fix old docs and comments

add docs audit
feat(config): add multiprocessing option to DataLoader context and sets spawn as default (#4139 )
2026-07-29 04:36:04 +00:00 · 2026-07-28 14:31:30 +02:00 · 2026-07-28 14:31:07 +02:00 · 2026-07-28 00:42:55 +02:00 · 2026-07-27 19:29:44 +02:00 · 2026-07-27 18:42:17 +02:00
82 changed files with 2805 additions and 1472 deletions
@@ -0,0 +1,11 @@
+version: 2
+updates:
+  - package-ecosystem: "github-actions"
+    directory: "/"
+    schedule:
+      interval: "weekly"
+    cooldown:
+      default-days: 7
+    groups:
+      actions:
+        patterns: ["*"]
@@ -51,6 +51,7 @@ pre-commit run --all-files                           # Lint + format (ruff, typo
 ## Notes

 - **Mypy is gradual**: strict only for `lerobot.envs`, `lerobot.configs`, `lerobot.optim`, `lerobot.model`, `lerobot.cameras`, `lerobot.motors`, `lerobot.transport`. Add type annotations when modifying these modules.
- **Optional dependencies**: many policies, envs, and robots are behind extras (e.g., `lerobot[aloha]`). New imports for optional packages must be guarded or lazy. See `pyproject.toml [project.optional-dependencies]`.
+- **Imports**: prefer top-level imports; relative (`from .sibling import X`) across sibling files within a module, absolute (`from lerobot.module import X`) across modules.
+- **Optional dependencies**: many policies, envs, and robots are behind extras (e.g., `lerobot[aloha]`, see `pyproject.toml`). Guard optional imports with `TYPE_CHECKING or _foo_available` at module top + a `require_package(...)` check at use time. Reuse the `_foo_available` flags in `utils/import_utils.py`; don't call `is_package_available`.
 - **Video decoding**: datasets can store observations as video files. `LeRobotDataset` handles frame extraction, but tests need ffmpeg installed.
 - **Prioritize use of `uv run`** to execute Python commands (not raw `python` or `pip`).
@@ -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.
@@ -83,7 +83,7 @@ episode_index=0
 print(f"{dataset[episode_index]['action'].shape=}\n")
 ```

-Learn more about it in the [LeRobotDataset Documentation](https://huggingface.co/docs/lerobot/lerobot-dataset-v3)
+Learn more about it in the [LeRobotDataset Documentation](https://huggingface.co/docs/lerobot/lerobot-dataset-v3).

 ## SoTA Models

@@ -109,7 +109,7 @@ lerobot-train \
 | **World Models**           | [VLA-JEPA](./docs/source/vla_jepa.mdx), [LingBot-VA](./docs/source/lingbot_va.mdx), [FastWAM](./docs/source/fastwam.mdx)                                                                                                                                                                                                                                                                   |
 | **Reward Models**          | [SARM](./docs/source/sarm.mdx), [TOPReward](./docs/source/topreward.mdx), [Robometer](./docs/source/robometer.mdx)                                                                                                                                                                                                                                                                         |

-Similarly to the hardware, you can easily implement your own policy & leverage LeRobot's data collection, training, and visualization tools, and share your model to the HF Hub
+Similarly to the hardware, you can easily implement your own policy & leverage LeRobot's data collection, training, and visualization tools, and share your model to the HF Hub.

 For detailed policy setup guides, see the [Policy Documentation](https://huggingface.co/docs/lerobot/bring_your_own_policies). For GPU/RAM requirements and expected training time per policy, see the [Compute Hardware Guide](https://huggingface.co/docs/lerobot/hardware_guide).

@@ -126,7 +126,7 @@ lerobot-eval \
  --eval.n_episodes=10
 ```

-Learn how to implement your own simulation environment or benchmark and distribute it from the HF Hub by following the [EnvHub Documentation](https://huggingface.co/docs/lerobot/envhub)
+Learn how to implement your own simulation environment or benchmark and distribute it from the HF Hub by following the [EnvHub Documentation](https://huggingface.co/docs/lerobot/envhub).

 ## Resources

@@ -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 \
@@ -89,8 +89,8 @@ subtask.

 The resulting spans are then stitched into a gap-free, full-episode
 cover, so **every frame has exactly one active subtask**. See
-[`run_hf_job.py`](https://github.com/huggingface/lerobot/blob/main/examples/annotations/run_hf_job.py)
-for the production settings (single camera, timestamped contact sheets,
+[Running on Hugging Face Jobs](#running-on-hugging-face-jobs) for the
+production settings (single camera, timestamped contact sheets,
 auto-windowed subtask generation).

 ### Tools
@@ -110,28 +110,67 @@ not-yet-implemented.

 ## Running on Hugging Face Jobs

-Annotation runs on [Hugging Face Jobs](https://huggingface.co/docs/hub/en/jobs).
-The repo ships a launcher script you copy and tweak for your dataset:
+Annotating a real dataset needs a GPU big enough to serve the VLM, so
+`lerobot-annotate` can dispatch itself to
+[Hugging Face Jobs](https://huggingface.co/docs/hub/en/jobs) — same as
+`lerobot-train`. Add `--job.target=<flavor>` to the exact command you'd
+run locally and it runs on that hardware instead:

 ```bash
-HF_TOKEN=hf_... uv run python examples/annotations/run_hf_job.py
+hf auth login   # once
+
+uv run lerobot-annotate \
+    --repo_id=user/my_dataset \
+    --new_repo_id=user/my_dataset_annotated \
+    --push_to_hub=true \
+    --vlm.model_id=Qwen/Qwen3.6-27B \
+    --vlm.num_gpus=1 \
+    --vlm.serve_command="vllm serve Qwen/Qwen3.6-27B --tensor-parallel-size 1 \
+        --max-model-len 32768 --gpu-memory-utilization 0.8 \
+        --uvicorn-log-level warning --port {port}" \
+    --vlm.serve_ready_timeout_s=1800 \
+    --vlm.chat_template_kwargs='{"enable_thinking": false}' \
+    --job.target=h200
 ```

-[`run_hf_job.py`](https://github.com/huggingface/lerobot/blob/main/examples/annotations/run_hf_job.py)
-starts a single-GPU `h200` job (bump it to `h200x4` for big datasets)
-that:
+That submits a single-GPU `h200` job that:

-1. installs `lerobot` (from `main`) plus the annotation extras,
-2. boots one vLLM server per GPU (using the `vllm/vllm-openai` image) and
-   drives it over the OpenAI-compatible API,
-3. runs the `plan` / `interjections` / `vqa` modules across the dataset
-   with `lerobot-annotate`,
+1. starts from the `vllm/vllm-openai` image and installs `lerobot` on top,
+2. boots one vLLM server per GPU and drives it over the OpenAI-compatible API,
+3. runs the `plan` / `interjections` / `vqa` modules across the dataset,
 4. with `--push_to_hub=true`, uploads the result to `--new_repo_id` (or
   back to `--repo_id` in place if you leave that unset).

-To use a different dataset, model, or hub repo, edit the `CMD` block in
-the script. Every flag there maps directly to a `lerobot-annotate` flag
-(run `lerobot-annotate --help` for the full list).
+The command streams the job's logs; `Ctrl-C` detaches without cancelling
+it. List the available flavors and their pricing with `hf jobs hardware`.
+
+<Tip warning={true}>
+
+Qwen3.6 ships with thinking enabled, which eats the token budget the
+annotator needs for its JSON answer — `--vlm.chat_template_kwargs='{"enable_thinking": false}'`
+turns it off. Without `--push_to_hub=true` the annotated dataset is
+discarded when the pod exits.
+
+</Tip>
+
+### Job options
+
+| Flag                | Default                   | What it does                                                                    |
+| ------------------- | ------------------------- | ------------------------------------------------------------------------------- |
+| `--job.target`      | `local`                   | HF Jobs flavor to run on (e.g. `h200`, `h200x4`). Omitted/`local` runs here.    |
+| `--job.image`       | `vllm/vllm-openai:latest` | Runtime image for the pod.                                                      |
+| `--job.timeout`     | `2h`                      | Wall-clock cap. Raise it for large datasets.                                    |
+| `--job.detach`      | `false`                   | Submit and exit instead of streaming logs.                                      |
+| `--job.lerobot_ref` | `main`                    | Git ref of lerobot installed on the pod — point it at a branch to test changes. |
+| `--job.tags`        | `[]`                      | Extra tags on the job and on any dataset it pushes (`lerobot` is always added). |
+
+For a bigger dataset, scale to `h200x4` and raise
+`--vlm.parallel_servers` / `--vlm.num_gpus` to match, and give the job
+more headroom with e.g. `--job.timeout=8h`.
+
+Remote runs need `--repo_id` (the pod pulls the dataset from the Hub;
+`--root` names a directory only your machine has). A dataset that exists
+only in your local cache is pushed to a **private** repo first.

 ## Key options

@@ -165,6 +165,8 @@ Batches are flat dictionaries keyed by the constants in [`lerobot.utils.constant

 LeRobot uses `PolicyProcessorPipeline`s to normalize inputs and de-normalize outputs around your policy. For a concrete reference, see [`processor_act.py`](https://github.com/huggingface/lerobot/blob/main/src/lerobot/policies/act/processor_act.py) or [`processor_diffusion.py`](https://github.com/huggingface/lerobot/blob/main/src/lerobot/policies/diffusion/processor_diffusion.py).

+Pay close attention here: processors are the most common reproducibility pain point. A mismatch in normalization mode (`IDENTITY` vs `MEAN_STD` vs `MIN_MAX` vs `QUANTILES`/`QUANTILE10`) or in which features get normalized will train and eval without erroring, yet silently wreck results. Make sure the modes match how the checkpoint was trained, that the required stats exist (e.g. `QUANTILES` needs `q01`/`q99`), and that the pre- and post-processors stay consistent.
+
 ```python
 # processor_my_policy.py
 from typing import Any
@@ -304,7 +306,9 @@ Mirror an existing policy that's structurally similar to yours; the diff is smal

 ### Heavy / optional dependencies

-Most policies need a heavy backbone (transformers, diffusers, a specific VLM SDK). The convention is **two-step gating**: a `TYPE_CHECKING`-guarded import at module top, and a `require_package` runtime check in the constructor. [`modeling_diffusion.py`](https://github.com/huggingface/lerobot/blob/main/src/lerobot/policies/diffusion/modeling_diffusion.py) is the canonical reference:
+Most policies need a heavy backbone (transformers, diffusers, a specific VLM SDK). Wherever one exists, prefer loading it e.g from `transformers` or `diffusers` rather than re-implementing the architecture in-tree.
+
+The convention is **two-step gating**: a `TYPE_CHECKING`-guarded import at module top, and a `require_package` runtime check in the constructor. [`modeling_diffusion.py`](https://github.com/huggingface/lerobot/blob/main/src/lerobot/policies/diffusion/modeling_diffusion.py) is the canonical reference:

 ```python
 from typing import TYPE_CHECKING
@@ -374,6 +378,7 @@ The general expectations are in [`CONTRIBUTING.md`](https://github.com/huggingfa
 - [ ] Optional deps live behind a `[project.optional-dependencies]` extra and the `TYPE_CHECKING + require_package` guard.
 - [ ] `tests/policies/` updated; backward-compat artifact committed & policy-specific tests.
 - [ ] `src/lerobot/policies/<name>/README.md` symlinked into `docs/source/policy_<name>_README.md`; user-facing `docs/source/<name>.mdx` written and added to `_toctree.yml`.
+- [ ] `lerobot-train --policy.type my_policy ...` runs end-to-end for at least a few steps + save a checkpoint that can be loaded and run by `lerobot-eval` or `lerobot-rollout`.
 - [ ] `templates/lerobot_modelcard_template.md` has a description entry and a `policy_docs` link for your policy.
 - [ ] The models table in the root `README.md` lists your policy in the right category, linking to your doc page.
 - [ ] At least one reproducible benchmark eval in the policy MDX with a published checkpoint (sim benchmark, or real-robot dataset + checkpoint).
@@ -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.
@@ -1,3 +1,11 @@
+# OMX
+
+<img
+  src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/omx_mainimage.png"
+  alt="OMX"
+  width=600
+/>
+
 ## Order and Assemble the parts

