ROBOTICS_TRANSFORMS])
-def test_robotics_transform_output_range(name, cls, kwargs, img_tensor_factory):
-    img = img_tensor_factory()
-    tf = cls(**kwargs)
-    out = tf(img)
-    assert out.min() >= -0.01, f"{name} min below range: {out.min():.4f}"
-    assert out.max() <= 1.01, f"{name} max above range: {out.max():.4f}"
-
-
-@pytest.mark.parametrize("name,cls,kwargs", ROBOTICS_TRANSFORMS, ids=[t[0] for t in ROBOTICS_TRANSFORMS])
-def test_robotics_transform_float_output(name, cls, kwargs, img_tensor_factory):
-    img = img_tensor_factory()
-    tf = cls(**kwargs)
-    out = tf(img)
-    assert out.is_floating_point(), f"{name} output dtype={out.dtype}"
-
-
-@pytest.mark.parametrize("name,cls,kwargs", ROBOTICS_TRANSFORMS, ids=[t[0] for t in ROBOTICS_TRANSFORMS])
-def test_robotics_transform_non_float_passthrough(name, cls, kwargs):
-    int_img = torch.randint(0, 255, (3, 32, 32), dtype=torch.uint8)
-    tf = cls(**kwargs)
-    out = tf(int_img)
-    assert torch.equal(out, int_img), f"{name} modified non-float input"
-
-
-@pytest.mark.parametrize("name,cls,kwargs", ROBOTICS_TRANSFORMS, ids=[t[0] for t in ROBOTICS_TRANSFORMS])
-def test_robotics_transform_via_config(name, cls, kwargs):
-    cfg = ImageTransformConfig(type=name, kwargs=kwargs)
-    tf = make_transform_from_config(cfg)
-    assert isinstance(tf, cls), f"Config produced {type(tf)}, expected {cls}"
-
-
-def test_make_transform_error_message_includes_custom():
-    """Error message should list all registered custom transforms."""
-    with pytest.raises(ValueError, match="GaussianNoise"):
-        make_transform_from_config(ImageTransformConfig(type="NonExistent"))
-
-
-@pytest.mark.parametrize("name,cls,kwargs", ROBOTICS_TRANSFORMS, ids=[t[0] for t in ROBOTICS_TRANSFORMS])
-@pytest.mark.parametrize("shape", [(4, 3, 32, 32), (2, 4, 3, 16, 16)])
-def test_robotics_transform_supports_temporal_batches(name, cls, kwargs, shape):
-    img = torch.rand(shape)
-    out = cls(**kwargs)(img)
-    assert out.shape == img.shape, f"{name} changed shape: {img.shape} -> {out.shape}"
-    assert out.min() >= 0
-    assert out.max() <= 1
-
-
-@pytest.mark.parametrize(
-    "cls,kwargs",
-    [
-        (GaussianNoise, {"std": (25.0, 25.0)}),
-        (MotionBlur, {"kernel_size": 5}),
-        (JPEGCompression, {"quality": 10}),
-        (
-            GaussianPatchBrightness,
-            {"num_patches": 1, "sigma_range": (0.2, 0.2), "factor_range": (0.5, 0.5)},
-        ),
-        (RandomShadow, {"opacity": 0.5}),
-        (CoarseDropout, {"max_holes": 1, "fill_value": 0.0}),
-        (GammaCorrection, {"gamma": (2.0, 2.0)}),
-        (PlanckianJitter, {"temperature": 3_000}),
-    ],
-)
-def test_robotics_transform_is_not_silent_noop(cls, kwargs):
-    img = torch.rand(3, 32, 32)
-    out = cls(**kwargs)(img)
-    assert not torch.equal(out, img)
-
-
-@pytest.mark.parametrize(
-    "transform",
-    [
-        GaussianNoise(std=25),
-        RandomShadow(opacity=0.5),
-        CoarseDropout(max_holes=4),
-    ],
-)
-def test_robotics_transform_random_params_are_reused(transform):
-    img = torch.rand(3, 32, 32)
-    params = transform.make_params([img])
-    torch.testing.assert_close(transform.transform(img, params), transform.transform(img, params))
-
-
-def test_motion_blur_kernel_size_stays_in_configured_range():
-    transform = MotionBlur(kernel_size=(4, 10))
-    sampled_sizes = {transform.make_params([])["kernel_size"] for _ in range(100)}
-    assert sampled_sizes <= {5, 7, 9}
-    assert sampled_sizes
-
-
-def test_gamma_correction_scalar_below_one_defines_symmetric_range():
-    transform = GammaCorrection(gamma=0.5)
-    assert transform.gamma == (0.5, 2.0)
-    assert transform(torch.rand(3, 8, 8)).shape == (3, 8, 8)
-
-
-def test_planckian_jitter_uses_correlated_temperature_coefficients():
-    img = torch.full((2, 3, 8, 8), 0.25)
-    out = PlanckianJitter(temperature=3_000)(img)
-    torch.testing.assert_close(out[:, 1], img[:, 1])
-    assert torch.all(out[:, 0] > out[:, 1])
-    assert torch.all(out[:, 2] < out[:, 1])
-
-
-def test_random_shadow_supports_small_images():
-    img = torch.rand(3, 7, 7)
-    assert RandomShadow()(img).shape == img.shape
-
-
-@pytest.mark.parametrize(
-    "cls,kwargs",
-    [
-        (GaussianNoise, {"std": (-1.0, 1.0)}),
-        (MotionBlur, {"kernel_size": 4}),
-        (JPEGCompression, {"quality": (0, 75)}),
-        (GaussianPatchBrightness, {"sigma_range": (0.0, 0.25)}),
-        (RandomShadow, {"opacity": (0.3, 1.1)}),
-        (CoarseDropout, {"max_holes": 0}),
-        (GammaCorrection, {"gamma": 0.0}),
-        (PlanckianJitter, {"temperature": (2_000, 6_500)}),
-    ],
-)
-def test_robotics_transform_rejects_invalid_config(cls, kwargs):
-    with pytest.raises(ValueError):
-        cls(**kwargs)
@@ -482,20 +482,6 @@ def test_add_frame_works_in_write_mode(tmp_path):
 # ── Resume mode ──────────────────────────────────────────────────────


