Top-down flow (read episodes → 3 modules fan out → validator → writer →
parquet) with aligned boxes, instead of the cramped bordered version.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Rewrite annotation_pipeline.mdx in plainer, easier-to-read language
(shorter sentences, active voice, a plain-text intro), add an ASCII
'How it fits together' architecture diagram, and remove the
'Reproducibility via seed and prompt hashes' section. Content/links are
preserved; only wording and structure change.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The example already pins '@main'; update the doc step and the script
docstring from 'the branch under test' to 'lerobot (from main)' now that
the pipeline is merging to main.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Bugs
* validator: don't re-raise on unknown style. The second column_for_style
lookup (used to route persistent vs event) now sits in try/except so an
unknown style is recorded by _check_column_routing and skipped instead
of crashing the whole validation pass.
* general_vqa._target_cameras: when restrict_to_default_camera is set but
the configured camera_key isn't one the provider exposes, warn and fall
back to all cameras instead of returning a phantom key that KeyErrors
deep in frame decode.
* interjections: clamp interjection timestamps to frame_timestamps[0]
rather than a hardcoded 0.0 (datasets can start at non-zero t).
Docs / code drift
* annotation_pipeline.mdx: drop the phantom 'vocabulary discovery / phase
0 / --vocabulary.* / canonical_vocabulary.json' section (none of it
exists); describe the real describe->segment + coverage-stitch flow.
Soften the src/lerobot/tools/ + TOOL_REGISTRY reference to 'not part of
this PR' (matches tools.mdx, which already marks the runtime layer as
not-yet-implemented). Fix the --push_to_hub/--new_repo_id wording. Note
the default is now a single h200. Add a 'Contributing new modules'
section inviting module / prompt / quality contributions.
* executor docstring: six phases, no phantom phase 0.
run_hf_job.py
* add the Apache 2.0 license header (was flagged repeatedly).
* default to a single GPU: flavor=h200, parallel_servers=1, num_gpus=1
(scale to h200x4 noted in the docstring).
* pin the install to @main instead of the feature branch (won't break
after merge).
Naming / cleanup
* rename dest_repo_id -> new_repo_id across config / script / example /
test to match the LeRobot dataset edit tools.
* rename prompt templates module_N_*.txt -> descriptive (plan_*,
interjections_*, vqa.txt) and update every load_prompt() call.
* remove dead _messages_to_prompt (used only by the removed in-process
backends).
* declare _warned_decode_fail (frames) and _warned_no_camera (vqa) as
real init=False dataclass fields instead of getattr monkey-patches.
* scope bandit B607 to the two ffmpeg subprocess.run sites via
'# nosec B607' and drop it from the global skip list.
Tests
* fix stale canned-VLM markers ('ONE realistic interruption' ->
'compact interjection', 'Update the memory' -> 'compressed semantic
memory') and drop the dead 'concise hierarchical PLAN' plan responders
(plan generation is deterministic now) in run_e2e_smoke,
test_pipeline_recipe_render, test_modules.
* run_e2e_smoke now asserts interjection + speech rows are produced so a
stale marker can't silently pass again.
* drop remaining 'PR 1' / 'PR 2' references from test comments / names.
Verified: tests/annotations + tests/datasets/test_language +
tests/scripts/test_lerobot_annotate (31 passed); make-style E2E smoke
(interjections=1 speech_atoms=2); pre-commit (ruff, mypy, bandit,
prettier) clean.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The shipped workflow is Hugging Face Jobs (examples/annotations/run_hf_
job.py): it serves the model with vLLM in the vllm/vllm-openai image and
the pipeline talks to it over the OpenAI-compatible API. The in-process
vllm / transformers local backends added surface (and the vllm
one pinned an old torch) without being part of that path, so they're
removed for now.
* vlm_client.make_vlm_client: keep only backend='openai' (+ 'stub'
rejected with the usual guidance). Requesting 'vllm'/'transformers'
now raises a clear 'not supported for now — use the HF Jobs flow'
error. Removed _make_vllm_client and _make_transformers_client.