 First, assemble the OMX hardware following the official assembly guide.
@@ -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.
@@ -252,6 +252,10 @@ lerobot-dataset-viz \
    --episode-index 0
 ```

+For a private or gated dataset, authenticate first with `hf auth login`, or set the
+`HF_TOKEN` environment variable. The Hub client then discovers the credential
+automatically; no token argument is needed.
+
 **From a local folder:**
 Add the `--root` option and set `--mode local`. For example, to search in `./my_local_data_dir/lerobot/pusht`:

@@ -1,80 +0,0 @@
-#!/usr/bin/env python
-
-# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Launch ``lerobot-annotate`` on a Hugging Face job (vllm + Qwen3.6-27B VLM).
-
-Spawns one single-GPU ``h200`` job that:
-
-  1. installs ``lerobot`` from ``main`` plus the annotation extras,
-  2. boots one vllm server with Qwen3.6-27B (dense VLM),
-  3. runs the plan / interjections / vqa modules across the dataset
-     in free-form mode (each episode generates its own subtasks +
-     memory),
-  4. uploads the annotated dataset to ``--new_repo_id`` (when set)
-     or back to ``--repo_id``.
-
-Usage:
-
-    HF_TOKEN=hf_... uv run python examples/annotations/run_hf_job.py
-
-Adjust ``CMD`` (dataset, model, hub repo) and ``flavor`` below for your
-run. For larger datasets, scale to ``h200x4`` and raise
-``--vlm.parallel_servers`` / ``--vlm.num_gpus`` to match.
-"""
-
-import os
-
-from huggingface_hub import get_token, run_job
-
-token = os.environ.get("HF_TOKEN") or get_token()
-if not token:
-    raise RuntimeError("No HF token. Run `huggingface-cli login` or `export HF_TOKEN=hf_...`")
-
-CMD = (
-    "apt-get update -qq && apt-get install -y -qq git ffmpeg && "
-    "pip install --no-deps "
-    "'lerobot @ git+https://github.com/huggingface/lerobot.git@main' && "
-    # Pins mirror pyproject.toml — unpinned installs pull av 18 / datasets 5 /
-    # draccus 0.11, which break lerobot at import time.
-    "pip install --upgrade-strategy only-if-needed "
-    "'datasets>=4.7.0,<5.0.0' 'pyarrow>=21.0.0,<30.0.0' 'av>=15.0.0,<16.0.0' 'draccus==0.10.0' "
-    "'pandas>=2.0.0,<3.0.0' jsonlines gymnasium torchcodec mergedeep pyyaml-include toml typing-inspect "
-    "openai && "
-    "export VLLM_MEMORY_PROFILER_ESTIMATE_CUDAGRAPHS=0 && "
-    "export VLLM_VIDEO_BACKEND=pyav && "
-    "lerobot-annotate "
-    "--repo_id=pepijn223/robocasa_pretrain_human300_v4 "
-    "--new_repo_id=pepijn223/robocasa_pretrain_human300_v4_annotated "
-    "--push_to_hub=true "
-    "--vlm.backend=openai "
-    "--vlm.model_id=Qwen/Qwen3.6-27B "
-    "--vlm.num_gpus=1 "
-    '--vlm.serve_command="vllm serve Qwen/Qwen3.6-27B '
-    "--tensor-parallel-size 1 --max-model-len 32768 "
-    '--gpu-memory-utilization 0.8 --uvicorn-log-level warning --port {port}" '
-    "--vlm.serve_ready_timeout_s=1800 "
-    # Qwen3.6 ships with thinking on; annotation wants plain JSON answers.
-    "--vlm.chat_template_kwargs='{\"enable_thinking\": false}'"
-)
-
-job = run_job(
-    image="vllm/vllm-openai:latest",
-    command=["bash", "-c", CMD],
-    flavor="h200",
-    secrets={"HF_TOKEN": token},
-    timeout="2h",
-)
-print(f"Job URL: {job.url}")
-print(f"Job ID:  {job.id}")
@@ -155,7 +155,7 @@ accelerate-dep = ["accelerate>=1.14.0,<2.0.0"]
 can-dep = ["python-can>=4.2.0,<5.0.0"]
 peft-dep = ["peft>=0.18.0,<1.0.0"]
 scipy-dep = ["scipy>=1.14.0,<2.0.0"]
-diffusers-dep = ["diffusers>=0.27.2,<0.36.0"]
+diffusers-dep = ["diffusers>=0.38.0,<0.40.0"]
 qwen-vl-utils-dep = ["qwen-vl-utils>=0.0.11,<0.1.0"]
 matplotlib-dep = ["matplotlib>=3.10.3,<4.0.0", "contourpy>=1.3.0,<2.0.0"] # NOTE: Explicitly listing contourpy helps the resolver converge faster.
 pyserial-dep = ["pyserial>=3.5,<4.0"]
@@ -20,6 +20,29 @@ from dataclasses import dataclass, field
 from pathlib import Path
 from typing import Any

+from lerobot.configs.default import JobConfig
+
+# The annotation pipeline boots its own vLLM server, so the pod starts from the
+# official vLLM runtime rather than the prebuilt `lerobot-gpu` training image;
+# `lerobot` is pip-installed on top (see `lerobot.jobs.annotate`).
+DEFAULT_ANNOTATE_JOB_IMAGE = "vllm/vllm-openai:latest"
+
+
+@dataclass
+class AnnotationJobConfig(JobConfig):
+    """`JobConfig` with the annotation runtime's defaults.
+
+    Adds `lerobot_ref` because the vLLM image ships no lerobot: the pod installs
+    it from git, and the ref decides which code actually annotates. Point it at a
+    branch/tag/SHA to try unmerged changes remotely.
+    """
+
+    image: str = DEFAULT_ANNOTATE_JOB_IMAGE
+    # Annotation is a bounded pass over a dataset; a tighter cap than training's
+    # "2d" keeps a wedged vLLM server from burning a day of GPU time.
+    timeout: str | None = "2h"
+    lerobot_ref: str = "main"
+

@dataclass
 class PlanConfig:
@@ -207,6 +230,11 @@ class AnnotationPipelineConfig:
    vlm: VlmConfig = field(default_factory=VlmConfig)
    executor: ExecutorConfig = field(default_factory=ExecutorConfig)

+    # Where the annotation runs: omitted / "local" annotates on this machine, any
+    # other value is an HF Jobs flavor (e.g. "h200") and submits the run there.
+    # List flavors + pricing with `hf jobs hardware`.
+    job: AnnotationJobConfig = field(default_factory=AnnotationJobConfig)
+
    skip_validation: bool = False
    only_episodes: tuple[int, ...] | None = None

@@ -30,8 +30,8 @@ Phase 3 is why the ``plan`` module must be re-entered after the
 timestamps.

 Distributed execution is provided by Hugging Face Jobs (see
-``examples/annotations/run_hf_job.py``); the runner inside the job
-invokes ``lerobot-annotate`` which uses this in-process executor.
+``lerobot.jobs.annotate``, reached via ``--job.target=<flavor>``); the pod
+inside the job invokes ``lerobot-annotate`` which uses this in-process executor.
 Episode-level concurrency is controlled by
 ``ExecutorConfig.episode_parallelism``.
 """
@@ -194,12 +194,13 @@ def make_vlm_client(config: VlmConfig) -> VlmClient:
    """Build the shared VLM client.

    Only the ``openai`` backend is supported for now. The shipped workflow
-    is Hugging Face Jobs (``examples/annotations/run_hf_job.py``): it boots
-    a vLLM server inside the ``vllm/vllm-openai`` image and the pipeline
-    talks to it over the OpenAI-compatible API (``--vlm.backend=openai``,
-    optionally auto-spawning the server via ``auto_serve`` /
-    ``serve_command``). The former in-process ``vllm`` / ``transformers``
-    backends were removed to keep the support surface to the HF Jobs path.
+    is Hugging Face Jobs (``lerobot-annotate --job.target=<flavor>``): it
+    boots a vLLM server inside the ``vllm/vllm-openai`` image and the
+    pipeline talks to it over the OpenAI-compatible API
+    (``--vlm.backend=openai``, optionally auto-spawning the server via
+    ``auto_serve`` / ``serve_command``). The former in-process ``vllm`` /
+    ``transformers`` backends were removed to keep the support surface to
+    the HF Jobs path.

    For ``stub``, construct :class:`StubVlmClient` directly with a responder
    callable; it is rejected here to make accidental misuse obvious.
@@ -213,8 +214,8 @@ def make_vlm_client(config: VlmConfig) -> VlmClient:
    if config.backend in {"vllm", "transformers"}:
        raise ValueError(
            f"backend={config.backend!r} (in-process local model) is not supported for now — "
-            "only backend='openai' (the Hugging Face Jobs flow) is. Run the pipeline via "
-            "examples/annotations/run_hf_job.py, which serves the model with vLLM in the "
+            "only backend='openai' (the Hugging Face Jobs flow) is. Run the pipeline with "
+            "`lerobot-annotate --job.target=<flavor>`, which serves the model with vLLM in the "
            "vllm/vllm-openai image and talks to it over the OpenAI-compatible API."
        )
    raise ValueError(f"Unknown VLM backend: {config.backend!r}")
@@ -173,7 +173,8 @@ class Reachy2Camera(Camera):
            raise ValueError(
                f"Invalid color mode '{self.color_mode}'. Expected {ColorMode.RGB} or {ColorMode.BGR}."
            )
-        if self.color_mode == ColorMode.RGB:
+        is_depth_frame = self.config.name == "depth" and self.config.image_type == "depth"
+        if not is_depth_frame and self.color_mode == ColorMode.RGB:
            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)

        self.latest_frame = frame
@@ -453,7 +453,7 @@ class RealSenseCamera(Camera):
            )

        processed_image = image
-        if self.color_mode == ColorMode.BGR:
+        if not depth_frame and self.color_mode == ColorMode.BGR:
            processed_image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)

        if self.rotation in [cv2.ROTATE_90_CLOCKWISE, cv2.ROTATE_90_COUNTERCLOCKWISE, cv2.ROTATE_180]:
@@ -14,6 +14,7 @@
 import builtins
 import datetime as dt
 import json
+import multiprocessing
 import os
 import tempfile
 from dataclasses import dataclass, field
@@ -101,6 +102,12 @@ class TrainPipelineConfig(HubMixin):
    batch_size: int = 8
    prefetch_factor: int = 4
    persistent_workers: bool = True
+    # DataLoader worker start method. "spawn" is safer than "fork" with
+    # non-fork-safe libs (PyAV / torchcodec / ffmpeg), but adds some
+    # worker-startup time per run since workers re-import modules instead
+    # of inheriting parent state. Override with `--dataloader_multiprocessing_context=fork`
+    # when appropriate, or set it to `null` to use Python's platform default.
+    dataloader_multiprocessing_context: str | None = "spawn"
    steps: int = 100_000
    # Run policy in the simulation environment every N steps to measure reward/success (0 = disabled).
    env_eval_freq: int = 20_000
@@ -212,6 +219,17 @@ class TrainPipelineConfig(HubMixin):
            self.reward_model.pretrained_path = str(policy_dir)

    def validate(self) -> None:
+        available_contexts = multiprocessing.get_all_start_methods()
+        if (
+            self.dataloader_multiprocessing_context is not None
+            and self.dataloader_multiprocessing_context not in available_contexts
+        ):
+            raise ValueError(
+                "`dataloader_multiprocessing_context` must be None or one of "
+                f"{available_contexts} on this platform, got "
+                f"{self.dataloader_multiprocessing_context!r}."
+            )
+
        self._resolve_pretrained_from_cli()

        if self.policy is None and self.reward_model is None:
@@ -73,6 +73,8 @@ class LeRobotDatasetMetadata:
        revision: str | None = None,
        force_cache_sync: bool = False,
        metadata_buffer_size: int = 10,
+        *,
+        token: str | bool | None = None,
    ):
        """Load or download metadata for an existing LeRobot dataset.

@@ -94,6 +96,10 @@ class LeRobotDatasetMetadata:
                even when local files exist.
            metadata_buffer_size: Number of episode metadata records to buffer
                in memory before flushing to parquet.
+            token: Authentication token used for Hub requests. Pass a string
+                token, ``True`` to require the locally stored token, ``False``
+                to disable authentication, or ``None`` to use the Hugging Face
+                Hub default.
        """
        self.repo_id = repo_id
        self.revision = revision if revision else CODEBASE_VERSION
@@ -113,9 +119,12 @@ class LeRobotDatasetMetadata:
            self._load_metadata()
        except (FileNotFoundError, NotADirectoryError):
            if is_valid_version(self.revision):
-                self.revision = get_safe_version(self.repo_id, self.revision)
+                if token is None:
+                    self.revision = get_safe_version(self.repo_id, self.revision)
+                else:
+                    self.revision = get_safe_version(self.repo_id, self.revision, token=token)

-            self._pull_from_repo(allow_patterns="meta/")
+            self._pull_from_repo(allow_patterns="meta/", token=token)
            self._load_metadata()

    def _flush_metadata_buffer(self) -> None:
@@ -220,7 +229,10 @@ class LeRobotDatasetMetadata:
        self,
        allow_patterns: list[str] | str | None = None,
        ignore_patterns: list[str] | str | None = None,
+        *,
+        token: str | bool | None = None,
    ) -> None:
+        token_kwargs = {} if token is None else {"token": token}
        if self._requested_root is None:
            self.root = Path(
                snapshot_download(
@@ -230,6 +242,7 @@ class LeRobotDatasetMetadata:
                    cache_dir=HF_LEROBOT_HUB_CACHE,
                    allow_patterns=allow_patterns,
                    ignore_patterns=ignore_patterns,
+                    **token_kwargs,
                )
            )
            return
@@ -242,6 +255,7 @@ class LeRobotDatasetMetadata:
            local_dir=self._requested_root,
            allow_patterns=allow_patterns,
            ignore_patterns=ignore_patterns,
+            **token_kwargs,
        )
        self.root = self._requested_root

@@ -172,6 +172,23 @@ class DatasetWriter:
    def _get_image_file_dir(self, episode_index: int, image_key: str) -> Path:
        return self._get_image_file_path(episode_index, image_key, frame_index=0).parent

+    def _get_episode_buffer_index(self) -> int:
+        episode_index = self.episode_buffer["episode_index"]
+        # episode_index is `int` when freshly created, but becomes `np.ndarray` after
+        # save_episode() mutates the buffer. Handle both types here.
+        if isinstance(episode_index, np.ndarray):
+            episode_index = episode_index.item() if episode_index.size == 1 else episode_index[0]
+        return int(episode_index)
+
+    def _delete_camera_frame_dirs(self, camera_keys: list[str]) -> None:
+        if self.image_writer is not None:
+            self._wait_image_writer()
+        episode_index = self._get_episode_buffer_index()
+        for camera_key in camera_keys:
+            img_dir = self._get_image_file_dir(episode_index, camera_key)
+            if img_dir.is_dir():
+                shutil.rmtree(img_dir)
+
    def _save_image(
        self, image: torch.Tensor | np.ndarray | PIL.Image.Image, fpath: Path, compress_level: int = 1
    ) -> None:
@@ -369,7 +386,9 @@ class DatasetWriter:
                self._episodes_since_last_encoding = 0

        if episode_data is None:
-            self.clear_episode_buffer(delete_images=len(self._meta.image_keys) > 0)
+            if len(self._meta.image_keys) > 0:
+                self._delete_camera_frame_dirs(self._meta.image_keys)
+            self.episode_buffer = self._create_episode_buffer()

    def _batch_save_episode_video(self, start_episode: int, end_episode: int | None = None) -> None:
        """Batch save videos for multiple episodes."""
@@ -561,10 +580,10 @@ class DatasetWriter:
        return metadata

    def clear_episode_buffer(self, delete_images: bool = True) -> None:
-        """Discard the current episode buffer and optionally delete temp images.
+        """Discard the current episode buffer and optionally delete temp camera frames.

        Args:
-            delete_images: If ``True``, remove temporary image directories
+            delete_images: If ``True``, remove temporary camera frame directories
                written for the current episode.
        """
        # Cancel streaming encoder if active
@@ -572,17 +591,7 @@ class DatasetWriter:
            self._streaming_encoder.cancel_episode()

        if delete_images:
-            if self.image_writer is not None:
-                self._wait_image_writer()
-            episode_index = self.episode_buffer["episode_index"]
-            # episode_index is `int` when freshly created, but becomes `np.ndarray` after
-            # save_episode() mutates the buffer. Handle both types here.
-            if isinstance(episode_index, np.ndarray):
-                episode_index = episode_index.item() if episode_index.size == 1 else episode_index[0]
-            for cam_key in self._meta.image_keys:
-                img_dir = self._get_image_file_dir(episode_index, cam_key)
-                if img_dir.is_dir():
-                    shutil.rmtree(img_dir)
+            self._delete_camera_frame_dirs(self._meta.camera_keys)

        self.episode_buffer = self._create_episode_buffer()

@@ -65,6 +65,8 @@ class LeRobotDataset(torch.utils.data.Dataset):
        encoder_threads: int | None = None,
        streaming_encoding: bool = False,
        encoder_queue_maxsize: int = 30,
+        *,
+        token: str | bool | None = None,
    ):
        """
        2 modes are available for instantiating this class, depending on 2 different use cases:
@@ -197,6 +199,11 @@ class LeRobotDataset(torch.utils.data.Dataset):
                instead of writing PNG images first. This makes save_episode() near-instant. Defaults to False.
            encoder_queue_maxsize (int, optional): Maximum number of frames to buffer per camera when using
                streaming encoding. Defaults to 30 (~1s at 30fps).
+            token: Authentication token used while downloading this dataset
+                from the Hub. Pass a string token, ``True`` to require the
+                locally stored token, ``False`` to disable authentication, or
+                ``None`` to use the Hugging Face Hub default. The token is not
+                retained on the dataset instance after initialization.

        Note:
            Write-mode parameters (``streaming_encoding``, ``batch_encoding_size``) passed to
@@ -220,7 +227,11 @@ class LeRobotDataset(torch.utils.data.Dataset):

        # Load metadata (sets self.root once from the resolved metadata root)
        self.meta = LeRobotDatasetMetadata(
-            self.repo_id, self._requested_root, self.revision, force_cache_sync=force_cache_sync
+            self.repo_id,
+            self._requested_root,
+            self.revision,
+            force_cache_sync=force_cache_sync,
+            token=token,
        )
        self.root = self.meta.root
        self.revision = self.meta.revision
@@ -260,8 +271,11 @@ class LeRobotDataset(torch.utils.data.Dataset):
        # Load actual data
        if force_cache_sync or not self.reader.try_load():
            if is_valid_version(self.revision):
-                self.revision = get_safe_version(self.repo_id, self.revision)
-            self._download(download_videos)
+                if token is None:
+                    self.revision = get_safe_version(self.repo_id, self.revision)
+                else:
+                    self.revision = get_safe_version(self.repo_id, self.revision, token=token)
+            self._download(download_videos, token=token)
            self.reader.load_and_activate()

        # Detect write-mode params for backward compatibility
@@ -478,18 +492,19 @@ class LeRobotDataset(torch.utils.data.Dataset):
        """Return the number of frames in the selected episodes."""
        return self.num_frames

-    def __getitem__(self, idx) -> dict:
-        """Return a single frame by index, with all transforms applied.
+    def __getitem__(self, idx: int | slice) -> dict | list[dict]:
+        """Return one frame or a slice of frames, with all transforms applied.

        Loads the frame from the underlying HF dataset, expands delta-timestamp
        windows, decodes video frames, and applies image transforms. Delegates
-        the core logic to :meth:`DatasetReader.get_item`.
+        the core logic to :class:`DatasetReader`.

        Args:
-            idx: Index into the (possibly episode-filtered) dataset.
+            idx: Integer index or slice into the possibly episode-filtered dataset.

        Returns:
-            Dict mapping feature names to their tensor values for this frame.
+            A frame dictionary for an integer index, or a list of frame
+            dictionaries for a slice.

        Raises:
            RuntimeError: If the dataset is currently being recorded and
@@ -499,6 +514,9 @@ class LeRobotDataset(torch.utils.data.Dataset):
            raise RuntimeError(
                "Cannot read from a dataset that is being recorded. Call finalize() first, then access items."
            )
+        if isinstance(idx, slice):
+            return [self[item_idx] for item_idx in range(*idx.indices(len(self)))]
+
        reader = self._ensure_reader()
        if reader.hf_dataset is None:
            # One-shot load after finalize()
@@ -622,10 +640,11 @@ class LeRobotDataset(torch.utils.data.Dataset):
                hub_api.delete_tag(self.repo_id, tag=CODEBASE_VERSION, repo_type="dataset")
            hub_api.create_tag(self.repo_id, tag=CODEBASE_VERSION, revision=branch, repo_type="dataset")

-    def _download(self, download_videos: bool = True) -> None:
+    def _download(self, download_videos: bool = True, *, token: str | bool | None = None) -> None:
        """Downloads the dataset from the given 'repo_id' at the provided version."""
        ignore_patterns = None if download_videos else "videos/"
        files = None
+        token_kwargs = {} if token is None else {"token": token}
        if self.episodes is not None:
            # Reader is guaranteed to exist here (created in __init__ before _download)
            files = self.reader.get_episodes_file_paths()
@@ -639,6 +658,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