-def test_resume_freshly_created_empty_dataset(tmp_path):
-    """resume() accepts a local dataset created before any episode was recorded."""
-    root = tmp_path / "resume_empty_ds"
-    LeRobotDataset.create(repo_id=DUMMY_REPO_ID, fps=DEFAULT_FPS, features=SIMPLE_FEATURES, root=root)
-
-    resumed = LeRobotDataset.resume(repo_id=DUMMY_REPO_ID, root=root)
-
-    assert isinstance(resumed.writer, DatasetWriter)
-    assert resumed.meta.total_episodes == 0
-    assert resumed.meta.total_frames == 0
-    assert resumed.meta.tasks is None
-    assert resumed.meta.episodes is None
-
-
 def test_resume_creates_writer(tmp_path):
    """After resume(), writer is a DatasetWriter."""
    root = tmp_path / "resume_ds"
@@ -294,19 +294,6 @@ def test__sync_read(addr, length, ids_values, mock_motors, dummy_motors):
    assert read_values == ids_values


-def test__sync_read_retries_after_transient_failure(mock_motors, dummy_motors):
-    addr, length, ids_values = (10, 4, {1: 1337})
-    stub = mock_motors.build_sync_read_stub(addr, length, ids_values, num_invalid_try=1)
-    bus = FeetechMotorsBus(port=mock_motors.port, motors=dummy_motors)
-    bus.connect(handshake=False)
-
-    read_values, read_comm = bus._sync_read(addr, length, list(ids_values), num_retry=1)
-
-    assert read_comm == scs.COMM_SUCCESS
-    assert read_values == ids_values
-    assert mock_motors.stubs[stub].calls == 2
-
-
@pytest.mark.parametrize("raise_on_error", (True, False))
 def test__sync_read_comm(raise_on_error, mock_motors, dummy_motors):
    addr, length, ids_values = (10, 4, {1: 1337})
@@ -1,83 +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.
-
-from types import SimpleNamespace
-from unittest.mock import MagicMock
-
-import torch
-
-import lerobot.policies.factory as policy_factory
-
-
-def test_make_policy_keeps_peft_adapter_and_base_revisions_separate(monkeypatch):
-    cfg = SimpleNamespace(
-        type="mock",
-        device="cpu",
-        pretrained_path="user/adapter",
-        pretrained_revision="adapter-sha",
-        use_peft=True,
-        input_features={},
-        output_features={},
-    )
-    dataset_meta = SimpleNamespace(features={}, stats={})
-
-    base_policy = torch.nn.Linear(1, 1)
-    policy_from_pretrained = MagicMock(return_value=base_policy)
-    policy_class = SimpleNamespace(from_pretrained=policy_from_pretrained)
-    monkeypatch.setattr(policy_factory, "get_policy_class", lambda _: policy_class)
-    monkeypatch.setattr(policy_factory, "dataset_to_policy_features", lambda _: {})
-    monkeypatch.setattr(policy_factory, "validate_visual_features_consistency", lambda *args: None)
-
-    peft_config = SimpleNamespace(
-        base_model_name_or_path="user/base-policy",
-        revision="base-sha",
-    )
-    peft_config_from_pretrained = MagicMock(return_value=peft_config)
-    adapted_policy = torch.nn.Linear(1, 1)
-    peft_model_from_pretrained = MagicMock(return_value=adapted_policy)
-    require_package = MagicMock()
-    monkeypatch.setattr(policy_factory, "require_package", require_package)
-    monkeypatch.setattr(
-        policy_factory,
-        "PeftConfig",
-        SimpleNamespace(from_pretrained=peft_config_from_pretrained),
-    )
-    monkeypatch.setattr(
-        policy_factory,
-        "PeftModel",
-        SimpleNamespace(from_pretrained=peft_model_from_pretrained),
-    )
-
-    policy = policy_factory.make_policy(cfg, ds_meta=dataset_meta)
-
-    assert policy is adapted_policy
-    require_package.assert_called_once_with("peft", extra="peft")
-    peft_config_from_pretrained.assert_called_once_with(
-        "user/adapter",
-        revision="adapter-sha",
-    )
-    policy_from_pretrained.assert_called_once_with(
-        config=cfg,
-        dataset_stats=dataset_meta.stats,
-        dataset_meta=dataset_meta,
-        pretrained_name_or_path="user/base-policy",
-        revision="base-sha",
-    )
-    peft_model_from_pretrained.assert_called_once_with(
-        base_policy,
-        "user/adapter",
-        config=peft_config,
-        revision="adapter-sha",
-        is_trainable=True,
-    )
@@ -113,7 +113,6 @@ def test_gaussian_actor_config_default_initialization():
    # Concurrency configuration
    assert config.concurrency.actor == "threads"
    assert config.concurrency.learner == "threads"
-    assert config.concurrency.multiprocessing_context == "spawn"