                    cache_dir=HF_LEROBOT_HUB_CACHE,
                    allow_patterns=files,
                    ignore_patterns=ignore_patterns,
+                    **token_kwargs,
                )
            )
        else:
@@ -650,6 +670,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
                local_dir=self._requested_root,
                allow_patterns=files,
                ignore_patterns=ignore_patterns,
+                **token_kwargs,
            )
            self.meta.root = self._requested_root

@@ -789,6 +810,8 @@ class LeRobotDataset(torch.utils.data.Dataset):
        image_writer_threads: int = 0,
        streaming_encoding: bool = False,
        encoder_queue_maxsize: int = 30,
+        *,
+        token: str | bool | None = None,
    ) -> "LeRobotDataset":
        """Resume recording on an existing dataset.

@@ -822,6 +845,8 @@ class LeRobotDataset(torch.utils.data.Dataset):
            streaming_encoding: If ``True``, encode video in real-time during
                capture.
            encoder_queue_maxsize: Max buffered frames per camera for streaming.
+            token: Authentication token used if metadata must be downloaded
+                from the Hub. The token is not retained on the dataset instance.

        Returns:
            A :class:`LeRobotDataset` in write mode, ready to append episodes.
@@ -850,7 +875,11 @@ class LeRobotDataset(torch.utils.data.Dataset):

        # Load metadata (revision-safe when root is not provided)
        obj.meta = LeRobotDatasetMetadata(
-            obj.repo_id, obj._requested_root, obj.revision, force_cache_sync=force_cache_sync
+            obj.repo_id,
+            obj._requested_root,
+            obj.revision,
+            force_cache_sync=force_cache_sync,
+            token=token,
        )

        obj._encoder_threads = encoder_threads
@@ -48,6 +48,8 @@ class MultiLeRobotDataset(torch.utils.data.Dataset):
        tolerances_s: dict | None = None,
        download_videos: bool = True,
        video_backend: str | None = None,
+        *,
+        token: str | bool | None = None,
    ):
        super().__init__()
        self.repo_ids = repo_ids
@@ -65,6 +67,7 @@ class MultiLeRobotDataset(torch.utils.data.Dataset):
                tolerance_s=self.tolerances_s[repo_id],
                download_videos=download_videos,
                video_backend=video_backend,
+                token=token,
            )
            for repo_id in repo_ids
        ]
@@ -256,6 +256,8 @@ class StreamingLeRobotDataset(torch.utils.data.IterableDataset):
        shuffle: bool = True,
        return_uint8: bool = False,
        depth_output_unit: str = DEFAULT_DEPTH_UNIT,
+        *,
+        token: str | bool | None = None,
    ):
        """Initialize a StreamingLeRobotDataset.

@@ -278,6 +280,11 @@ class StreamingLeRobotDataset(torch.utils.data.IterableDataset):
            shuffle (bool, optional): Whether to shuffle the dataset across exhaustions. Defaults to True.
            depth_output_unit (str, optional): Physical unit depth maps are dequantized to ("m" or "mm").
                Defaults to "mm".
+            token: Authentication token used while streaming this dataset from
+                the Hub. Pass a string token, ``True`` to require the locally
+                stored token, ``False`` to disable authentication, or ``None``
+                to use the Hugging Face Hub default. The token is not retained
+                on the dataset instance after initialization.
        """
        super().__init__()
        self.repo_id = repo_id
@@ -306,7 +313,11 @@ class StreamingLeRobotDataset(torch.utils.data.IterableDataset):

        # Load metadata
        self.meta = LeRobotDatasetMetadata(
-            self.repo_id, self._requested_root, self.revision, force_cache_sync=force_cache_sync
+            self.repo_id,
+            self._requested_root,
+            self.revision,
+            force_cache_sync=force_cache_sync,
+            token=token,
        )
        self.root = self.meta.root
        self.revision = self.meta.revision
@@ -334,12 +345,14 @@ class StreamingLeRobotDataset(torch.utils.data.IterableDataset):
            self.delta_timestamps = delta_timestamps
            self.delta_indices = get_delta_indices(self.delta_timestamps, self.fps)

+        token_kwargs = {} if token is None or self.streaming_from_local else {"token": token}
        self.hf_dataset: datasets.IterableDataset = load_dataset(
            self.repo_id if not self.streaming_from_local else str(self.root),
            split="train",
            streaming=self.streaming,
            data_files="data/*/*.parquet",
            revision=self.revision,
+            **token_kwargs,
        )

        self.num_shards = min(self.hf_dataset.num_shards, max_num_shards)
@@ -325,16 +325,19 @@ def check_version_compatibility(
        logging.warning(FUTURE_MESSAGE.format(repo_id=repo_id, version=v_check))


-def get_repo_versions(repo_id: str) -> list[packaging.version.Version]:
+def get_repo_versions(repo_id: str, *, token: str | bool | None = None) -> list[packaging.version.Version]:
    """Return available valid versions (branches and tags) on a given Hub repo.

    Args:
        repo_id (str): The repository ID on the Hugging Face Hub.
+        token: Authentication token used for Hub requests. Pass a string token,
+            ``True`` to require the locally stored token, ``False`` to disable
+            authentication, or ``None`` to use the Hugging Face Hub default.

    Returns:
        list[packaging.version.Version]: A list of valid versions found.
    """
-    api = HfApi()
+    api = HfApi() if token is None else HfApi(token=token)
    repo_refs = api.list_repo_refs(repo_id, repo_type="dataset")
    repo_refs = [b.name for b in repo_refs.branches + repo_refs.tags]
    repo_versions = []
@@ -345,7 +348,12 @@ def get_repo_versions(repo_id: str) -> list[packaging.version.Version]:
    return repo_versions


-def get_safe_version(repo_id: str, version: str | packaging.version.Version) -> str:
+def get_safe_version(
+    repo_id: str,
+    version: str | packaging.version.Version,
+    *,
+    token: str | bool | None = None,
+) -> str:
    """Return the specified version if available on repo, or the latest compatible one.

    If the exact version is not found, it looks for the latest version with the
@@ -354,6 +362,7 @@ def get_safe_version(repo_id: str, version: str | packaging.version.Version) ->
    Args:
        repo_id (str): The repository ID on the Hugging Face Hub.
        version (str | packaging.version.Version): The target version.
+        token: Authentication token forwarded to the Hub version lookup.

    Returns:
        str: The safe version string (e.g., "v1.2.3") to use as a revision.
@@ -366,7 +375,7 @@ def get_safe_version(repo_id: str, version: str | packaging.version.Version) ->
    target_version = (
        packaging.version.parse(version) if not isinstance(version, packaging.version.Version) else version
    )
-    hub_versions = get_repo_versions(repo_id)
+    hub_versions = get_repo_versions(repo_id) if token is None else get_repo_versions(repo_id, token=token)

    if not hub_versions:
        raise RevisionNotFoundError(
@@ -322,7 +322,7 @@ class HILSerlRobotEnvConfig(EnvConfig):
 class LiberoEnv(EnvConfig):
    task: str = "libero_10"  # can also choose libero_spatial, libero_object, etc.
    task_ids: list[int] | None = None
-    fps: int = 30
+    fps: int = 20  # Must match robosuite's default control_freq (20 Hz)
    episode_length: int | None = None
    obs_type: str = "pixels_agent_pos"
    render_mode: str = "rgb_array"
@@ -354,6 +354,9 @@ class LiberoEnv(EnvConfig):
    control_mode: str = "relative"  # or "absolute"

    def __post_init__(self):
+        if self.fps <= 0:
+            raise ValueError(f"fps must be positive, got {self.fps}")
+
        if self.obs_type == "pixels":
            self.features[LIBERO_KEY_PIXELS_AGENTVIEW] = PolicyFeature(
                type=FeatureType.VISUAL, shape=(self.observation_height, self.observation_width, 3)
@@ -412,6 +415,7 @@ class LiberoEnv(EnvConfig):
            "render_mode": self.render_mode,
            "observation_height": self.observation_height,
            "observation_width": self.observation_width,
+            "control_freq": self.fps,
        }
        if self.task_ids is not None:
            kwargs["task_ids"] = self.task_ids
@@ -125,10 +125,13 @@ class LiberoEnv(gym.Env):
        n_envs: int = 1,
        camera_name_mapping: dict[str, str] | None = None,
        num_steps_wait: int = 10,
+        control_freq: int = 20,
        control_mode: str = "relative",
        is_libero_plus: bool = False,
    ):
        super().__init__()
+        if control_freq <= 0:
+            raise ValueError(f"control_freq must be positive, got {control_freq}")
        self.task_id = task_id
        self.is_libero_plus = is_libero_plus
        self.obs_type = obs_type
@@ -154,6 +157,7 @@ class LiberoEnv(gym.Env):
            }
        self.camera_name_mapping = camera_name_mapping
        self.num_steps_wait = num_steps_wait
+        self.control_freq = control_freq
        self.episode_index = episode_index
        self.episode_length = episode_length
        # Load once and keep
@@ -260,6 +264,7 @@ class LiberoEnv(gym.Env):
            bddl_file_name=self._task_bddl_file,
            camera_heights=self.observation_height,
            camera_widths=self.observation_width,
+            control_freq=self.control_freq,
        )
        env.reset()
        self._env = env
@@ -155,6 +155,7 @@ class MetaworldEnv(gym.Env):
            env.model.cam_pos[2] = [0.75, 0.075, 0.7]
        env.reset()
        env._freeze_rand_vec = False  # otherwise no randomization
+        env.seeded_rand_vec = True  # use seeded RNG so reset(seed=X) controls object positions
        self._env = env

    def render(self) -> np.ndarray:
@@ -220,6 +221,8 @@ class MetaworldEnv(gym.Env):
        self._ensure_env()
        super().reset(seed=seed)

+        if seed is not None:
+            self._env.seed(seed)
        raw_obs, info = self._env.reset(seed=seed)

        observation = self._format_raw_obs(raw_obs)
@@ -18,6 +18,7 @@ from lerobot.utils.import_utils import require_package
 # guard the optional dependency here so importing this package fails loudly if it's missing.
 require_package("datasets", extra="dataset")

+from .annotate import submit_annotate_to_hf
 from .hf import submit_to_hf

-__all__ = ["submit_to_hf"]
+__all__ = ["submit_annotate_to_hf", "submit_to_hf"]
@@ -0,0 +1,176 @@
+# 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.
+"""Run ``lerobot-annotate`` on HF Jobs (HuggingFace GPUs).
+
+Same shape as the training submitter in ``hf.py``, with one difference: the
+annotation pipeline serves its own VLM, so the pod starts from the official
+``vllm/vllm-openai`` image (which has no lerobot) instead of the prebuilt
+``lerobot-gpu`` image, and installs lerobot on top before running.
+
+Because there is no config repo to stage, the pod replays the user's own CLI
+flags — everything except the client-only ``--job.*`` and the host-local
+``--root``, which is replaced by ``--repo_id`` so the pod pulls the dataset
+from the Hub.
+"""
+
+from __future__ import annotations
+
+import shlex
+import sys
+from dataclasses import is_dataclass
+from typing import TYPE_CHECKING
+
+from huggingface_hub import HfApi, get_token, run_job
+
+from .dataset import ensure_dataset_available
+
+# Package-internal reuse of the training submitter's job plumbing: following a
+# submitted job and forwarding argv are identical for annotation runs.
+from .hf import _pod_forwarded_args, follow_job, resolve_job_tags
+
+if TYPE_CHECKING:
+    from lerobot.annotations.steerable_pipeline.config import AnnotationPipelineConfig
+
+LEROBOT_GIT_URL = "https://github.com/huggingface/lerobot.git"
+
+# Mirrors the pins in pyproject.toml. The vLLM image resolves dependencies on its
+# own otherwise, and pulls av 18 / datasets 5 / draccus 0.11 — each of which breaks
+# lerobot at import time. `--upgrade-strategy only-if-needed` keeps vLLM's own
+# (torch, transformers, ...) pins intact.
+_RUNTIME_REQUIREMENTS = (
+    "'datasets>=4.7.0,<5.0.0' 'pyarrow>=21.0.0,<30.0.0' 'av>=15.0.0,<16.0.0' 'draccus==0.10.0' "
+    "'pandas>=2.0.0,<3.0.0' jsonlines gymnasium torchcodec mergedeep pyyaml-include toml typing-inspect "
+    "openai"
+)
+
+# Flags the submitter resolves itself instead of forwarding verbatim: `--root`
+# names a directory only this machine has, `--repo_id` is re-emitted from the
+# config, and the config-file args name local files (rejected up front by
+# `submit_annotate_to_hf`). `--job.*` is dropped separately, by prefix; bare
+# `--job` is not, hence its entry here — it is the one arg that could smuggle a
+# remote `target` onto the pod and have the job recursively submit itself.
+_SUBMITTER_OWNED_ARGS = ("--root", "--repo_id", "--config_path", "--job")
+
+
+def _local_config_file_args(cfg: AnnotationPipelineConfig) -> list[str]:
+    """The CLI args that name a config file on the client's disk.
+
+    draccus exposes ``--config_path`` for the whole config plus a ``--<field>``
+    for every nested dataclass (``--vlm``, ``--plan``, ``--job``, ...). The pod has
+    none of those files, so a remote run has to reject them rather than silently
+    drop the settings they carry.
+    """
+    return ["--config_path", *(f"--{name}" for name in vars(cfg) if is_dataclass(getattr(cfg, name)))]
+
+
+def build_pod_setup(lerobot_ref: str) -> str:
+    """Shell prelude that turns the vLLM image into a ``lerobot-annotate`` runtime."""
+    spec = f"lerobot @ git+{LEROBOT_GIT_URL}@{lerobot_ref}"
+    return (
+        # git to install from the repo, ffmpeg to decode the dataset's videos.
+        "apt-get update -qq && apt-get install -y -qq git ffmpeg && "
+        f"pip install --no-deps {shlex.quote(spec)} && "
+        f"pip install --upgrade-strategy only-if-needed {_RUNTIME_REQUIREMENTS} && "
+        # vLLM's cudagraph memory estimate over-reserves and starves the KV cache;
+        # PyAV is the video backend the server can decode our frames with.
+        "export VLLM_MEMORY_PROFILER_ESTIMATE_CUDAGRAPHS=0 && "
+        "export VLLM_VIDEO_BACKEND=pyav"
+    )
+
+
+def build_pod_command(repo_id: str, lerobot_ref: str, argv: list[str]) -> list[str]:
+    """Build the ``bash -c`` command the pod runs: setup prelude, then annotation.
+
+    ``argv`` is the user's CLI (``sys.argv[1:]``) minus the flags in
+    ``_SUBMITTER_OWNED_ARGS``; ``--repo_id`` is re-added from the config so the pod
+    always annotates the dataset we just made sure is reachable on the Hub.
+    ``--job.target=local`` stops the pod from re-dispatching to itself.
+    """
+    forwarded = _pod_forwarded_args(argv, drop_names=_SUBMITTER_OWNED_ARGS, drop_prefixes=("--job.",))
+    annotate = shlex.join(["lerobot-annotate", f"--repo_id={repo_id}", *forwarded, "--job.target=local"])
+    return ["bash", "-c", f"{build_pod_setup(lerobot_ref)} && {annotate}"]
+
+
+def submit_annotate_to_hf(cfg: AnnotationPipelineConfig) -> None:
+    """Submit an annotation run to HF Jobs infrastructure.
+
+    Resolves credentials, makes sure the source dataset is reachable from the pod,
+    submits the job, then tails its logs until the job reaches a terminal stage —
+    or returns immediately with ``--job.detach``. Ctrl-C detaches without
+    cancelling the remote job.
+    """
+    token = get_token()
+    if not token:
+        raise RuntimeError("Not logged in to Hugging Face. Run `hf auth login` first.")
+
+    if cfg.repo_id is None:
+        raise ValueError(
+            "Remote annotation requires --repo_id: the pod downloads the dataset from the Hub, "
+            "and --root only names a directory on this machine."
+        )
+
+    argv = sys.argv[1:]
+    passed = {tok.split("=", 1)[0] for tok in argv}
+    used_config_files = sorted(passed.intersection(_local_config_file_args(cfg)))
+    if used_config_files:
+        raise ValueError(
+            f"{', '.join(used_config_files)} cannot be used with a remote --job.target: the pod "
+            "cannot read config files from this machine. Pass the settings as CLI flags instead."
+        )
+
+    if not cfg.push_to_hub:
+        # The pod's filesystem is discarded when the job ends, so without a push the
+        # run produces nothing. Warn rather than fail: a smoke test over
+        # --only_episodes that only inspects the logs is a legitimate use.
+        print(
+            "WARNING: --push_to_hub is off. The annotated dataset lives only on the pod and is "
+            "discarded when the job ends. Pass --push_to_hub=true to keep the result."
+        )
+
+    api = HfApi(token=token)
+    tags = resolve_job_tags(cfg.job.tags)
+    ensure_dataset_available(cfg.repo_id, api=api, tags=tags)
+
+    command = build_pod_command(cfg.repo_id, cfg.job.lerobot_ref, argv)
+
+    print(f"Submitting job to HF Jobs (flavor={cfg.job.target}, image={cfg.job.image}) ...")
+    job_info = run_job(
+        image=cfg.job.image,
+        command=command,
+        flavor=cfg.job.target,
+        secrets={"HF_TOKEN": token},
+        timeout=cfg.job.timeout,
+        # HF Jobs labels are key/value; expose each tag as a queryable label.
+        labels=dict.fromkeys(tags, "true"),
+    )
+    job_id = job_info.id
+    job_url = getattr(job_info, "url", None)
+    print(f"Job submitted: {job_id}")
+    if job_url:
+        print(f"  Job page:     {job_url}")
+    target_repo_id = cfg.new_repo_id or cfg.repo_id
+    if cfg.push_to_hub:
+        print(f"  Dataset repo: https://huggingface.co/datasets/{target_repo_id}")
+    print(f"  Monitor:      hf jobs logs {job_id}")
+    print(f"  Cancel:       hf jobs cancel {job_id}")
+
+    # No success marker: `lerobot-annotate` keeps working after the upload log line
+    # (dataset card, version tag), so completion has to be stage-based.
+    if not follow_job(job_id, detach=cfg.job.detach):
+        return
+
+    if cfg.push_to_hub:
+        print(f"\nAnnotation complete — dataset pushed to https://huggingface.co/datasets/{target_repo_id}")
+    else:
+        print("\nAnnotation complete. Note: --push_to_hub was off, so the result stayed on the pod.")
@@ -223,6 +223,74 @@ def _poll_until_done(
    return None


+def follow_job(job_id: str, *, detach: bool = False, success_marker: str | None = None) -> bool:
+    """Watch a submitted job to the end, streaming its logs to stdout.
+
+    Returns True when the job finished successfully and False when we stopped watching
+    without a verdict — `detach`, or the user pressing Ctrl-C, which detaches rather than
+    cancelling the remote job. Raises RuntimeError when the job reaches a terminal stage
+    other than COMPLETED.
+
+    `success_marker` finishes as soon as that string appears in the logs instead of waiting
+    out the platform's post-run finalization (~30s). Callers that have a log line meaning
+    "the artifact is on the Hub" should pass it; without one, completion is stage-based.
+    """
+    if detach:
+        return False
+
+    done = threading.Event()
+    detached = threading.Event()
+    marker_seen = threading.Event()
+    stage_holder: dict[str, str | None] = {}
+
+    def _poll() -> None:
+        stage_holder["stage"] = _poll_until_done(job_id, done, status_holder=stage_holder)
+
+    poll_thread = threading.Thread(target=_poll, daemon=True)
+    poll_thread.start()
+    log_thread = threading.Thread(
+        target=_tail_logs, args=(job_id, done, success_marker, marker_seen), daemon=True
+    )
+    log_thread.start()
+
+    def _detach(sig, frame):
+        detached.set()
+        done.set()
+        print("\nDetached. Job is still running.")
+        print(f"  Monitor: hf jobs logs {job_id}")
+        print(f"  Cancel:  hf jobs cancel {job_id}")
+
+    # signal.signal only works on the main thread; when called from a worker thread
+    # (e.g. an orchestration framework) skip the Ctrl-C-detaches-instead-of-cancels
+    # handler rather than crashing with ValueError.
+    install_sigint = threading.current_thread() is threading.main_thread()
+    original_sigint = signal.getsignal(signal.SIGINT) if install_sigint else None
+    if install_sigint:
+        signal.signal(signal.SIGINT, _detach)
+    try:
+        # Timeout-based join so SIGINT is delivered to the main thread promptly.
+        while poll_thread.is_alive():
+            poll_thread.join(timeout=0.5)
+        log_thread.join(timeout=5)
+    finally:
+        if install_sigint:
+            signal.signal(signal.SIGINT, original_sigint)
+
+    if detached.is_set():
+        return False
+    if marker_seen.is_set():
+        return True
+
+    stage = stage_holder.get("stage")
+    if stage != "COMPLETED":
+        message = stage_holder.get("message")
+        detail = f" ({message})" if message else ""
+        raise RuntimeError(
+            f"Job {job_id} ended with stage={stage}{detail}. Check logs: hf jobs logs {job_id}"
+        )
+    return True
+
+
 def _pod_forwarded_args(
    argv: list[str], drop_names: tuple[str, ...] = (), drop_prefixes: tuple[str, ...] = ()
 ) -> list[str]:
@@ -362,64 +430,11 @@ def submit_to_hf(cfg: TrainPipelineConfig) -> None:
    print(f"  Monitor:    hf jobs logs {job_id}")
    print(f"  Cancel:     hf jobs cancel {job_id}")

-    if cfg.job.detach:
-        return
-
-    done = threading.Event()
-    detached = threading.Event()
-    pushed_ok = threading.Event()
-    stage_holder: dict[str, str | None] = {}
-
-    def _poll() -> None:
-        stage_holder["stage"] = _poll_until_done(job_id, done, status_holder=stage_holder)
-
-    poll_thread = threading.Thread(target=_poll, daemon=True)
-    poll_thread.start()
    # Finish as soon as the model is pushed, rather than waiting out the platform's
    # post-run finalization before the job stage flips to COMPLETED. This matches the
    # exact log line emitted by PreTrainedPolicy.push_model_to_hub — the two must stay
    # in sync. If it ever stops matching we just fall back to stage-based completion
    # (~30s slower), so the contract is an optimization, not a correctness requirement.
    success_marker = f"Model pushed to https://huggingface.co/{repo_id}"
-    log_thread = threading.Thread(
-        target=_tail_logs, args=(job_id, done, success_marker, pushed_ok), daemon=True
-    )
-    log_thread.start()
-
-    def _detach(sig, frame):
-        detached.set()
-        done.set()
-        print("\nDetached. Job is still running.")
-        print(f"  Monitor: hf jobs logs {job_id}")
-        print(f"  Cancel:  hf jobs cancel {job_id}")
-
-    # signal.signal only works on the main thread; when called from a worker thread
-    # (e.g. an orchestration framework) skip the Ctrl-C-detaches-instead-of-cancels
-    # handler rather than crashing with ValueError.
-    install_sigint = threading.current_thread() is threading.main_thread()
-    original_sigint = signal.getsignal(signal.SIGINT) if install_sigint else None
-    if install_sigint:
-        signal.signal(signal.SIGINT, _detach)
-    try:
-        # Timeout-based join so SIGINT is delivered to the main thread promptly.
-        while poll_thread.is_alive():
-            poll_thread.join(timeout=0.5)
-        log_thread.join(timeout=5)
-    finally:
-        if install_sigint:
-            signal.signal(signal.SIGINT, original_sigint)
-
-    if detached.is_set():
-        return
-
-    if pushed_ok.is_set():
+    if follow_job(job_id, detach=cfg.job.detach, success_marker=success_marker):
        print(f"\nTraining complete — model pushed to https://huggingface.co/{repo_id}")
-        return
-
-    stage = stage_holder.get("stage")
-    if stage != "COMPLETED":
-        message = stage_holder.get("message")
-        detail = f" ({message})" if message else ""
-        raise RuntimeError(
-            f"Job {job_id} ended with stage={stage}{detail}. Check logs: hf jobs logs {job_id}"
-        )
@@ -20,7 +20,6 @@ import logging
 import time
 from contextlib import contextmanager
 from copy import deepcopy
-from functools import cached_property
 from typing import TYPE_CHECKING, Any, TypedDict

 from lerobot.utils.decorators import check_if_already_connected, check_if_not_connected
@@ -854,7 +853,7 @@ class DamiaoMotorsBus(MotorsBusBase):
        else:
            raise ValueError(f"Motor {motor_obj} doesn't have a valid recv_id (None).")

-    @cached_property
+    @property
    def is_calibrated(self) -> bool:
        """Check if motors are calibrated."""
        return bool(self.calibration)
@@ -23,6 +23,7 @@ from __future__ import annotations

 import abc
 import logging
+import time
 from collections.abc import Sequence
 from contextlib import contextmanager
 from dataclasses import dataclass
@@ -818,13 +819,13 @@ class SerialMotorsBus(MotorsBusBase):
        """
        motor_names = self._get_motors_list(motors)

-        start_positions = self.sync_read("Present_Position", motor_names, normalize=False)
+        start_positions = self.sync_read("Present_Position", motor_names, normalize=False, num_retry=5)
        mins = start_positions.copy()
        maxes = start_positions.copy()

        user_pressed_enter = False
        while not user_pressed_enter:
-            positions = self.sync_read("Present_Position", motor_names, normalize=False)
+            positions = self.sync_read("Present_Position", motor_names, normalize=False, num_retry=5)
            mins = {motor: min(positions[motor], min_) for motor, min_ in mins.items()}
            maxes = {motor: max(positions[motor], max_) for motor, max_ in maxes.items()}

@@ -837,9 +838,12 @@ class SerialMotorsBus(MotorsBusBase):
            if enter_pressed():
                user_pressed_enter = True

-            if display_values and not user_pressed_enter:
-                # Move cursor up to overwrite the previous output
-                move_cursor_up(len(motor_names) + 3)
+            if not user_pressed_enter:
+                if display_values:
+                    # Move cursor up to overwrite the previous output
+                    move_cursor_up(len(motor_names) + 3)
+                # Throttle reads even when the live table is disabled.
+                time.sleep(0.02)

        same_min_max = [motor for motor in motor_names if mins[motor] == maxes[motor]]
        if same_min_max:
@@ -79,6 +79,8 @@ class DiffusionConfig(PreTrainedConfig):
        use_film_scale_modulation: FiLM (https://huggingface.co/papers/1709.07871) is used for the Unet conditioning.
            Bias modulation is used be default, while this parameter indicates whether to also use scale
            modulation.
+        gradient_checkpointing: Whether to checkpoint the Unet residual blocks during training. This reduces
+            activation memory at the cost of recomputing those blocks during the backward pass.
        noise_scheduler_type: Name of the noise scheduler to use. Supported options: ["DDPM", "DDIM"].
        num_train_timesteps: Number of diffusion steps for the forward diffusion schedule.
        beta_schedule: Name of the diffusion beta schedule as per DDPMScheduler from Hugging Face diffusers.
@@ -132,6 +134,7 @@ class DiffusionConfig(PreTrainedConfig):
    n_groups: int = 8
    diffusion_step_embed_dim: int = 128
    use_film_scale_modulation: bool = True
+    gradient_checkpointing: bool = False
    # Noise scheduler.
    noise_scheduler_type: str = "DDPM"
    num_train_timesteps: int = 100
@@ -31,6 +31,7 @@ import torch
 import torch.nn.functional as F  # noqa: N812
 import torchvision
 from torch import Tensor, nn
+from torch.utils.checkpoint import checkpoint

 from lerobot.utils.constants import ACTION, OBS_ENV_STATE, OBS_IMAGES, OBS_STATE
 from lerobot.utils.import_utils import _diffusers_available, require_package
@@ -727,22 +728,35 @@ class DiffusionConditionalUnet1d(nn.Module):
        else:
            global_feature = timesteps_embed

+        use_gc = self.config.gradient_checkpointing and self.training
+
        # Run encoder, keeping track of skip features to pass to the decoder.
        encoder_skip_features: list[Tensor] = []
        for resnet, resnet2, downsample in self.down_modules:
-            x = resnet(x, global_feature)
-            x = resnet2(x, global_feature)
+            if use_gc:
+                x = checkpoint(resnet, x, global_feature, use_reentrant=False)
+                x = checkpoint(resnet2, x, global_feature, use_reentrant=False)
+            else:
+                x = resnet(x, global_feature)
+                x = resnet2(x, global_feature)
            encoder_skip_features.append(x)
            x = downsample(x)

        for mid_module in self.mid_modules:
-            x = mid_module(x, global_feature)
+            if use_gc:
+                x = checkpoint(mid_module, x, global_feature, use_reentrant=False)
+            else:
+                x = mid_module(x, global_feature)

        # Run decoder, using the skip features from the encoder.
        for resnet, resnet2, upsample in self.up_modules:
            x = torch.cat((x, encoder_skip_features.pop()), dim=1)
-            x = resnet(x, global_feature)
-            x = resnet2(x, global_feature)
+            if use_gc:
+                x = checkpoint(resnet, x, global_feature, use_reentrant=False)
+                x = checkpoint(resnet2, x, global_feature, use_reentrant=False)
+            else:
+                x = resnet(x, global_feature)
+                x = resnet2(x, global_feature)
            x = upsample(x)

        x = self.final_conv(x)
@@ -177,6 +177,7 @@ def make_pre_post_processors(
            return make_groot_pre_post_processors_from_pretrained(
                config=policy_cfg,
                pretrained_path=pretrained_path,
+                revision=pretrained_revision,
                dataset_stats=kwargs.get("dataset_stats"),
                dataset_meta=kwargs.get("dataset_meta"),
                preprocessor_overrides=kwargs.get("preprocessor_overrides"),
@@ -475,6 +475,7 @@ def make_groot_pre_post_processors_from_pretrained(
    config: GrootConfig,
    pretrained_path: str,
    *,
+    revision: str | None = None,
    dataset_stats: dict[str, dict[str, torch.Tensor]] | None = None,
    dataset_meta: Any | None = None,
    preprocessor_overrides: dict[str, Any] | None = None,
@@ -511,6 +512,7 @@ def make_groot_pre_post_processors_from_pretrained(

    preprocessor, postprocessor = _load_groot_processor_pipelines(
        pretrained_path,
+        revision=revision,
        preprocessor_overrides=preprocessor_overrides,
        postprocessor_overrides=postprocessor_overrides,
        preprocessor_config_filename=preprocessor_config_filename,
@@ -526,6 +528,7 @@ def make_groot_pre_post_processors_from_pretrained(
 def _load_groot_processor_pipelines(
    pretrained_path: str,
    *,
+    revision: str | None,
    preprocessor_overrides: dict[str, Any],
    postprocessor_overrides: dict[str, Any],
    preprocessor_config_filename: str,
@@ -540,6 +543,7 @@ def _load_groot_processor_pipelines(
    preprocessor = PolicyProcessorPipeline.from_pretrained(
        pretrained_model_name_or_path=pretrained_path,
        config_filename=preprocessor_config_filename,
+        revision=revision,
        overrides=preprocessor_overrides,
        to_transition=batch_to_transition,
        to_output=transition_to_batch,
@@ -547,6 +551,7 @@ def _load_groot_processor_pipelines(
    postprocessor = PolicyProcessorPipeline.from_pretrained(
        pretrained_model_name_or_path=pretrained_path,
        config_filename=postprocessor_config_filename,
+        revision=revision,
        overrides=postprocessor_overrides,
        to_transition=policy_action_to_transition,
        to_output=transition_to_policy_action,
@@ -524,8 +524,6 @@ class PI05Pytorch(nn.Module):  # see openpi `PI0Pytorch`

    def embed_suffix(self, noisy_actions, timestep):
        """Embed noisy_actions, timestep to prepare for Expert Gemma processing."""
-        embs = []
-        pad_masks = []
        att_masks = []

        # Embed timestep using sine-cosine positional encoding
@@ -551,23 +549,17 @@ class PI05Pytorch(nn.Module):  # see openpi `PI0Pytorch`
            return F.silu(x)

        time_emb = self._apply_checkpoint(time_mlp_func, time_emb)
-        action_time_emb = action_emb
        adarms_cond = time_emb

-        embs.append(action_time_emb)
-        bsize, action_time_dim = action_time_emb.shape[:2]
-        action_time_mask = torch.ones(bsize, action_time_dim, dtype=torch.bool, device=timestep.device)
-        pad_masks.append(action_time_mask)
+        bsize, action_time_dim = action_emb.shape[:2]
+        pad_masks = torch.ones(bsize, action_time_dim, dtype=torch.bool, device=timestep.device)

        # Set attention masks so that image, language and state inputs do not attend to action tokens
        att_masks += [1] + ([0] * (self.config.chunk_size - 1))
-
-        embs = torch.cat(embs, dim=1)
-        pad_masks = torch.cat(pad_masks, dim=1)
-        att_masks = torch.tensor(att_masks, dtype=embs.dtype, device=embs.device)
+        att_masks = torch.tensor(att_masks, dtype=action_emb.dtype, device=action_emb.device)
        att_masks = att_masks[None, :].expand(bsize, len(att_masks))

-        return embs, pad_masks, att_masks, adarms_cond
+        return action_emb, pad_masks, att_masks, adarms_cond

    def forward(self, images, img_masks, tokens, masks, actions, noise, time) -> Tensor:
        """Do a full training forward pass and compute the loss."""
@@ -236,16 +236,11 @@ class ActionQueue:
        if action_index_before_inference is not None:
            indexes_diff = max(0, self.last_index - action_index_before_inference)
            if indexes_diff != real_delay:
-                # take the min of both to avoid discarding actions that were not
-                # actually consumed during inference, which would cause a jump in the queue
-                resolved = min(real_delay, indexes_diff)
                logger.warning(
-                    "Indexes diff != real delay (indexes_diff=%d, real_delay=%d); "
-                    "clamping discard to %d to avoid a queue-splice jump.",
+                    "Indexes diff is not equal to real delay. indexes_diff=%d, real_delay=%d",
                    indexes_diff,
                    real_delay,
-                    resolved,
                )
-                return resolved
+                return real_delay

        return effective_delay
@@ -713,6 +713,8 @@ class DataProcessorPipeline[TInput, TOutput](HubMixin):
            ProcessorMigrationError: If the model requires migration to processor format.
        """
        model_id = str(pretrained_model_name_or_path)
+        model_path = Path(model_id)
+        is_local_source = model_path.is_dir() or model_path.is_file()
        hub_download_kwargs = {
            "force_download": force_download,
            "resume_download": resume_download,
@@ -731,7 +733,7 @@ class DataProcessorPipeline[TInput, TOutput](HubMixin):

        # 3. Build steps with overrides
        steps, validated_overrides = cls._build_steps_with_overrides(
-            loaded_config, overrides or {}, model_id, base_path, hub_download_kwargs
+            loaded_config, overrides or {}, model_id, base_path, hub_download_kwargs, is_local_source
        )

        # 4. Validate that all overrides were used
@@ -921,6 +923,7 @@ class DataProcessorPipeline[TInput, TOutput](HubMixin):
        model_id: str,
        base_path: Path | None,
        hub_download_kwargs: dict[str, Any],
+        is_local_source: bool = False,
    ) -> tuple[list[ProcessorStep], set[str]]:
        """Build all processor steps with overrides and state loading.

@@ -944,7 +947,7 @@ class DataProcessorPipeline[TInput, TOutput](HubMixin):
        3. **State Loading** (via _load_step_state):
           - **If step has "state_file"**: Load tensor state from .safetensors
           - **Local first**: Check base_path/state_file.safetensors
-           - **Hub fallback**: Download state file if not found locally
+           - **Hub fallback**: Download state file if the pipeline was loaded from the Hub
           - **Optional**: Only load if step has load_state_dict method

        4. **Override Tracking**:
@@ -962,6 +965,7 @@ class DataProcessorPipeline[TInput, TOutput](HubMixin):
            model_id: The model identifier (needed for Hub state file downloads)
            base_path: Local directory path for finding state files
            hub_download_kwargs: Parameters for hf_hub_download (tokens, cache, etc.)
+            is_local_source: Whether model_id resolved to a local directory or config file.

        Returns:
            Tuple of (instantiated_steps_list, unused_override_keys)
@@ -975,7 +979,9 @@ class DataProcessorPipeline[TInput, TOutput](HubMixin):
        steps, remaining_override_keys = cls._build_steps_from_config(loaded_config, overrides)

        for step_instance, step_entry in zip(steps, loaded_config["steps"], strict=True):
-            cls._load_step_state(step_instance, step_entry, model_id, base_path, hub_download_kwargs)
+            cls._load_step_state(
+                step_instance, step_entry, model_id, base_path, hub_download_kwargs, is_local_source
+            )

        return steps, remaining_override_keys

@@ -1139,6 +1145,7 @@ class DataProcessorPipeline[TInput, TOutput](HubMixin):
        model_id: str,
        base_path: Path | None,
        hub_download_kwargs: dict[str, Any],
+        is_local_source: bool = False,
    ) -> None:
        """Load state dictionary for a processor step if available.

@@ -1157,7 +1164,7 @@ class DataProcessorPipeline[TInput, TOutput](HubMixin):
           - **Use case**: Loading from local saved model directory

        2. **Hub download fallback**: Download state file from repository
-           - **When triggered**: Local file not found or base_path is None
+           - **When triggered**: Local file not found and the pipeline source is a Hub repo
           - **Process**: Use hf_hub_download with same parameters as config
           - **Example**: Download "normalize_step_0.safetensors" from "user/repo"
           - **Result**: Downloaded to local cache, path returned
@@ -1178,6 +1185,7 @@ class DataProcessorPipeline[TInput, TOutput](HubMixin):
            model_id: The model identifier (used for Hub downloads if needed)
            base_path: Local directory path for finding state files (None for Hub-only)
            hub_download_kwargs: Parameters for hf_hub_download (tokens, cache, etc.)
+            is_local_source: Whether model_id resolved to a local directory or config file.

        Note:
            This method modifies step_instance in-place and returns None.
@@ -1191,6 +1199,12 @@ class DataProcessorPipeline[TInput, TOutput](HubMixin):
        # Try local file first
        if base_path and (base_path / state_filename).exists():
            state_path = str(base_path / state_filename)
+        elif is_local_source:
+            state_path = base_path / state_filename if base_path else Path(state_filename)
+            raise FileNotFoundError(
+                f"State file '{state_filename}' was not found for local processor pipeline "
+                f"'{model_id}' at '{state_path}'."
+            )
        else:
            # Download from Hub
            state_path = hf_hub_download(
@@ -58,6 +58,9 @@ class BiSOFollower(BimanualMixin, Robot):
            port=config.left_arm_config.port,
            disable_torque_on_disconnect=config.left_arm_config.disable_torque_on_disconnect,
            max_relative_target=config.left_arm_config.max_relative_target,
+            position_p_coefficient=config.left_arm_config.position_p_coefficient,
+            position_i_coefficient=config.left_arm_config.position_i_coefficient,
+            position_d_coefficient=config.left_arm_config.position_d_coefficient,
            use_degrees=config.left_arm_config.use_degrees,
            cameras=left_arm_cameras,
        )
@@ -68,6 +71,9 @@ class BiSOFollower(BimanualMixin, Robot):
            port=config.right_arm_config.port,
            disable_torque_on_disconnect=config.right_arm_config.disable_torque_on_disconnect,
            max_relative_target=config.right_arm_config.max_relative_target,
+            position_p_coefficient=config.right_arm_config.position_p_coefficient,
+            position_i_coefficient=config.right_arm_config.position_i_coefficient,
+            position_d_coefficient=config.right_arm_config.position_d_coefficient,
            use_degrees=config.right_arm_config.use_degrees,
            cameras=config.right_arm_config.cameras,
        )
@@ -323,6 +323,10 @@ class LeKiwiClient(Robot):
            np.ndarray: the action sent to the motors, potentially clipped.
        """

+        # Action values may be torch tensors (e.g. replayed from a dataset) or numpy
+        # scalars; json.dumps only serializes Python primitives, so coerce each value to a
+        # plain float before sending.
+        action = {key: float(value) for key, value in action.items()}
        self.zmq_cmd_socket.send_string(json.dumps(action))  # action is in motor space

        # TODO(Steven): Remove the np conversion when it is possible to record a non-numpy array value
@@ -150,9 +150,6 @@ class OpenArmFollower(Robot):

        self.configure()

-        if self.is_calibrated:
-            self.bus.set_zero_position()
-
        self.bus.enable_torque()

        logger.info(f"{self} connected.")
@@ -41,6 +41,11 @@ class SOFollowerConfig:
    # Set to `True` for backward compatibility with previous policies/dataset
    use_degrees: bool = True

+    # Position-mode PID gains written to Feetech STS3215 motors at connect time.
+    position_p_coefficient: int = 16
+    position_i_coefficient: int = 0
+    position_d_coefficient: int = 32
+

@RobotConfig.register_subclass("so101_follower")
@RobotConfig.register_subclass("so100_follower")
@@ -161,11 +161,9 @@ class SOFollower(Robot):
            self.bus.configure_motors()
            for motor in self.bus.motors:
                self.bus.write("Operating_Mode", motor, OperatingMode.POSITION.value)
-                # Set P_Coefficient to lower value to avoid shakiness (Default is 32)
-                self.bus.write("P_Coefficient", motor, 16)
-                # Set I_Coefficient and D_Coefficient to default value 0 and 32
-                self.bus.write("I_Coefficient", motor, 0)
-                self.bus.write("D_Coefficient", motor, 32)
+                self.bus.write("P_Coefficient", motor, self.config.position_p_coefficient)
+                self.bus.write("I_Coefficient", motor, self.config.position_i_coefficient)