    assert isinstance(config.actor_network_kwargs, ActorNetworkConfig)
    assert isinstance(config.policy_kwargs, PolicyConfig)
@@ -153,7 +152,6 @@ def test_concurrency_config():
    config = ConcurrencyConfig()
    assert config.actor == "threads"
    assert config.learner == "threads"
-    assert config.multiprocessing_context == "spawn"


 def test_gaussian_actor_config_custom_initialization():
@@ -49,7 +49,7 @@ def _make_bus_mock() -> MagicMock:


@pytest.fixture
-def follower(tmp_path):
+def follower():
    bus_mock = _make_bus_mock()

    def _bus_side_effect(*_args, **kwargs):
@@ -71,7 +71,7 @@ def follower(tmp_path):
        ),
        patch.object(SO100Follower, "configure", lambda self: None),
    ):
-        cfg = SO100FollowerConfig(port="/dev/null", calibration_dir=tmp_path)
+        cfg = SO100FollowerConfig(port="/dev/null")
        robot = SO100Follower(cfg)
        yield robot
        if robot.is_connected:
@@ -99,27 +99,6 @@ def test_get_observation(follower):
        assert obs[f"{motor}.pos"] == idx


-def test_get_observation_uses_read_retries(follower):
-    # Feetech buses can intermittently fail a sync_read; the follower should forward the configured
-    # retry count so transient failures don't abort the control loop (see #3131).
-    follower.config.num_read_retries = 7
-    follower.connect()
-    follower.get_observation()
-
-    follower.bus.sync_read.assert_called_once_with("Present_Position", num_retry=7)
-
-
-def test_send_action_uses_read_retries(follower):
-    follower.config.max_relative_target = 10.0
-    follower.config.num_read_retries = 7
-    follower.connect()
-
-    action = {f"{motor}.pos": value * 10 for value, motor in enumerate(follower.bus.motors, 1)}
-    follower.send_action(action)
-
-    follower.bus.sync_read.assert_called_once_with("Present_Position", num_retry=7)
-
-
 def test_send_action(follower):
    follower.connect()

@@ -185,25 +185,18 @@ def test_load_pretrained_peft_policy_keeps_adapter_and_base_revisions_separate(m
    peft_config_from_pretrained = MagicMock(return_value=peft_config)
    adapted_policy = MagicMock()
    peft_model_from_pretrained = MagicMock(return_value=adapted_policy)
-    require_package = MagicMock()
-    monkeypatch.setattr(rollout_context, "require_package", require_package)
-    monkeypatch.setattr(
-        rollout_context,
-        "PeftConfig",
-        SimpleNamespace(from_pretrained=peft_config_from_pretrained),
-        raising=False,
-    )
-    monkeypatch.setattr(
-        rollout_context,
-        "PeftModel",
-        SimpleNamespace(from_pretrained=peft_model_from_pretrained),
-        raising=False,
+    monkeypatch.setitem(
+        sys.modules,
+        "peft",
+        SimpleNamespace(
+            PeftConfig=SimpleNamespace(from_pretrained=peft_config_from_pretrained),
+            PeftModel=SimpleNamespace(from_pretrained=peft_model_from_pretrained),
+        ),
    )

    policy = rollout_context._load_pretrained_policy(policy_config)

    assert policy is adapted_policy
-    require_package.assert_called_once_with("peft", extra="peft")
    peft_config_from_pretrained.assert_called_once_with("user/adapter", revision="adapter-sha")
    policy_class.from_pretrained.assert_called_once_with(
        pretrained_name_or_path="user/base-policy",
Author	SHA1	Message	Date
Nikodem Bartnik	68d6335d5b	fix old docs and comments	2026-07-28 14:31:30 +02:00
Nikodem Bartnik	258d521a89	add docs audit	2026-07-28 14:31:07 +02:00