+                self.bus.write("D_Coefficient", motor, self.config.position_d_coefficient)

                if motor == "gripper":
                    self.bus.write("Max_Torque_Limit", motor, 500)  # 50% of max torque to avoid burnout
@@ -326,8 +326,17 @@ class RolloutConfig:

        policy_path = parser.get_path_arg("policy")
        if policy_path:
-            cli_overrides = parser.get_cli_overrides("policy")
-            self.policy = PreTrainedConfig.from_pretrained(policy_path, cli_overrides=cli_overrides)
+            yaml_overrides = parser.get_yaml_overrides("policy")
+            cli_overrides = parser.get_cli_overrides("policy") or []
+            policy_overrides = yaml_overrides + cli_overrides
+            pretrained_revision = parser.parse_arg("pretrained_revision", cli_overrides)
+            if pretrained_revision is None:
+                pretrained_revision = parser.parse_arg("pretrained_revision", yaml_overrides)
+            self.policy = PreTrainedConfig.from_pretrained(
+                policy_path,
+                revision=pretrained_revision,
+                cli_overrides=policy_overrides,
+            )
            self.policy.pretrained_path = policy_path
        if self.policy is None:
            raise ValueError("--policy.path is required for rollout")
@@ -27,7 +27,7 @@ from threading import Event

 import torch

-from lerobot.configs import FeatureType
+from lerobot.configs import FeatureType, PreTrainedConfig
 from lerobot.datasets import (
    LeRobotDataset,
    aggregate_pipeline_dataset_features,
@@ -159,6 +159,35 @@ class RolloutContext:
 # ---------------------------------------------------------------------------


+def _load_pretrained_policy(policy_config: PreTrainedConfig) -> PreTrainedPolicy:
+    """Load policy weights, keeping adapter and base-model revisions independent."""
+    pretrained_revision = policy_config.pretrained_revision
+    policy_class = get_policy_class(policy_config.type)
+
+    if not policy_config.use_peft:
+        return policy_class.from_pretrained(
+            policy_config.pretrained_path,
+            config=policy_config,
+            revision=pretrained_revision,
+        )
+
+    from peft import PeftConfig, PeftModel
+
+    peft_path = policy_config.pretrained_path
+    peft_config = PeftConfig.from_pretrained(peft_path, revision=pretrained_revision)
+    policy = policy_class.from_pretrained(
+        pretrained_name_or_path=peft_config.base_model_name_or_path,
+        config=policy_config,
+        revision=peft_config.revision,
+    )
+    return PeftModel.from_pretrained(
+        policy,
+        peft_path,
+        config=peft_config,
+        revision=pretrained_revision,
+    )
+
+
 def build_rollout_context(
    cfg: RolloutConfig,
    shutdown_event: Event,
@@ -176,7 +205,6 @@ def build_rollout_context(
    # --- 1. Policy (heavy I/O, but no hardware yet) -------------------
    logger.info("Loading policy from '%s'...", cfg.policy.pretrained_path)
    policy_config = cfg.policy
-    policy_class = get_policy_class(policy_config.type)

    if hasattr(policy_config, "compile_model"):
        policy_config.compile_model = cfg.use_torch_compile
@@ -187,17 +215,7 @@ def build_rollout_context(
            "Please use `cpu` or `cuda` backend."
        )

-    if policy_config.use_peft:
-        from peft import PeftConfig, PeftModel
-
-        peft_path = policy_config.pretrained_path
-        peft_config = PeftConfig.from_pretrained(peft_path)
-        policy = policy_class.from_pretrained(
-            pretrained_name_or_path=peft_config.base_model_name_or_path, config=policy_config
-        )
-        policy = PeftModel.from_pretrained(policy, peft_path, config=peft_config)
-    else:
-        policy = policy_class.from_pretrained(policy_config.pretrained_path, config=policy_config)
+    policy = _load_pretrained_policy(policy_config)

    if is_rtc:
        policy.config.rtc_config = cfg.inference.rtc
@@ -392,6 +410,7 @@ def build_rollout_context(
    preprocessor, postprocessor = make_pre_post_processors(
        policy_cfg=policy_config,
        pretrained_path=cfg.policy.pretrained_path,
+        pretrained_revision=policy_config.pretrained_revision,
        dataset_stats=dataset_stats,
        preprocessor_overrides={
            "device_processor": {"device": cfg.device},
@@ -116,8 +116,7 @@ def create_inference_engine(
            postprocessor=postprocessor,
            robot_wrapper=robot_wrapper,
            rtc_config=config.rtc,
-            dataset_features=dataset_features,
-            ordered_action_keys=ordered_action_keys,
+            hw_features=hw_features,
            task=task,
            fps=fps,
            device=device,
@@ -34,7 +34,7 @@ import torch
 from lerobot.policies.pretrained import PreTrainedPolicy
 from lerobot.policies.rtc import ActionQueue, LatencyTracker, reanchor_relative_rtc_prefix
 from lerobot.policies.rtc.configuration_rtc import RTCConfig
-from lerobot.policies.utils import make_robot_action, prepare_observation_for_inference
+from lerobot.policies.utils import prepare_observation_for_inference
 from lerobot.processor import (
    NormalizerProcessorStep,
    PolicyProcessorPipeline,
@@ -66,16 +66,14 @@ def _normalize_prev_actions_length(prev_actions: torch.Tensor, target_steps: int
    """Pad or truncate RTC prefix actions to a fixed length for stable compiled inference."""
    if prev_actions.ndim != 2:
        raise ValueError(f"Expected 2D [T, A] tensor, got shape={tuple(prev_actions.shape)}")
-    steps, _ = prev_actions.shape
+    steps, action_dim = prev_actions.shape
    if steps == target_steps:
        return prev_actions
    if steps > target_steps:
        return prev_actions[:target_steps]
-    if steps == 0:
-        raise ValueError("Cannot pad an empty prefix: no last action to hold.")
-    # repeat the last action to fill the remaining steps to avoid a sudden jump
-    hold = prev_actions[-1:].expand(target_steps - steps, -1)
-    return torch.cat([prev_actions, hold], dim=0)
+    padded = torch.zeros((target_steps, action_dim), dtype=prev_actions.dtype, device=prev_actions.device)
+    padded[:steps] = prev_actions
+    return padded


 # ---------------------------------------------------------------------------
@@ -99,8 +97,7 @@ class RTCInferenceEngine(InferenceEngine):
        postprocessor: PolicyProcessorPipeline,
        robot_wrapper: ThreadSafeRobot,
        rtc_config: RTCConfig,
-        dataset_features: dict,
-        ordered_action_keys: list[str],
+        hw_features: dict,
        task: str,
        fps: float,
        device: str | None,
@@ -114,8 +111,7 @@ class RTCInferenceEngine(InferenceEngine):
        self._postprocessor = postprocessor
        self._robot = robot_wrapper
        self._rtc_config = rtc_config
-        self._obs_features = dataset_features
-        self._ordered_action_keys = ordered_action_keys
+        self._hw_features = hw_features
        self._task = task
        self._fps = fps
        self._device = device or "cpu"
@@ -237,13 +233,7 @@ class RTCInferenceEngine(InferenceEngine):
        """Pop the next action from the RTC queue (ignores ``obs_frame``)."""
        if self._action_queue is None:
            return None
-        action = self._action_queue.get()
-        if action is None:
-            return None
-
-        # properly reorder the action dict to match the robot's expected order
-        action_dict = make_robot_action(action, self._obs_features)
-        return torch.tensor([action_dict[k] for k in self._ordered_action_keys])
+        return self._action_queue.get()

    def notify_observation(self, obs: dict) -> None:
        """Publish the latest observation for the RTC thread to consume."""
@@ -262,8 +252,6 @@ class RTCInferenceEngine(InferenceEngine):
            policy_device = torch.device(self._device)

            warmup_required = max(1, self._compile_warmup_inferences) if self._use_torch_compile else 0
-            # exclude the first N inferences from the latency tracker to avoid cold-start spikes
-            latency_warmup_required = max(1, warmup_required)
            inference_count = 0
            consecutive_errors = 0

@@ -288,7 +276,7 @@ class RTCInferenceEngine(InferenceEngine):
                        latency = latency_tracker.max()
                        delay = math.ceil(latency / time_per_chunk) if latency else 0

-                        obs_batch = build_dataset_frame(self._obs_features, obs, prefix="observation")
+                        obs_batch = build_dataset_frame(self._hw_features, obs, prefix="observation")
                        obs_batch = prepare_observation_for_inference(
                            obs_batch, policy_device, self._task, self._robot.robot_type
                        )
@@ -328,8 +316,7 @@ class RTCInferenceEngine(InferenceEngine):
                        inference_count += 1
                        consecutive_errors = 0
                        is_warmup = self._use_torch_compile and inference_count <= warmup_required
-                        # Ignore the first N inferences for latency tracking to avoid cold-start spikes
-                        if inference_count <= latency_warmup_required:
+                        if is_warmup:
                            latency_tracker.reset()
                        else:
                            latency_tracker.add(new_latency)
@@ -61,6 +61,7 @@ import pyarrow as pa
 import tqdm
 from datasets import Dataset, Features, Image
 from huggingface_hub import HfApi, snapshot_download
+from huggingface_hub.errors import RevisionNotFoundError
 from requests import HTTPError

 from lerobot.datasets import CODEBASE_VERSION, LeRobotDataset, aggregate_stats
@@ -521,7 +522,7 @@ def convert_dataset(
        hub_api = HfApi()
        try:
            hub_api.delete_tag(repo_id, tag=CODEBASE_VERSION, repo_type="dataset")
-        except HTTPError as e:
+        except (HTTPError, RevisionNotFoundError) as e:
            print(f"tag={CODEBASE_VERSION} probably doesn't exist. Skipping exception ({e})")
            pass
        hub_api.delete_files(
@@ -24,7 +24,14 @@ Example:
      --root=/path/to/dataset \\
      --vlm.model_id=Qwen/Qwen2.5-VL-7B-Instruct

-For distributed runs, see ``examples/annotations/run_hf_job.py``.
+Pass ``--job.target=<flavor>`` to run the same command on a Hugging Face
+Jobs GPU instead of this machine (see ``lerobot.jobs.annotate``):
+
+  uv run lerobot-annotate \\
+      --repo_id=user/dataset \\
+      --new_repo_id=user/dataset_annotated \\
+      --push_to_hub=true \\
+      --job.target=h200
 """

 import logging
@@ -69,6 +76,14 @@ def _resolve_root(cfg: AnnotationPipelineConfig) -> Path:
 def annotate(cfg: AnnotationPipelineConfig) -> None:
    """Run the steerable annotation pipeline against a dataset."""
    logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s")
+
+    if cfg.job.is_remote:
+        # Imported lazily: the submitter pulls in LeRobotDataset (the `dataset`
+        # extra), which a local annotation run over --root doesn't need.
+        from lerobot.jobs.annotate import submit_annotate_to_hf
+
+        return submit_annotate_to_hf(cfg)
+
    root = _resolve_root(cfg)
    logger.info("annotate: root=%s", root)

@@ -453,6 +453,9 @@ def eval_policy(
            raise exc from None

    start = time.time()
+    # Preserve the mode for direct callers. eval_policy_all scopes the mode
+    # around all tasks so parallel evaluations cannot race with each other.
+    was_training = policy.training
    policy.eval()

    # Determine how many batched rollouts we need to get n_episodes. Note that if n_episodes is not evenly
@@ -674,6 +677,8 @@ def eval_policy(
    if save_predicted_video:
        info["predicted_video_paths"] = predicted_video_paths

+    policy.train(was_training)
+
    return info


@@ -1010,40 +1015,48 @@ def eval_policy_all(
        recording_private=recording_private,
    )

-    if max_parallel_tasks <= 1:
-        prefetch_thread: threading.Thread | None = None
-        for i, (task_group, task_id, env) in enumerate(tasks):
-            if prefetch_thread is not None:
-                prefetch_thread.join()
-                prefetch_thread = None
+    # Set the shared policy's mode before launching any workers. Restoring it
+    # inside individual tasks would let one task enable training mode while
+    # another task is still evaluating.
+    was_training = policy.training
+    policy.eval()
+    try:
+        if max_parallel_tasks <= 1:
+            prefetch_thread: threading.Thread | None = None
+            for i, (task_group, task_id, env) in enumerate(tasks):
+                if prefetch_thread is not None:
+                    prefetch_thread.join()
+                    prefetch_thread = None

-            try:
-                tg, tid, metrics = task_runner(task_group, task_id, env)
-                _accumulate_to(tg, metrics)
-                per_task_infos.append({"task_group": tg, "task_id": tid, "metrics": metrics})
-            finally:
-                env.close()
-                # Prefetch next task's workers *after* closing current env to prevent
-                # GPU memory overlap between consecutive tasks.
-                if i + 1 < len(tasks):
-                    next_env = tasks[i + 1][2]
-                    if hasattr(next_env, "_ensure"):
-                        prefetch_thread = threading.Thread(target=next_env._ensure, daemon=True)
-                        prefetch_thread.start()
-    else:
-        with cf.ThreadPoolExecutor(max_workers=max_parallel_tasks) as executor:
-            fut2meta = {}
-            for task_group, task_id, env in tasks:
-                fut = executor.submit(task_runner, task_group, task_id, env)
-                fut2meta[fut] = (task_group, task_id, env)
-            for fut in cf.as_completed(fut2meta):
-                tg, tid, env = fut2meta[fut]
                try:
-                    tg, tid, metrics = fut.result()
+                    tg, tid, metrics = task_runner(task_group, task_id, env)
                    _accumulate_to(tg, metrics)
                    per_task_infos.append({"task_group": tg, "task_id": tid, "metrics": metrics})
                finally:
                    env.close()
+                    # Prefetch next task's workers *after* closing current env to prevent
+                    # GPU memory overlap between consecutive tasks.
+                    if i + 1 < len(tasks):
+                        next_env = tasks[i + 1][2]
+                        if hasattr(next_env, "_ensure"):
+                            prefetch_thread = threading.Thread(target=next_env._ensure, daemon=True)
+                            prefetch_thread.start()
+        else:
+            with cf.ThreadPoolExecutor(max_workers=max_parallel_tasks) as executor:
+                fut2meta = {}
+                for task_group, task_id, env in tasks:
+                    fut = executor.submit(task_runner, task_group, task_id, env)
+                    fut2meta[fut] = (task_group, task_id, env)
+                for fut in cf.as_completed(fut2meta):
+                    tg, tid, env = fut2meta[fut]
+                    try:
+                        tg, tid, metrics = fut.result()
+                        _accumulate_to(tg, metrics)
+                        per_task_infos.append({"task_group": tg, "task_id": tid, "metrics": metrics})
+                    finally:
+                        env.close()
+    finally:
+        policy.train(was_training)

    # compute aggregated metrics helper (robust to lists/scalars)
    def _agg_from_list(xs):
@@ -453,9 +453,11 @@ def record(
                encoder_queue_maxsize=cfg.dataset.encoder_queue_maxsize,
            )

-        robot.connect()
+        # Connect the teleoperator before the robot so the robot isn't left idle (and possibly
+        # tripping a firmware watchdog) during teleop init. Matches lerobot_teleoperate.py.
        if teleop is not None:
            teleop.connect()
+        robot.connect()

        listener, events = init_keyboard_listener()

@@ -61,6 +61,7 @@ from lerobot.robots import (  # noqa: F401
    earthrover_mini_plus,
    hope_jr,
    koch_follower,
+    lekiwi,
    make_robot_from_config,
    omx_follower,
    openarm_follower,
@@ -51,19 +51,7 @@ from lerobot.teleoperators import (  # noqa: F401
    rebot_102_leader,
    so_leader,
 )
-
-COMPATIBLE_DEVICES = [
-    "koch_follower",
-    "koch_leader",
-    "omx_follower",
-    "omx_leader",
-    "openarm_mini",
-    "so100_follower",
-    "so100_leader",
-    "so101_follower",
-    "so101_leader",
-    "lekiwi",
-]
+from lerobot.utils.import_utils import register_third_party_plugins


@dataclass
@@ -80,18 +68,19 @@ class SetupConfig:

@draccus.wrap()
 def setup_motors(cfg: SetupConfig):
-    if cfg.device.type not in COMPATIBLE_DEVICES:
-        raise NotImplementedError
-
    if isinstance(cfg.device, RobotConfig):
        device = make_robot_from_config(cfg.device)
    else:
        device = make_teleoperator_from_config(cfg.device)

-    device.setup_motors()
+    setup = getattr(device, "setup_motors", None)
+    if not callable(setup):
+        raise NotImplementedError(f"Device type '{cfg.device.type}' does not support motor setup.")
+    setup()


 def main():
+    register_third_party_plugins()
    setup_motors()


@@ -71,6 +71,16 @@ from lerobot.utils.utils import (
 from .lerobot_eval import eval_policy_all


+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
+    return {
+        "prefetch_factor": cfg.prefetch_factor if workers_enabled else None,
+        "persistent_workers": cfg.persistent_workers and workers_enabled,
+        "multiprocessing_context": cfg.dataloader_multiprocessing_context if workers_enabled else None,
+    }
+
+
 def update_policy(
    train_metrics: MetricsTracker,
    policy: PreTrainedPolicy,
@@ -473,8 +483,7 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
        pin_memory=device.type == "cuda",
        drop_last=False,
        collate_fn=collate_fn,
-        prefetch_factor=cfg.prefetch_factor if cfg.num_workers > 0 else None,
-        persistent_workers=cfg.persistent_workers and cfg.num_workers > 0,
+        **_dataloader_worker_kwargs(cfg),
    )

    # Build eval dataloader if a held-out split exists
@@ -500,8 +509,7 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
            pin_memory=device.type == "cuda",
            drop_last=False,
            collate_fn=eval_collate_fn,
-            prefetch_factor=cfg.prefetch_factor if cfg.num_workers > 0 else None,
-            persistent_workers=cfg.persistent_workers and cfg.num_workers > 0,
+            **_dataloader_worker_kwargs(cfg),
        )

    # Prepare everything with accelerator
@@ -23,3 +23,5 @@ from ..config import TeleoperatorConfig
@dataclass
 class GamepadTeleopConfig(TeleoperatorConfig):
    use_gripper: bool = True
+    # Use hidapi instead of pygame for controllers that pygame cannot detect reliably.
+    hidapi_fallback: bool = False
@@ -14,6 +14,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.

+import logging
 import sys
 from enum import IntEnum
 from typing import Any
@@ -27,6 +28,8 @@ from ..teleoperator import Teleoperator
 from ..utils import TeleopEvents
 from .configuration_gamepad import GamepadTeleopConfig

+logger = logging.getLogger(__name__)
+

 class GripperAction(IntEnum):
    CLOSE = 0
@@ -56,6 +59,13 @@ class GamepadTeleop(Teleoperator):

        self.gamepad = None

+        self.hidapi_fallback = config.hidapi_fallback
+        if sys.platform == "darwin" and not self.hidapi_fallback:
+            logger.warning(
+                "On macOS, pygame may not reliably detect input from some controllers. "
+                "If you experience issues, set `hidapi_fallback=true`."
+            )
+
    @property
    def action_features(self) -> dict:
        if self.config.use_gripper:
@@ -76,9 +86,7 @@ class GamepadTeleop(Teleoperator):
        return {}

    def connect(self) -> None:
-        # use HidApi for macos
-        if sys.platform == "darwin":
-            # NOTE: On macOS, pygame doesn’t reliably detect input from some controllers so we fall back to hidapi
+        if self.hidapi_fallback:
            from .gamepad_utils import GamepadControllerHID as Gamepad
        else:
            from .gamepad_utils import GamepadController as Gamepad
@@ -26,7 +26,7 @@ import cv2
 import numpy as np
 import pytest

-from lerobot.cameras.configs import Cv2Rotation
+from lerobot.cameras.configs import ColorMode, Cv2Rotation
 from lerobot.cameras.opencv import OpenCVCamera, OpenCVCameraConfig
 from lerobot.utils.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError

@@ -132,6 +132,28 @@ def test_read(index_or_path):
        assert isinstance(img, np.ndarray)


+@pytest.mark.parametrize("index_or_path", TEST_IMAGE_PATHS, ids=TEST_IMAGE_SIZES)
+def test_color_mode_conversion(index_or_path):
+    """RGB and BGR reads of the same frame must differ only by a channel-axis reversal."""
+    rgb_config = OpenCVCameraConfig(index_or_path=index_or_path, color_mode=ColorMode.RGB, warmup_s=0)
+    bgr_config = OpenCVCameraConfig(index_or_path=index_or_path, color_mode=ColorMode.BGR, warmup_s=0)
+    with OpenCVCamera(rgb_config) as rgb_cam:
+        rgb = rgb_cam.read()
+    with OpenCVCamera(bgr_config) as bgr_cam:
+        bgr = bgr_cam.read()
+
+    assert rgb.shape == bgr.shape
+    np.testing.assert_array_equal(rgb, bgr[..., ::-1])
+
+
+def test_postprocess_invalid_color_mode():
+    config = OpenCVCameraConfig(index_or_path=DEFAULT_PNG_FILE_PATH)
+    camera = OpenCVCamera(config)
+    camera.color_mode = "invalid"
+    with pytest.raises(ValueError):
+        camera._postprocess_image(np.zeros((120, 160, 3), dtype=np.uint8))
+
+
 def test_read_before_connect():
    config = OpenCVCameraConfig(index_or_path=DEFAULT_PNG_FILE_PATH)

@@ -22,6 +22,7 @@ import pytest

 pytest.importorskip("reachy2_sdk")

+from lerobot.cameras.configs import ColorMode
 from lerobot.cameras.reachy2_camera import Reachy2Camera, Reachy2CameraConfig
 from lerobot.utils.errors import DeviceNotConnectedError

@@ -33,28 +34,19 @@ PARAMS = [
 ]


-def _make_cam_manager_mock():
+def _make_cam_manager_mock(color_frame, depth_frame=None):
    c = MagicMock(name="CameraManagerMock")

    teleop = MagicMock(name="TeleopCam")
    teleop.width = 640
    teleop.height = 480
-    teleop.get_frame = MagicMock(
-        side_effect=lambda *_, **__: (
-            np.zeros((480, 640, 3), dtype=np.uint8),
-            time.time(),
-        )
-    )
+    teleop.get_frame = MagicMock(side_effect=lambda *_, **__: (color_frame, time.time()))

    depth = MagicMock(name="DepthCam")
    depth.width = 640
    depth.height = 480
-    depth.get_frame = MagicMock(
-        side_effect=lambda *_, **__: (
-            np.zeros((480, 640, 3), dtype=np.uint8),
-            time.time(),
-        )
-    )
+    depth.get_frame = MagicMock(side_effect=lambda *_, **__: (color_frame, time.time()))
+    depth.get_depth_frame = MagicMock(side_effect=lambda *_, **__: (depth_frame, time.time()))

    c.is_connected.return_value = True
    c.teleop = teleop
@@ -84,12 +76,14 @@ def _make_cam_manager_mock():
    # ids=["teleop-left", "teleop-right", "torso-rgb", "torso-depth"],
    ids=["teleop-left", "teleop-right", "torso-rgb"],
 )
-def camera(request):
+def camera(request, img_array_factory):
    name, image_type = request.param
+    color_frame = img_array_factory(height=480, width=640)
+    depth_frame = img_array_factory(height=480, width=640, channels=1, dtype=np.uint16)[..., 0]
    with (
        patch(
            "lerobot.cameras.reachy2_camera.reachy2_camera.CameraManager",
-            side_effect=lambda *a, **k: _make_cam_manager_mock(),
+            side_effect=lambda *a, **k: _make_cam_manager_mock(color_frame, depth_frame),
        ),
    ):
        config = Reachy2CameraConfig(name=name, image_type=image_type)
@@ -188,6 +182,41 @@ def test_read_latest_too_old(camera):
        _ = camera.read_latest(max_age_ms=0)  # immediately too old


+def test_color_mode_conversion(img_array_factory):
+    """teleop frames are native BGR: RGB reverses the channel axis, BGR is passed through."""
+    frame = img_array_factory(height=8, width=8)
+
+    outputs = {}
+    for color_mode in (ColorMode.RGB, ColorMode.BGR):
+        with patch(
+            "lerobot.cameras.reachy2_camera.reachy2_camera.CameraManager",
+            side_effect=lambda *a, **k: _make_cam_manager_mock(frame),
+        ):
+            cam = Reachy2Camera(Reachy2CameraConfig(name="teleop", image_type="left", color_mode=color_mode))
+            cam.connect()
+            outputs[color_mode] = cam.read()
+            cam.disconnect()
+
+    np.testing.assert_array_equal(outputs[ColorMode.BGR], frame)
+    np.testing.assert_array_equal(outputs[ColorMode.RGB], frame[..., ::-1])
+
+
+def test_depth_frame_not_color_converted(img_array_factory):
+    """A depth/depth frame must be returned as-is, without BGR<->RGB conversion."""
+    color_frame = img_array_factory(height=8, width=8)
+    depth = img_array_factory(height=8, width=8, channels=1, dtype=np.uint16)[..., 0]
+    with patch(
+        "lerobot.cameras.reachy2_camera.reachy2_camera.CameraManager",
+        side_effect=lambda *a, **k: _make_cam_manager_mock(color_frame, depth_frame=depth),
+    ):
+        cam = Reachy2Camera(Reachy2CameraConfig(name="depth", image_type="depth"))
+        cam.connect()
+        out = cam.read()
+        cam.disconnect()
+
+    np.testing.assert_array_equal(out, depth)
+
+
 def test_wrong_camera_name():
    with pytest.raises(ValueError):
        _ = Reachy2CameraConfig(name="wrong-name", image_type="left")
@@ -25,7 +25,7 @@ from unittest.mock import patch
 import numpy as np
 import pytest

-from lerobot.cameras.configs import Cv2Rotation
+from lerobot.cameras.configs import ColorMode, Cv2Rotation
 from lerobot.utils.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError

 pytest.importorskip("pyrealsense2")
@@ -109,6 +109,32 @@ def test_read_depth():
    assert isinstance(img, np.ndarray)


+# These exercise _postprocess_image directly rather than read(): the bag playback returns
+# non-deterministic frames we can't compare against, and the depth read() path is skipped
+# (see test_read_depth) with the current pyrealsense2 version.
+def test_color_mode_conversion(img_array_factory):
+    """RGB (native for RealSense) is passed through; BGR reverses the channel axis."""
+    color = img_array_factory(height=3, width=4)
+
+    outputs = {}
+    for color_mode in (ColorMode.RGB, ColorMode.BGR):
+        camera = RealSenseCamera(RealSenseCameraConfig(serial_number_or_name="042", color_mode=color_mode))
+        camera.capture_height, camera.capture_width = color.shape[:2]
+        outputs[color_mode] = camera._postprocess_image(color)
+
+    np.testing.assert_array_equal(outputs[ColorMode.RGB], color)
+    np.testing.assert_array_equal(outputs[ColorMode.BGR], color[..., ::-1])
+
+
+def test_depth_frame_not_color_converted(img_array_factory):
+    """Depth frames must bypass color conversion, even when a BGR color_mode is set."""
+    camera = RealSenseCamera(RealSenseCameraConfig(serial_number_or_name="042", color_mode=ColorMode.BGR))
+    depth = img_array_factory(height=3, width=4, channels=1, dtype=np.uint16)[..., 0]
+    camera.capture_height, camera.capture_width = depth.shape
+
+    np.testing.assert_array_equal(camera._postprocess_image(depth, depth_frame=True), depth)
+
+
 def test_read_before_connect():
    config = RealSenseCameraConfig(serial_number_or_name="042")
    camera = RealSenseCamera(config)
@@ -14,16 +14,21 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.

+from types import SimpleNamespace
+from unittest.mock import Mock
+
 import pytest
 import torch
+from packaging.version import Version

 pytest.importorskip("datasets", reason="datasets is required (install lerobot[dataset])")

 from datasets import Dataset  # noqa: E402
 from huggingface_hub import DatasetCard

+import lerobot.datasets.utils as dataset_utils
 from lerobot.datasets.io_utils import hf_transform_to_torch
-from lerobot.datasets.utils import create_lerobot_dataset_card
+from lerobot.datasets.utils import create_lerobot_dataset_card, get_repo_versions, get_safe_version
 from lerobot.utils.constants import ACTION, OBS_IMAGES
 from lerobot.utils.feature_utils import combine_feature_dicts

@@ -57,6 +62,30 @@ def test_default_parameters():
    ]


+@pytest.mark.parametrize("token", ["hf_test_token", True, False])
+def test_get_repo_versions_forwards_token(monkeypatch, token):
+    api = Mock()
+    api.list_repo_refs.return_value = SimpleNamespace(
+        branches=[SimpleNamespace(name="v3.0")],
+        tags=[],
+    )
+    hf_api = Mock(return_value=api)
+    monkeypatch.setattr(dataset_utils, "HfApi", hf_api)
+
+    assert get_repo_versions("private/repo", token=token) == [Version("3.0")]
+    hf_api.assert_called_once_with(token=token)
+    api.list_repo_refs.assert_called_once_with("private/repo", repo_type="dataset")
+
+
+@pytest.mark.parametrize("token", ["hf_test_token", True, False])
+def test_get_safe_version_forwards_token(monkeypatch, token):
+    get_versions = Mock(return_value=[Version("3.0")])
+    monkeypatch.setattr(dataset_utils, "get_repo_versions", get_versions)
+
+    assert get_safe_version("private/repo", "v3.0", token=token) == "v3.0"
+    get_versions.assert_called_once_with("private/repo", token=token)
+
+
 def test_with_tags():
    tags = ["tag1", "tag2"]
    card = create_lerobot_dataset_card(tags=tags)
@@ -204,6 +204,38 @@ def test_clear_resets_buffer(tmp_path):
    assert dataset.writer.episode_buffer["size"] == 0


+def test_clear_removes_video_frame_staging_dir(tmp_path):
+    """clear_episode_buffer() removes PNG staging dirs for video features."""
+    video_key = "observation.images.cam"
+    features = {
+        video_key: {
+            "dtype": "video",
+            "shape": (64, 96, 3),
+            "names": ["height", "width", "channels"],
+        },
+        "action": {"dtype": "float32", "shape": (2,), "names": None},
+    }
+    dataset = LeRobotDataset.create(
+        repo_id=DUMMY_REPO_ID,
+        fps=DEFAULT_FPS,
+        features=features,
+        root=tmp_path / "ds",
+        use_videos=True,
+    )
+
+    dataset.add_frame(_make_frame(features))
+    video_staging_dir = (
+        dataset.root
+        / Path(DEFAULT_IMAGE_PATH.format(image_key=video_key, episode_index=0, frame_index=0)).parent
+    )
+    assert video_staging_dir.is_dir()
+
+    dataset.clear_episode_buffer()
+
+    assert dataset.writer.episode_buffer["size"] == 0
+    assert not video_staging_dir.exists()
+
+
 def test_finalize_is_idempotent(tmp_path):
    """Calling finalize() twice does not raise."""
    dataset = LeRobotDataset.create(
@@ -114,6 +114,20 @@ def test_dataset_initialization(tmp_path, lerobot_dataset_factory):
    assert dataset.num_frames == len(dataset)


+def test_dataset_slice(tmp_path, lerobot_dataset_factory):
+    dataset = lerobot_dataset_factory(
+        root=tmp_path / "test", total_episodes=3, total_frames=30, use_videos=False
+    )
+
+    assert len(dataset[:5]) == 5
+    assert len(dataset[::2]) == (len(dataset) + 1) // 2
+    assert [item["index"].item() for item in dataset[4::-1]] == [4, 3, 2, 1, 0]
+    assert [item["index"].item() for item in dataset[-3:]] == list(range(len(dataset) - 3, len(dataset)))
+    assert dataset[len(dataset) :] == []
+    assert isinstance(dataset[0], dict)
+    assert dataset[:1][0].keys() == dataset[0].keys()
+
+
 # TODO(rcadene, aliberts): do not run LeRobotDataset.create, instead refactor LeRobotDatasetMetadata.create
 # and test the small resulting function that validates the features
 def test_dataset_feature_with_forward_slash_raises_error():
@@ -1741,6 +1755,38 @@ def test_delta_timestamps_query_returns_correct_values(tmp_path, empty_lerobot_d
    assert is_pad == [True, False], f"Expected [True, False], got {is_pad}"


+def test_dataset_slice_with_delta_timestamps(tmp_path, empty_lerobot_dataset_factory):
+    features = {
+        "observation.state": {"dtype": "float32", "shape": (1,), "names": ["x"]},
+    }
+    dataset = empty_lerobot_dataset_factory(
+        root=tmp_path / "test_slice_delta", features=features, use_videos=False, fps=10
+    )
+
+    for frame_idx in range(5):
+        dataset.add_frame(
+            {
+                "observation.state": torch.tensor([frame_idx], dtype=torch.float32),
+                "task": "task_0",
+            }
+        )
+    dataset.save_episode()
+    dataset.finalize()
+
+    sliced_dataset = LeRobotDataset(
+        dataset.repo_id,
+        root=dataset.root,
+        delta_timestamps={"observation.state": [-0.1, 0.0]},
+        tolerance_s=0.04,
+    )
+
+    items = sliced_dataset[:2]
+
+    assert items[0]["observation.state"].tolist() == [0.0, 0.0]
+    assert items[0]["observation.state_is_pad"].tolist() == [True, False]
+    assert items[1]["observation.state"].tolist() == [0.0, 1.0]
+
+
 def test_episode_filter_filters_dataset(tmp_path, lerobot_dataset_factory):
    """episode_filter on LeRobotDataset narrows the loaded dataset to matching episodes."""
    dataset = lerobot_dataset_factory(root=tmp_path / "test", total_episodes=8, total_frames=200)
@@ -20,6 +20,7 @@ property delegation, and the full create-record-finalize-read lifecycle.
 """

 from pathlib import Path
+from types import SimpleNamespace
 from unittest.mock import Mock

 import pytest
@@ -191,6 +192,48 @@ def test_metadata_without_root_uses_hub_cache_snapshot_download(
    }


+@pytest.mark.parametrize("token", ["hf_test_token", True, False])
+def test_metadata_download_forwards_token(tmp_path, monkeypatch, token):
+    snapshot_root = tmp_path / "snapshot"
+    snapshot_download = Mock(return_value=str(snapshot_root))
+    get_safe_version = Mock(return_value="v3.0")
+    load_metadata = Mock(side_effect=[FileNotFoundError, None])
+    monkeypatch.setattr(dataset_metadata_module, "snapshot_download", snapshot_download)
+    monkeypatch.setattr(dataset_metadata_module, "get_safe_version", get_safe_version)
+    monkeypatch.setattr(LeRobotDatasetMetadata, "_load_metadata", load_metadata)
+
+    meta = LeRobotDatasetMetadata(
+        repo_id=DUMMY_REPO_ID,
+        revision="v3.0",
+        token=token,
+    )
+
+    assert meta.root == snapshot_root
+    assert not hasattr(meta, "_token")
+    get_safe_version.assert_called_once_with(DUMMY_REPO_ID, "v3.0", token=token)
+    assert snapshot_download.call_args.kwargs["token"] is token
+
+
+@pytest.mark.parametrize("token", ["hf_test_token", True, False])
+def test_data_download_forwards_token(tmp_path, monkeypatch, token):
+    snapshot_root = tmp_path / "snapshot"
+    snapshot_download = Mock(return_value=str(snapshot_root))
+    monkeypatch.setattr(lerobot_dataset_module, "snapshot_download", snapshot_download)
+
+    dataset = LeRobotDataset.__new__(LeRobotDataset)
+    dataset.repo_id = DUMMY_REPO_ID
+    dataset.revision = "main"
+    dataset.episodes = None
+    dataset._requested_root = None
+    dataset.meta = SimpleNamespace(root=None)
+    dataset.reader = SimpleNamespace(root=None)
+
+    dataset._download(token=token)
+
+    assert dataset.root == snapshot_root
+    assert snapshot_download.call_args.kwargs["token"] is token
+
+
 def test_without_root_reads_different_revisions_from_distinct_snapshot_roots(
    tmp_path,
    info_factory,
@@ -13,12 +13,16 @@
 # 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 Mock
+
 import numpy as np
 import pytest
 import torch

 pytest.importorskip("datasets", reason="datasets is required (install lerobot[dataset])")

+import lerobot.datasets.streaming_dataset as streaming_dataset_module
 from lerobot.datasets.streaming_dataset import StreamingLeRobotDataset
 from lerobot.datasets.utils import safe_shard
 from lerobot.utils.constants import ACTION
@@ -71,6 +75,40 @@ def get_frames_expected_order(streaming_ds: StreamingLeRobotDataset) -> list[int
    return expected_indices


+@pytest.mark.parametrize("token", ["hf_test_token", True, False])
+@pytest.mark.parametrize("from_local", [False, True])
+def test_streaming_dataset_forwards_hub_token_only_for_remote_data(tmp_path, monkeypatch, token, from_local):
+    requested_root = tmp_path / "local" if from_local else None
+    metadata = SimpleNamespace(
+        root=requested_root or tmp_path / "snapshot",
+        revision=streaming_dataset_module.CODEBASE_VERSION,
+        _version=streaming_dataset_module.CODEBASE_VERSION,
+        features={},
+        depth_keys=[],
+        image_keys=[],
+        rescale_depth_stats=Mock(),
+    )
+    metadata_cls = Mock(return_value=metadata)
+    load_dataset = Mock(return_value=SimpleNamespace(num_shards=1))
+    monkeypatch.setattr(streaming_dataset_module, "LeRobotDatasetMetadata", metadata_cls)
+    monkeypatch.setattr(streaming_dataset_module, "load_dataset", load_dataset)
+
+    dataset = StreamingLeRobotDataset(DUMMY_REPO_ID, root=requested_root, token=token)
+
+    metadata_cls.assert_called_once_with(
+        DUMMY_REPO_ID,
+        requested_root,
+        streaming_dataset_module.CODEBASE_VERSION,
+        force_cache_sync=False,
+        token=token,
+    )
+    if from_local:
+        assert "token" not in load_dataset.call_args.kwargs
+    else:
+        assert load_dataset.call_args.kwargs["token"] is token
+    assert not hasattr(dataset, "_token")
+
+
 def test_single_frame_consistency(tmp_path, lerobot_dataset_factory):
    """Test if are correctly accessed"""
    ds_num_frames = 400
@@ -35,6 +35,17 @@ def test_unknown_type():
        make_env_config("nonexistent")


+def test_libero_fps_controls_simulator_frequency():
+    cfg = LiberoEnv(fps=17)
+
+    assert cfg.gym_kwargs["control_freq"] == 17
+
+
+def test_libero_rejects_nonpositive_fps():
+    with pytest.raises(ValueError, match="fps must be positive"):
+        LiberoEnv(fps=0)
+
+
 def test_identity_processors():
    """Base class get_env_processors() returns identity pipelines."""
    cfg = make_env_config("aloha")
@@ -0,0 +1,245 @@
+# 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.
+
+import shlex
+import sys
+from unittest.mock import MagicMock
+
+import draccus
+import pytest
+
+pytest.importorskip("datasets", reason="datasets is required (install lerobot[dataset])")
+
+from lerobot.annotations.steerable_pipeline.config import (
+    DEFAULT_ANNOTATE_JOB_IMAGE,
+    AnnotationJobConfig,
+    AnnotationPipelineConfig,
+)
+from lerobot.jobs.annotate import build_pod_command, build_pod_setup, submit_annotate_to_hf
+
+
+def _parse(*args):
+    return draccus.parse(AnnotationPipelineConfig, args=list(args))
+
+
+def _set_argv(monkeypatch, *args):
+    monkeypatch.setattr(sys, "argv", ["lerobot-annotate", *args])
+
+
+# --- config ----------------------------------------------------------------
+
+
+def test_annotation_job_defaults_are_local_with_vllm_image():
+    cfg = AnnotationJobConfig()
+    assert cfg.target is None
+    assert cfg.is_remote is False
+    assert cfg.image == DEFAULT_ANNOTATE_JOB_IMAGE
+    assert cfg.timeout == "2h"
+    assert cfg.lerobot_ref == "main"
+
+
+def test_annotation_config_parses_job_target():
+    cfg = _parse("--repo_id", "u/d", "--job.target", "h200")
+    assert cfg.job.target == "h200"
+    assert cfg.job.is_remote is True
+
+
+def test_annotation_config_defaults_to_local():
+    assert _parse("--repo_id", "u/d").job.is_remote is False
+
+
+# --- pod command -----------------------------------------------------------
+
+
+def test_pod_setup_installs_requested_ref():
+    setup = build_pod_setup("my-branch")
+    assert "git+https://github.com/huggingface/lerobot.git@my-branch" in setup
+    # The vLLM image has neither ffmpeg (video decode) nor lerobot's pinned deps.
+    assert "ffmpeg" in setup
+    assert "'draccus==0.10.0'" in setup
+
+
+def _annotate_argv(command):
+    """Extract the `lerobot-annotate ...` argv from a `bash -c` pod command."""
+    assert command[:2] == ["bash", "-c"]
+    _setup, _, annotate = command[2].rpartition(" && ")
+    return shlex.split(annotate)
+
+
+def test_pod_command_forwards_user_flags_and_pins_local_target():
+    command = build_pod_command(
+        "u/d",
+        "main",
+        ["--repo_id=u/d", "--new_repo_id=u/d_annotated", "--push_to_hub=true", "--job.target=h200"],
+    )
+    argv = _annotate_argv(command)
+    assert argv[0] == "lerobot-annotate"
+    # --job.* is client-side orchestration; the pod must not re-dispatch itself.
+    assert not any(a.startswith("--job.") for a in argv[1:-1])
+    assert argv[-1] == "--job.target=local"
+    assert "--new_repo_id=u/d_annotated" in argv
+    assert "--push_to_hub=true" in argv
+
+
+def test_pod_command_replaces_host_local_root_with_repo_id():
+    """--root points at a directory only the client has; the pod resolves by repo_id."""
+    command = build_pod_command("u/d", "main", ["--root", "/home/me/datasets/d", "--seed=7"])
+    argv = _annotate_argv(command)
+    assert "--root" not in argv
+    assert "/home/me/datasets/d" not in argv
+    assert argv.count("--repo_id=u/d") == 1
+    assert "--seed=7" in argv
+
+
+def test_pod_command_does_not_duplicate_repo_id():
+    command = build_pod_command("u/d", "main", ["--repo_id", "u/d"])
+    assert _annotate_argv(command).count("--repo_id=u/d") == 1
+
+
+def test_pod_command_quotes_flags_containing_spaces_and_json():
+    """serve_command and chat_template_kwargs must survive the trip through `bash -c`."""
+    serve = "--vlm.serve_command=vllm serve Qwen/Qwen3.6-27B --max-model-len 32768 --port {port}"
+    kwargs = '--vlm.chat_template_kwargs={"enable_thinking": false}'
+    command = build_pod_command("u/d", "main", [serve, kwargs])
+    argv = _annotate_argv(command)
+    assert serve in argv
+    assert kwargs in argv
+
+
+# --- submission ------------------------------------------------------------
+
+
+def test_submit_requires_login(monkeypatch):
+    monkeypatch.setattr("lerobot.jobs.annotate.get_token", lambda: None)
+    with pytest.raises(RuntimeError, match="hf auth login"):
+        submit_annotate_to_hf(_parse("--repo_id", "u/d", "--job.target", "h200"))
+
+
+def test_submit_requires_repo_id(monkeypatch):
+    """A remote run over --root alone can't work: the pod can't see the client's disk."""
+    monkeypatch.setattr("lerobot.jobs.annotate.get_token", lambda: "tok")
+    cfg = _parse("--root", "/tmp/d", "--job.target", "h200")
+    with pytest.raises(ValueError, match="--repo_id"):
+        submit_annotate_to_hf(cfg)
+
+
+@pytest.mark.parametrize("arg", ["--config_path=annotate.yaml", "--vlm=vlm.yaml", "--job=job.yaml"])
+def test_submit_rejects_local_config_files(monkeypatch, arg):
+    """draccus takes a config file for the whole config and for each nested one; the
+    pod can read none of them, so a remote run must refuse rather than drop them."""
+    monkeypatch.setattr("lerobot.jobs.annotate.get_token", lambda: "tok")
+    _set_argv(monkeypatch, arg, "--job.target=h200")
+    cfg = _parse("--repo_id", "u/d", "--job.target", "h200")
+    with pytest.raises(ValueError, match="cannot read config files"):
+        submit_annotate_to_hf(cfg)
+
+
+def test_pod_command_drops_bare_job_config_file_arg():
+    """`--job` isn't caught by the `--job.` prefix, and could carry a remote target
+    that would make the pod submit a job of its own — recursively."""
+    argv = _annotate_argv(build_pod_command("u/d", "main", ["--job", "job.yaml", "--seed=7"]))
+    assert "--job" not in argv
+    assert "job.yaml" not in argv
+    assert argv[-1] == "--job.target=local"
+
+
+def test_submit_dispatches_job(monkeypatch):
+    monkeypatch.setattr("lerobot.jobs.annotate.get_token", lambda: "tok")
+    monkeypatch.setattr("lerobot.jobs.annotate.HfApi", lambda token=None: MagicMock())
+    monkeypatch.setattr("lerobot.jobs.annotate.ensure_dataset_available", lambda *a, **kw: None)
+
+    run_job_calls = []
+
+    def fake_run_job(**kwargs):
+        run_job_calls.append(kwargs)
+        return MagicMock(id="job-123")
+
+    monkeypatch.setattr("lerobot.jobs.annotate.run_job", fake_run_job)
+    _set_argv(monkeypatch, "--repo_id=u/d", "--push_to_hub=true", "--job.target=h200", "--job.detach=true")
+
+    cfg = _parse("--repo_id", "u/d", "--push_to_hub", "true", "--job.target", "h200", "--job.detach", "true")
+    submit_annotate_to_hf(cfg)
+
+    assert len(run_job_calls) == 1
+    call = run_job_calls[0]
+    assert call["flavor"] == "h200"
+    assert call["image"] == DEFAULT_ANNOTATE_JOB_IMAGE
+    assert call["timeout"] == "2h"
+    # The Hub token is forwarded so the pod can pull a private dataset and push the result.
+    assert call["secrets"]["HF_TOKEN"] == "tok"
+    assert call["labels"].get("lerobot") == "true"
+    argv = _annotate_argv(call["command"])
+    assert argv[0] == "lerobot-annotate"
+    assert "--push_to_hub=true" in argv
+
+
+@pytest.mark.timeout(15)
+def test_submit_follows_job_to_completion(monkeypatch, capsys):
+    """Non-detach path must stream logs and RETURN (not hang) once the job is terminal.
+
+    Exercises the `follow_job` helper shared with the training submitter from the
+    annotation side, which is why the job-state patches target `lerobot.jobs.hf`.
+    Asserting on the completion message and not merely on "didn't hang" is what makes
+    this fail if `follow_job` ever reports detached-without-a-verdict instead.
+    """
+    monkeypatch.setattr("lerobot.jobs.annotate.get_token", lambda: "tok")
+    monkeypatch.setattr("lerobot.jobs.annotate.HfApi", lambda token=None: MagicMock())
+    monkeypatch.setattr("lerobot.jobs.annotate.ensure_dataset_available", lambda *a, **kw: None)
+    monkeypatch.setattr("lerobot.jobs.annotate.run_job", lambda **kw: MagicMock(id="job-1", url="http://x"))
+    monkeypatch.setattr(
+        "lerobot.jobs.hf.inspect_job",
+        lambda job_id: MagicMock(status=MagicMock(stage=MagicMock(value="COMPLETED"), message=None)),
+    )
+    monkeypatch.setattr("lerobot.jobs.hf.fetch_job_logs", lambda job_id, follow=True: iter(()))
+    _set_argv(monkeypatch, "--repo_id=u/d", "--job.target=h200")
+
+    submit_annotate_to_hf(_parse("--repo_id", "u/d", "--push_to_hub", "true", "--job.target", "h200"))
+    assert "Annotation complete" in capsys.readouterr().out
+
+
+@pytest.mark.timeout(15)
+def test_submit_raises_when_job_fails(monkeypatch):
+    """A job that ends in a non-COMPLETED stage must surface as an error, not a silent return."""
+    monkeypatch.setattr("lerobot.jobs.annotate.get_token", lambda: "tok")
+    monkeypatch.setattr("lerobot.jobs.annotate.HfApi", lambda token=None: MagicMock())
+    monkeypatch.setattr("lerobot.jobs.annotate.ensure_dataset_available", lambda *a, **kw: None)
+    monkeypatch.setattr("lerobot.jobs.annotate.run_job", lambda **kw: MagicMock(id="job-1", url=None))
+    monkeypatch.setattr(
+        "lerobot.jobs.hf.inspect_job",
+        lambda job_id: MagicMock(status=MagicMock(stage=MagicMock(value="ERROR"), message="Job timeout")),
+    )
+    monkeypatch.setattr("lerobot.jobs.hf.fetch_job_logs", lambda job_id, follow=True: iter(()))
+    _set_argv(monkeypatch, "--repo_id=u/d", "--job.target=h200")
+
+    with pytest.raises(RuntimeError, match="stage=ERROR .Job timeout."):
+        submit_annotate_to_hf(_parse("--repo_id", "u/d", "--job.target", "h200"))
+
+
+def test_submit_ensures_dataset_is_on_the_hub(monkeypatch):
+    """A local-only dataset is pushed (privately) before the job can reach it by repo_id."""
+    monkeypatch.setattr("lerobot.jobs.annotate.get_token", lambda: "tok")
+    monkeypatch.setattr("lerobot.jobs.annotate.HfApi", lambda token=None: MagicMock())
+    monkeypatch.setattr("lerobot.jobs.annotate.run_job", lambda **kw: MagicMock(id="job-1"))
+
+    seen = []
+    monkeypatch.setattr(
+        "lerobot.jobs.annotate.ensure_dataset_available",
+        lambda repo_id, *, api, tags=None: seen.append((repo_id, tags)),
+    )
+    _set_argv(monkeypatch, "--repo_id=u/d", "--job.target=h200", "--job.detach=true")
+
+    submit_annotate_to_hf(
+        _parse("--repo_id", "u/d", "--job.target", "h200", "--job.detach", "true", "--job.tags", '["lelab"]')
+    )
+    assert seen == [("u/d", ["lerobot", "lelab"])]
@@ -29,12 +29,26 @@ from lerobot.jobs.hf import (
    _poll_until_done,
    build_remote_config_file,
    build_repo_id,
+    follow_job,
    resolve_job_tags,
    resolve_wandb_api_key,
    submit_to_hf,
 )


+def test_follow_job_detach_returns_without_watching(monkeypatch):
+    """`detach` must short-circuit before any polling or log streaming starts."""
+
+    def _boom(*a, **kw):
+        raise AssertionError("detach must not touch the job")
+
+    monkeypatch.setattr("lerobot.jobs.hf.inspect_job", _boom)
+    monkeypatch.setattr("lerobot.jobs.hf.fetch_job_logs", _boom)
+    # False = "stopped watching without a verdict", so callers stay quiet rather than
+    # claiming success for a job that is still running.
+    assert follow_job("job-1", detach=True) is False
+
+
 def test_resolve_job_tags_always_includes_lerobot_and_dedups():
    assert resolve_job_tags(None) == ["lerobot"]
    assert resolve_job_tags([]) == ["lerobot"]
@@ -405,12 +405,18 @@ def test_record_ranges_of_motion(mock_motors, dummy_motors):
    read_pos_stub = mock_motors.build_sequential_sync_read_stub(
        *X_SERIES_CONTROL_TABLE["Present_Position"], positions
    )
-    with patch("lerobot.motors.motors_bus.enter_pressed", side_effect=[False, True]):
-        bus = DynamixelMotorsBus(port=mock_motors.port, motors=dummy_motors)
-        bus.connect(handshake=False)
+    bus = DynamixelMotorsBus(port=mock_motors.port, motors=dummy_motors)
+    bus.connect(handshake=False)

+    with (
+        patch("lerobot.motors.motors_bus.enter_pressed", side_effect=[False, True]),
+        patch("lerobot.motors.motors_bus.time.sleep") as mock_sleep,
+        patch.object(bus, "sync_read", wraps=bus.sync_read) as mock_sync_read,
+    ):
        mins, maxes = bus.record_ranges_of_motion(display_values=False)

    assert mock_motors.stubs[read_pos_stub].calls == 3
+    assert all(call.kwargs["num_retry"] == 5 for call in mock_sync_read.call_args_list)
+    mock_sleep.assert_called_once_with(0.02)
    assert mins == expected_mins
    assert maxes == expected_maxes
@@ -509,12 +509,18 @@ def test_record_ranges_of_motion(mock_motors, dummy_motors):
    stub = mock_motors.build_sequential_sync_read_stub(
        *STS_SMS_SERIES_CONTROL_TABLE["Present_Position"], positions
    )
-    with patch("lerobot.motors.motors_bus.enter_pressed", side_effect=[False, True]):
-        bus = FeetechMotorsBus(port=mock_motors.port, motors=dummy_motors)
-        bus.connect(handshake=False)
+    bus = FeetechMotorsBus(port=mock_motors.port, motors=dummy_motors)
+    bus.connect(handshake=False)

+    with (
+        patch("lerobot.motors.motors_bus.enter_pressed", side_effect=[False, True]),
+        patch("lerobot.motors.motors_bus.time.sleep") as mock_sleep,
+        patch.object(bus, "sync_read", wraps=bus.sync_read) as mock_sync_read,
+    ):
        mins, maxes = bus.record_ranges_of_motion(display_values=False)

    assert mock_motors.stubs[stub].calls == 3
+    assert all(call.kwargs["num_retry"] == 5 for call in mock_sync_read.call_args_list)
+    mock_sleep.assert_called_once_with(0.02)
    assert mins == expected_mins
    assert maxes == expected_maxes
@@ -457,9 +457,8 @@ def test_merge_validates_delay_consistency(action_queue_rtc_enabled, sample_acti
        action_index_before_inference=0,
    )

-    # Check warning was logged (reworded when the discard clamp was added)
-    assert "Indexes diff != real delay" in caplog.text
-    assert "clamping discard" in caplog.text
+    # Check warning was logged
+    assert "Indexes diff is not equal to real delay" in caplog.text


 def test_merge_no_warning_when_delays_match(action_queue_rtc_enabled, sample_actions, caplog):
@@ -791,12 +790,8 @@ def test_typical_rtc_workflow(action_queue_rtc_enabled, sample_actions):

    assert action_queue_rtc_enabled.qsize() == 40

-    # Second inference with delay. Capture the index at inference *start*, then consume
-    # `real_delay` actions to simulate the robot executing during inference, so the
-    # measured `indexes_diff` matches `real_delay` and the discard clamp is a no-op.
+    # Second inference with delay
    action_index_before = action_queue_rtc_enabled.get_action_index()
-    for _ in range(5):
-        action_queue_rtc_enabled.get()

    action_queue_rtc_enabled.merge(
        sample_actions["original"],
@@ -26,8 +26,17 @@ import tempfile
 from pathlib import Path

 import pytest
+import torch
+from safetensors.torch import save_file

-from lerobot.processor.pipeline import DataProcessorPipeline, ProcessorMigrationError
+from lerobot.configs import PipelineFeatureType, PolicyFeature
+from lerobot.processor.pipeline import (
+    DataProcessorPipeline,
+    ProcessorMigrationError,
+    ProcessorStep,
+    ProcessorStepRegistry,
+)
+from lerobot.types import EnvTransition

 # Simplified Config Loading Tests

@@ -98,6 +107,140 @@ def test_load_config_nonexistent_path_tries_hub():
        DataProcessorPipeline._load_config("nonexistent/path", "processor.json", {})


+def test_from_pretrained_local_directory_missing_state_does_not_call_hub(monkeypatch):
+    """Local processor dirs must fail locally when a state file is missing."""
+
+    @ProcessorStepRegistry.register("local_missing_state_step")
+    class LocalMissingStateStep(ProcessorStep):
+        def __call__(self, transition: EnvTransition) -> EnvTransition:
+            return transition
+
+        def transform_features(
+            self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]
+        ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]:
+            return features
+
+        def load_state_dict(self, state: dict[str, torch.Tensor]) -> None:
+            pass
+
+    try:
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            tmp_path = Path(tmp_dir)
+            config = {
+                "name": "LocalMissingStatePipeline",
+                "steps": [{"registry_name": "local_missing_state_step", "state_file": "missing.safetensors"}],
+            }
+            (tmp_path / "processor.json").write_text(json.dumps(config))
+
+            def fail_hub_download(*args, **kwargs):
+                pytest.fail("local missing processor state should not call hf_hub_download")
+
+            monkeypatch.setattr("lerobot.processor.pipeline.hf_hub_download", fail_hub_download)
+
+            with pytest.raises(FileNotFoundError, match="missing.safetensors.*local processor pipeline"):
+                DataProcessorPipeline.from_pretrained(tmp_path, config_filename="processor.json")
+    finally:
+        ProcessorStepRegistry.unregister("local_missing_state_step")
+
+
+def test_from_pretrained_local_config_file_missing_state_does_not_call_hub(monkeypatch):
+    """Local single-file processor configs must also keep missing state resolution local."""
+
+    @ProcessorStepRegistry.register("local_file_missing_state_step")
+    class LocalFileMissingStateStep(ProcessorStep):
+        def __call__(self, transition: EnvTransition) -> EnvTransition:
+            return transition
+
+        def transform_features(
+            self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]
+        ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]:
+            return features
+
+        def load_state_dict(self, state: dict[str, torch.Tensor]) -> None:
+            pass
+
+    try:
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            tmp_path = Path(tmp_dir)
+            config_path = tmp_path / "processor.json"
+            config = {
+                "name": "LocalFileMissingStatePipeline",
+                "steps": [
+                    {"registry_name": "local_file_missing_state_step", "state_file": "missing.safetensors"}
+                ],
+            }
+            config_path.write_text(json.dumps(config))
+
+            def fail_hub_download(*args, **kwargs):
+                pytest.fail("local missing processor state should not call hf_hub_download")
+
+            monkeypatch.setattr("lerobot.processor.pipeline.hf_hub_download", fail_hub_download)
+
+            with pytest.raises(FileNotFoundError, match="missing.safetensors.*local processor pipeline"):
+                DataProcessorPipeline.from_pretrained(config_path, config_filename="ignored.json")
+    finally:
+        ProcessorStepRegistry.unregister("local_file_missing_state_step")
+
+
+def test_from_pretrained_hub_source_missing_local_state_still_calls_hub(monkeypatch, tmp_path):
+    """Hub sources still fall back to hf_hub_download for state files."""
+
+    @ProcessorStepRegistry.register("hub_state_step")
+    class HubStateStep(ProcessorStep):
+        def __init__(self):
+            self.value = torch.tensor(0)
+
+        def __call__(self, transition: EnvTransition) -> EnvTransition:
+            return transition
+
+        def transform_features(
+            self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]
+        ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]:
+            return features
+
+        def load_state_dict(self, state: dict[str, torch.Tensor]) -> None:
+            self.value = state["value"]
+
+    try:
+        state_path = tmp_path / "downloaded.safetensors"
+        save_file({"value": torch.tensor(7)}, state_path)
+        loaded_config = {
+            "name": "HubStatePipeline",
+            "steps": [{"registry_name": "hub_state_step", "state_file": "hub_state.safetensors"}],
+        }
+        calls = []
+
+        def fake_load_config(cls, model_id, config_filename, hub_download_kwargs):
+            return loaded_config, tmp_path / "hub_cache"
+
+        def fake_hub_download(**kwargs):
+            calls.append(kwargs)
+            return str(state_path)
+
+        monkeypatch.setattr(DataProcessorPipeline, "_load_config", classmethod(fake_load_config))
+        monkeypatch.setattr("lerobot.processor.pipeline.hf_hub_download", fake_hub_download)
+
+        pipeline = DataProcessorPipeline.from_pretrained("user/repo", config_filename="processor.json")
+
+        assert calls == [
+            {
+                "repo_id": "user/repo",
+                "filename": "hub_state.safetensors",
+                "repo_type": "model",
+                "force_download": False,
+                "resume_download": None,
+                "proxies": None,
+                "token": None,
+                "cache_dir": None,
+                "local_files_only": False,
+                "revision": None,
+            }
+        ]
+        assert pipeline.steps[0].value.item() == 7
+    finally:
+        ProcessorStepRegistry.unregister("hub_state_step")
+
+
 # Config Validation Tests


@@ -109,3 +109,22 @@ def test_send_action(follower):

    goal_pos = {m: (i + 1) * 10 for i, m in enumerate(follower.bus.motors)}
    follower.bus.sync_write.assert_called_once_with("Goal_Position", goal_pos)
+
+
+def test_configure_writes_position_pid_coefficients():
+    bus_mock = _make_bus_mock()
+    bus_mock.motors = ["shoulder_pan"]
+    robot = MagicMock()
+    robot.bus = bus_mock
+    robot.config = SO100FollowerConfig(
+        port="/dev/null",
+        position_p_coefficient=32,
+        position_i_coefficient=1,
+        position_d_coefficient=16,
+    )
+
+    SO100Follower.configure(robot)
+
+    bus_mock.write.assert_any_call("P_Coefficient", "shoulder_pan", 32)
+    bus_mock.write.assert_any_call("I_Coefficient", "shoulder_pan", 1)
+    bus_mock.write.assert_any_call("D_Coefficient", "shoulder_pan", 16)
@@ -0,0 +1,49 @@
+# 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 pytest
+
+import lerobot.scripts.lerobot_setup_motors as motors_module
+
+
+def test_main_registers_plugins_before_parsing(monkeypatch):
+    calls = []
+    monkeypatch.setattr(motors_module, "register_third_party_plugins", lambda: calls.append("register"))
+    monkeypatch.setattr(motors_module, "setup_motors", lambda: calls.append("setup"))
+
+    motors_module.main()
+
+    assert calls == ["register", "setup"]
+
+
+def test_setup_motors_accepts_third_party_device(monkeypatch):
+    device = MagicMock()
+    monkeypatch.setattr(motors_module, "make_teleoperator_from_config", lambda _: device)
+    cfg = SimpleNamespace(device=SimpleNamespace(type="third_party"))
+
+    motors_module.setup_motors.__wrapped__(cfg)
+
+    device.setup_motors.assert_called_once_with()
+
+
+def test_setup_motors_reports_unsupported_device(monkeypatch):
+    device = object()
+    monkeypatch.setattr(motors_module, "make_teleoperator_from_config", lambda _: device)
+    cfg = SimpleNamespace(device=SimpleNamespace(type="third_party"))
+
+    with pytest.raises(NotImplementedError, match="third_party"):
+        motors_module.setup_motors.__wrapped__(cfg)
@@ -17,6 +17,8 @@
 from __future__ import annotations

 import dataclasses
+import sys
+from types import SimpleNamespace
 from unittest.mock import MagicMock

 import pytest
@@ -106,6 +108,109 @@ def test_sentry_config_defaults():
    assert cfg.target_video_file_size_mb is None


+def test_rollout_config_passes_policy_pretrained_revision(monkeypatch):
+    from lerobot.configs import PreTrainedConfig, parser
+    from lerobot.rollout import RolloutConfig
+    from tests.mocks.mock_robot import MockRobotConfig
+
+    captured = {}
+
+    def fake_from_pretrained(cls, pretrained_name_or_path, **kwargs):
+        captured["pretrained_name_or_path"] = pretrained_name_or_path
+        captured.update(kwargs)
+        return SimpleNamespace(device="cpu", pretrained_revision=kwargs["revision"])
+
+    monkeypatch.setattr(parser, "get_yaml_overrides", lambda _: ["--pretrained_revision=yaml-sha"])
+    monkeypatch.setattr(
+        sys,
+        "argv",
+        ["lerobot-rollout", "--policy.path=user/policy", "--policy.pretrained_revision=cli-sha"],
+    )
+    monkeypatch.setattr(PreTrainedConfig, "from_pretrained", classmethod(fake_from_pretrained))
+
+    cfg = RolloutConfig(robot=MockRobotConfig())
+
+    assert captured["pretrained_name_or_path"] == "user/policy"
+    assert captured["revision"] == "cli-sha"
+    assert captured["cli_overrides"] == [
+        "--pretrained_revision=yaml-sha",
+        "--pretrained_revision=cli-sha",
+    ]
+    assert cfg.policy.pretrained_path == "user/policy"
+    assert cfg.policy.pretrained_revision == "cli-sha"
+
+
+def test_load_pretrained_policy_passes_revision(monkeypatch):
+    import lerobot.rollout.context as rollout_context
+
+    policy_config = SimpleNamespace(
+        type="mock",
+        use_peft=False,
+        pretrained_path="user/policy",
+        pretrained_revision="policy-sha",
+    )
+    policy_class = MagicMock()
+    loaded_policy = MagicMock()
+    policy_class.from_pretrained.return_value = loaded_policy
+    monkeypatch.setattr(rollout_context, "get_policy_class", lambda _: policy_class)
+
+    policy = rollout_context._load_pretrained_policy(policy_config)
+
+    assert policy is loaded_policy
+    policy_class.from_pretrained.assert_called_once_with(
+        "user/policy",
+        config=policy_config,
+        revision="policy-sha",
+    )
+
+
+def test_load_pretrained_peft_policy_keeps_adapter_and_base_revisions_separate(monkeypatch):
+    import lerobot.rollout.context as rollout_context
+
+    policy_config = SimpleNamespace(
+        type="mock",
+        use_peft=True,
+        pretrained_path="user/adapter",
+        pretrained_revision="adapter-sha",
+    )
+    policy_class = MagicMock()
+    base_policy = MagicMock()
+    policy_class.from_pretrained.return_value = base_policy
+    monkeypatch.setattr(rollout_context, "get_policy_class", lambda _: policy_class)
+
+    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 = MagicMock()
+    peft_model_from_pretrained = MagicMock(return_value=adapted_policy)
+    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
+    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",
+        config=policy_config,
+        revision="base-sha",
+    )
+    peft_model_from_pretrained.assert_called_once_with(
+        base_policy,
+        "user/adapter",
+        config=peft_config,
+        revision="adapter-sha",
+    )
+
+
 # ---------------------------------------------------------------------------
 # RolloutRingBuffer
 # ---------------------------------------------------------------------------
@@ -251,69 +356,6 @@ def test_create_inference_engine_sync():
    assert isinstance(engine, SyncInferenceEngine)


-# ---------------------------------------------------------------------------
-# RTC action-key ordering and prefix padding
-# ---------------------------------------------------------------------------
-
-
-def test_rtc_get_action_remaps_model_order_to_ordered_action_keys():
-    """RTC must remap the model-order action vector to ``ordered_action_keys`` by NAME
-    before returning — matching sync. Otherwise the strategy maps model outputs onto the
-    wrong joints (a per-joint permutation) whenever the two orders differ."""
-    from lerobot.rollout import RTCInferenceConfig, RTCInferenceEngine, create_inference_engine
-    from lerobot.utils.constants import ACTION
-
-    # The model emits actions in dataset order [a, b, c]; the robot wants [c, a, b].
-    dataset_action_names = ["a.pos", "b.pos", "c.pos"]
-    ordered_action_keys = ["c.pos", "a.pos", "b.pos"]
-    dataset_features = {
-        ACTION: {"dtype": "float32", "shape": (3,), "names": dataset_action_names},
-    }
-
-    engine = create_inference_engine(
-        RTCInferenceConfig(),
-        policy=MagicMock(),
-        preprocessor=MagicMock(steps=[]),
-        postprocessor=MagicMock(steps=[]),
-        robot_wrapper=MagicMock(robot_type="mock"),
-        hw_features={},
-        dataset_features=dataset_features,
-        ordered_action_keys=ordered_action_keys,
-        task="test",
-        fps=30.0,
-        device="cpu",
-    )
-    assert isinstance(engine, RTCInferenceEngine)
-
-    # Queue yields the model-order vector a=1, b=2, c=3.
-    engine._action_queue = MagicMock()
-    engine._action_queue.get.return_value = torch.tensor([1.0, 2.0, 3.0])
-
-    out = engine.get_action(None)
-    # Remapped by name to [c, a, b] = [3, 1, 2]; positional pass-through would give [1, 2, 3].
-    torch.testing.assert_close(out, torch.tensor([3.0, 1.0, 2.0]))
-
-
-def test_normalize_prev_actions_length_holds_last_action_not_zeros():
-    """A short RTC prefix is padded by repeating the last action, never with zeros —
-    zeros decode to the mean action and cause the intermittent chunk-seam spike."""
-    from lerobot.rollout.inference.rtc import _normalize_prev_actions_length
-
-    prev = torch.tensor([[1.0, -1.0], [2.0, -2.0]])  # 2 steps, dim 2
-
-    # Pad up to 5: rows 2..4 must equal the last real row, not zeros.
-    padded = _normalize_prev_actions_length(prev, target_steps=5)
-    assert padded.shape == (5, 2)
-    torch.testing.assert_close(padded[:2], prev)
-    for i in range(2, 5):
-        torch.testing.assert_close(padded[i], prev[-1])
-    assert not torch.any(padded[2:] == 0.0), "pad rows must not be zeros"
-
-    # Exact length: unchanged. Truncation: first `target_steps` rows.
-    torch.testing.assert_close(_normalize_prev_actions_length(prev, 2), prev)
-    torch.testing.assert_close(_normalize_prev_actions_length(prev, 1), prev[:1])
-
-
 # ---------------------------------------------------------------------------
 # Pure functions
 # ---------------------------------------------------------------------------
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
Steven Palma	95211b98f1	feat(config): add `multiprocessing` option to `DataLoader` context and sets `spawn` as default (#4139 ) * Add dataloader_multiprocessing_context, default to spawn Make the DataLoader multiprocessing start method configurable on TrainPipelineConfig and default it to 'spawn'. The previous default (fork on Linux) is unsafe with libraries that hold non-fork-safe state in the parent process — common ones in this codebase are PyAV, torchcodec, and the ffmpeg shared libs they wrap. Symptoms reported in #2488, #2209, and observed locally include: - multiprocessing.context.AuthenticationError: digest received was wrong - RuntimeError: Pin memory thread exited unexpectedly - RuntimeError: DataLoader worker exited unexpectedly - Random SIGSEGV inside worker processes during video decode Switching to spawn re-imports modules cleanly in each worker and eliminates these failure modes. Added the setting as a config field rather than hard-coding so users on platforms where fork is preferred can opt back in via --dataloader-multiprocessing-context=fork. * Address review: shorten config comment, note spawn startup tradeoff Per @jashshah999, mention that spawn workers re-import modules and so add some startup time vs fork. Also trim the failure-mode dump from the inline comment — the linked issue covers the symptoms in detail. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * chore(scripts): add multiprocessing_context safeguards * chore(config): add libs note --------- Co-authored-by: 0o8o0-blip <0o8o0-blip@users.noreply.github.com>	2026-07-28 00:42:55 +02:00
Xingdong Zuo	95256d766d	feat(lekiwi): support LeKiwi in lerobot-replay CLI (#3739 ) Register the `lekiwi` robot module in `lerobot_replay.py` so episodes can be replayed on a LeKiwi via `--robot.type=lekiwi_client`. The module is already registered in `lerobot_calibrate.py` and `lerobot_setup_motors.py`; this fills the gap so the replay CLI recognizes the same robot. Replayed actions are loaded from the dataset as torch tensors, which `json.dumps` cannot serialize when `LeKiwiClient.send_action` ships them over ZMQ. Coerce each action value to a plain float before sending. This is scoped to the LeKiwi network client and does not affect any other robot. Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>	2026-07-27 19:29:44 +02:00
Thomas Landeg	fd53716688	fix(envs): make metaworld seeding reproducible (#3727 ) Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com>	2026-07-27 18:42:17 +02:00
Steven Palma	a96540a2c4	fix rollout policy revision loading (#4161 ) Co-authored-by: RaviTeja-Kondeti <rkondet3@asu.edu>	2026-07-27 18:20:39 +02:00
WOLIKIMCHENG	acd42b4d85	fix(processor): keep missing local state resolution local (#3715 ) Co-authored-by: root <kinsonnee@gmail.com> Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>	2026-07-27 15:53:31 +02:00
Steven Palma	bbeacfe57d	fix(record): connect teleoperator before robot to avoid watchdog jump (#4166 ) * fix(record): connect teleoperator before robot to avoid watchdog jump lerobot-record connected the robot before the teleoperator. A robot's connect()/reset() can leave it holding a default pose under a firmware watchdog (e.g. Unitree G1); if teleop.connect() (model loading, IK init, network setup) then takes longer than that watchdog, the joints drop to damping and the first send_action() makes the robot jump. Swap the order so the teleoperator connects first, matching the ordering already used in lerobot_teleoperate.py. Pure ordering fix, no API change. Fixes #3684 * fix(record): trim comment and add connect-order regression test Address review feedback on #3684: - Trim the verbose ordering comment down to two lines. - Add test_record_connects_teleop_before_robot to tests/test_control_robot.py, asserting teleop.connect() runs before robot.connect() in record(). * chore(test): remove test --------- Co-authored-by: Jaimin Patel <jpatel@tuvalabs.com> Co-authored-by: Martino Russi <77496684+nepyope@users.noreply.github.com>	2026-07-27 14:09:58 +02:00
Steven Palma	801346e18c	fix(scripts): restore policy training mode after eval_policy() in lerobot-eval (#4162 ) * fix(scripts/eval): restore policy training mode after eval_policy() `eval_policy` calls `policy.eval()` before the rollout but never restores the prior mode on return. When called from the training loop (`lerobot_train.py`'s `eval_policy_all -> run_one -> eval_one -> eval_policy` chain), the policy is left in eval mode for every subsequent training step, which silently: * disables Dropout (no regularisation), * freezes BatchNorm running stats (no further EMA updates). Under DDP only `is_main_process` runs eval (lerobot_train.py:527), so the main rank ends up in eval mode while workers stay in train mode — the all-reduced gradients then combine forward passes computed with different dropout masks and different BN behaviour, a real DDP-correctness issue. Scope of impact: * Affects every policy with Dropout in its forward path. In-tree, that includes the default ACT (6 Dropout layers at p=0.1), Diffusion (vision backbone), VQ-BeT, Multi-Task DiT, X-VLA, plus all VLA policies that inherit Dropout from their pretrained HF backbone (PI0/PI0.5/PI0-FAST, SmolVLA, GR00T-N1.5, EO1, Wall-X). * Triggers from the first eval onward. On the default config (steps=100k, eval_freq=20k) that's 80% of training; on the LIBERO / RoboCasa / VLABench example commands in docs/ (eval_freq=1k–5k) it's 95–99% of training. * Policies using only LayerNorm/GroupNorm and no Dropout (TDMPC, RTC) are unaffected. Policies using `FrozenBatchNorm2d` (ACT's ResNet backbone) are immune to the BN-stat half; the Dropout half still bites. Fix: * Snapshot `policy.training` on entry to `eval_policy`. * Restore it on normal return. * Save-and-restore is a strict no-op for callers that pass an already-eval-mode policy (e.g. the standalone `lerobot-eval` script loading a frozen checkpoint). * Restoration is placed before the normal return only, not in a try/finally — exception paths leave the policy in eval mode, same as today. A try/finally upgrade would require re-indenting ~165 lines and can land as a separate cleanup if desired. Tests (tests/scripts/test_eval.py, 7 tests total, ~1.6s): * Regression gates on the lerobot_eval fix itself: training-mode preservation, eval-mode preservation, dropout-active behavioural check, non-crash for both entry modes. * Quantitative mechanism demonstration (`test_missing_mode_restoration_hurts_generalisation`): trains a tiny Dropout+BatchNorm MLP under both the bug pattern and the fix pattern on identical data and seed, then asserts the buggy variant generalises at least 5% worse on a held-out val set. In repeated runs we see 10-25% deltas on this toy problem; real policies (more layers, more Dropout, longer training) generally see larger gaps. Lives alongside the regression tests so the empirical proof is reproducible from the repo without adding a separate benchmarks/ directory. * fix(scripts): keep policy train/eval --------- Co-authored-by: ModeEric <ericjm4@illinois.edu>	2026-07-27 14:08:11 +02:00
MihaiAnca13	ab87fd9764	fix(datasets): clear video frame staging on episode reset (#3683 ) * fix video frame staging cleanup on episode reset * linting --------- Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>	2026-07-27 13:44:32 +02:00
hf-dependantbot-rollout[bot]	6c57dfd2ee	chore: enable Dependabot weekly GitHub Actions bumps (#3677 ) Co-authored-by: hf-dependantbot-rollout[bot] <285970069+hf-dependantbot-rollout[bot]@users.noreply.github.com> Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>	2026-07-27 13:22:04 +02:00
Kohei SENDAI	d63e6e67a5	fix convverstion err (#3656 ) Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>	2026-07-27 11:54:09 +02:00
Steven Palma	0d383d09f2	feat(dataset): accept token argument for private HF Hub datasets (#4136 )	2026-07-24 18:51:35 +02:00
Caroline Pascal	ab2b5b04dd	(depth image processing): excluding depth frames from the RGB to BGR image processing (#4135 ) * (depth image processing): excluding depth frames from the RGB to BGR image processing * test(update): updating tests to include RGB/BGR conversion checks	2026-07-24 17:43:17 +02:00
Steven Palma	ac5c7b8600	chore(deps): bump diffusers to `>=0.38.0,<0.40.0` (#4145 ) * fix(deps): bump diffusers cap to <0.39.0 (security) Diffusers 0.35.x is affected by GHSA-98h9-4798-4q5v (HIGH, CVSS 8.8): 'trust_remote_code bypass via custom_pipeline and local custom components'. Fixed in diffusers 0.38.0. Current cap 'diffusers<0.36.0' blocks downstream consumers (e.g. strands-labs/robots) from picking up the security fix. The lerobot diffusers surface area is narrow and stable across 0.36-0.38: - diffusers.schedulers.scheduling_ddim.DDIMScheduler - diffusers.schedulers.scheduling_ddpm.DDPMScheduler - diffusers.optimization.get_scheduler - diffusers.ConfigMixin / ModelMixin / register_to_config - diffusers.models.attention.{Attention,FeedForward} - diffusers.models.embeddings.* None of these were removed, renamed, or had breaking changes in 0.36, 0.37, or 0.38 release notes. Bumping the cap to <0.39.0 unblocks the security fix while keeping a major-version safety bound. * chore(dependecies): bump diffusers * chore(deps): update uv.lock --------- Co-authored-by: Cagatay Cali <cagataycali@users.noreply.github.com>	2026-07-24 17:13:53 +02:00
Steven Palma	a6befef0ba	chore(dependencies): update uv.lock (#3963 )	2026-07-24 16:30:36 +02:00
Steven Palma	53843007ea	feat(robot): Make SO follower P coefficient configurable (#4142 ) * Make SO follower P coefficient configurable * chore(test): minimize tests * feat(robots): expose PID coeff in SO arms --------- Co-authored-by: taivu1998 <46636857+taivu1998@users.noreply.github.com>	2026-07-24 16:03:04 +02:00
Maxime Ellerbach	d3bed0feee	chore(agents): adding additional infos to AGENTS.md and bring-your-own-policies.mdx (#3904 ) * chore(agents): adding additional infos to AGENTS.md * adding `lerobot-train` requirement inside PR checklist * prefer using code already implemented from transformers / diffusers instead of re-implementing in tree --------- Signed-off-by: Maxime Ellerbach <maxime.ellerbach@huggingface.co>	2026-07-24 14:58:43 +02:00
Steven Palma	a0eb860d1e	feat(dataset): add slice support to LeRobotDataset.__getitem__ (#4129 ) * feat(dataset): add efficient slice support * fix(dataset): handle empty dataset slices * refactor(dataset): reuse scalar path for slices --------- Co-authored-by: Francesco Capuano <fc.francescocapuano@gmail.com>	2026-07-23 22:05:29 +02:00
Steven Palma	cfd9ff969c	fix(envs): set LiberoEnvConfig.fps default to 20 to match robosuite (#4124 ) * fix(envs): set LiberoEnvConfig.fps default to 20 to match robosuite LiberoEnvConfig.fps was set to 30, but the underlying robosuite OffScreenRenderEnv always runs at its default control_freq of 20 Hz since fps is never passed through. This mismatch silently decouples the dataset/eval loop rate from the actual simulation step rate. Set the default to 20 to match the real sim rate and avoid the footgun. Fixes #3368 * fix(libero): apply configured control frequency --------- Co-authored-by: xinmotlanthua <275663218+xinmotlanthua@users.noreply.github.com>	2026-07-23 19:49:19 +02:00
Steven Palma	f59eae4e27	fix(robots): add retries while recording motor ranges (#4126 ) * Add retries while recording motor ranges * fix(motors): throttle calibration reads consistently --------- Co-authored-by: tom-doerr <tomdoerr96@gmail.com>	2026-07-23 18:41:48 +02:00
Martino Russi	a993af9c51	fix(openarms): stop set_zero_position()ing on connect (#4058 ) * fix(damiao): make is_calibrated a plain property, not cached `is_calibrated` was a `@cached_property`, so it froze at its first-read value and never reflected later changes to `self.calibration` (set by connect/calibrate/load). This caused the OpenArm teleop to re-run calibration even when a calibration file existed, and to skip `set_zero_position()` after a fresh calibration. Switch to `@property` (matching the MotorsBus base contract and the Feetech/SO-100 buses) and drop the now-unused `functools.cached_property` import. Co-authored-by: Cursor <cursoragent@cursor.com> * don't set_zero_position() on connect --------- Co-authored-by: Cursor <cursoragent@cursor.com>	2026-07-23 18:34:13 +02:00
Steven Palma	392246feaf	feat(diffusion): add gradient checkpointing for memory optimization (#4127 ) * feat(diffusion): add gradient checkpointing for memory optimization Add gradient_checkpointing config option to DiffusionPolicy. When enabled, wraps the UNet encoder, mid, and decoder residual blocks with torch.utils.checkpoint.checkpoint to trade compute for memory. Allows training with larger batch sizes or higher-resolution inputs on memory-constrained GPUs. Disabled by default. Usage: --policy.gradient_checkpointing=true Part of the 0.6.0 roadmap item 3.3 (gradient checkpointing for all policies). * test(diffusion): verify gradient checkpointing parity --------- Co-authored-by: Jash Shah <jashshah.999@gmail.com>	2026-07-23 18:33:10 +02:00
Steven Palma	19dcbc19f1	fix(gamepad): Gamepad on macos often does not need fallback (#4125 ) * gamepad does often work on macos * review comments * fix(gamepad): expose hidapi fallback in config --------- Co-authored-by: Maxim Bonnaerens <maxim@bonnaerens.be>	2026-07-23 18:21:48 +02:00
Steven Palma	679faeaafc	fix(scripts): register third-party plugins in lerobot_setup_motors (#4123 ) * fix(scripts): register third-party plugins in setup-motors * test(setup-motors): cover plugin registration --------- Co-authored-by: Janos von Gencsy <janos.von-gencsy@tum.de>	2026-07-23 18:06:44 +02:00
YK	228cb5ddb9	Fix missing periods at end of sentences in README (#3473 ) Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>	2026-07-23 16:07:58 +02:00
Eunsung Kim	ad176c6d41	Feature omx docs (#3421 ) * docs(omx): add header and omx image in docs * fix(docs):adjust image size in omx docs --------- Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>	2026-07-23 15:25:46 +02:00
Duhyeon, Kim	d6c605e8c5	refactor(pi05): remove unused variables in embed_suffix method (#3263 ) * refactor(pi05): remove unused variables in embed_suffix method * Refactor embed_suffix to streamline pad_masks handling Removed unused pad_masks list and simplified its creation. Signed-off-by: Duhyeon, Kim <49020301+dudududukim@users.noreply.github.com> --------- Signed-off-by: Duhyeon, Kim <49020301+dudududukim@users.noreply.github.com> Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>	2026-07-23 14:37:57 +02:00
Pepijn	9c82c39c7b	feat(annotate): run lerobot-annotate on HF Jobs via --job.target (#4095 ) * feat(annotate): run lerobot-annotate on HF Jobs via --job.target Annotation needed a hand-edited launcher script (examples/annotations/run_hf_job.py) to reach a GPU: users copied it, rewrote the embedded CMD string for their dataset, and ran it with `python`. Fold that into the CLI instead, mirroring `lerobot-train`: `lerobot-annotate --job.target=h200` submits the exact command you'd run locally. - AnnotationJobConfig extends JobConfig with the annotation runtime's defaults (vllm/vllm-openai image, 2h cap) plus --job.lerobot_ref, so an unmerged branch can be exercised remotely without editing a script. - lerobot.jobs.annotate builds the pod command by replaying the user's own CLI flags (minus --job./--root, with --repo_id re-emitted from the config) after a setup prelude that installs lerobot on top of the vLLM image. Job monitoring, log tailing and Ctrl-C-detaches reuse the training submitter's plumbing. - Remote runs require --repo_id; a local-only dataset is pushed privately first. The generated pod command is byte-for-byte the script's old CMD. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> fix(annotate): reject client-side config files on remote runs draccus exposes `--config_path` plus a `--<field>` config-file arg for every nested dataclass (`--vlm`, `--plan`, `--job`, ...). All name files on the client's disk, so forwarding them to the pod silently dropped whatever settings they carried. Reject them up front instead. Bare `--job` also slipped past the `--job.` prefix filter, so a `--job=cfg.yaml` holding `target: h200` would have reached the pod and had the job submit a job of its own, recursively. It is dropped from the forwarded args as well. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> * refactor(jobs): share the submit-and-follow loop between both submitters `submit_annotate_to_hf` reused the leaf helpers (`_poll_until_done`, `_tail_logs`, `_pod_forwarded_args`) but duplicated the orchestration around them: ~40 of the 50 lines that spawn the poll/log threads, install the Ctrl-C-detaches handler and raise on a non-COMPLETED stage were identical in both files. Extract that into `follow_job(job_id, *, detach, success_marker=None) -> bool`, returning True when the job finished and False when we stopped watching without a verdict (detach or Ctrl-C). Training keeps its model-pushed marker by passing it in; annotation has no equivalent line (the CLI keeps working after the upload log to write the card and tag) so its completion stays stage-based. Kept in hf.py rather than a new module so every existing monkeypatch target in test_hf.py still resolves. Behaviour change: a training run whose job reaches COMPLETED without the marker matching now prints its completion line instead of returning silently. The marker was already documented as an optimisation with a stage-based fallback; the fallback just never reported success. Tests: adds annotate coverage for the non-detach path (completion and failure) — previously only ever exercised with detach=true — plus a detach short-circuit test. Both new annotate tests verified to fail under a mutation that stubs out follow_job. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>	2026-07-23 10:30:33 +02:00