* config: backend docstring updated (openai-only); default model_id
bumped to Qwen/Qwen3.6-27B to match run_hf_job.
* docs/annotation_pipeline.mdx: remove the '## Running locally'
section; the launcher description now says one vLLM server per GPU
over the OpenAI API, and the 'One Qwen-VL pass' note drops the
'vLLM/transformers fallback' wording.
Tests are unaffected (they construct StubVlmClient directly; nothing
referenced the removed backends).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The docs pointed at src/lerobot/datasets/v30/, which does not exist.
Both scripts actually live in src/lerobot/scripts/:
- convert_dataset_v21_to_v30.py
- augment_dataset_quantile_stats.py
Updated the four references (one python -m module path and three
file-path invocations) to the correct location, matching each
script's own usage docstring.
Quality-gate fix: ruff-format/markdown prettier hook reflow of the
annotation pipeline doc. No content change.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* feat(rewards): add TOPReward reward model
* refactor(rewards): clean up TOPReward processor/model
* fix(rewards/topreward): add missing input keys mm_token_type_ids
* fix(rewards/topreward): fix pyproject extra typo and simplify processor (#3653)
Add lerobot[topreward] extra to all in
pyproject.toml, drop the redundant labels arg in scoring, and
collapse the dead-branch shape check in the encoder processor.
* optmize topreward input processing (#3660)
---------
Co-authored-by: Cole <91766445+jcoleharrison@users.noreply.github.com>
Co-authored-by: Haoming Song <haomingsong24@gmail.com>
Resolves conflicts from 32 commits on main:
* docs/source/_toctree.yml — keep both new toc entries
(annotation_pipeline + video_encoding_parameters).
* docs/source/language_and_recipes.mdx — adopt main's section
ordering (Layer 2 before "Temporal semantics") and float32
timestamp dtype to match the codebase.
* src/lerobot/configs/__init__.py — keep both export sets
(recipe + video encoder).
* src/lerobot/datasets/dataset_metadata.py — drop redundant lazy
imports (top-level imports cover both LANGUAGE_COLUMNS and
DEFAULT_TOOLS); adopt main's @tools.setter for info.json
write-back.
* src/lerobot/datasets/feature_utils.py — call the real
validate_feature_language() instead of returning "".
* src/lerobot/datasets/language.py — float32 timestamps to match
pa.float32() used in video_utils.py and the rest of the codebase.
* src/lerobot/datasets/language_render.py — adopt main's
unwrap_scalar() helper (drops two hand-rolled .item()/list
unwrappers); float32 in docstring.
* src/lerobot/processor/render_messages_processor.py — drop
PR-local _scalar() helper, use shared unwrap_scalar().
* tests/datasets/test_language.py — adopt main's new float32 dtype
+ validate_feature_language warning tests.
* tests/datasets/test_dataset_metadata.py — adopt main's new
tools.setter persist/clear tests.
* uv.lock — regenerated cleanly from main's resolver.
90 of 92 touched tests pass. Two pre-existing test failures
(test_module1_plan_memory_subtask_smoke,
test_module2_mid_episode_emits_paired_interjection_and_speech in
tests/annotations/test_modules.py) are unrelated to this merge —
that test file doesn't exist on main, so the failures originate on
the branch and are addressed by the 8 newer fix(annotate) commits
already on origin that will land in a follow-up.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Hardcoding ``n_subtask_target=10`` and ``n_memory_target=6`` baked task
complexity into the config — a simple pick-and-place needs ~6, a
multi-step recipe needs ~20. The VLM already sees the clips, so let it
pick the count itself from what's recurring across episodes.
Drop both knobs from ``VocabularyConfig`` and the ``module_0_vocabulary``
prompt template. The prompt now says "decide the count yourself based
on what you see — the smallest set that still covers every recurring
phase" and adds an "each label must recur across the demos" rule so
the VLM filters out one-off motions.
Update the launcher script + docs to remove the old knobs.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Co-authored-by: Cursor <cursoragent@cursor.com>
The pipeline previously emitted near-unique subtask + memory phrasings
per episode (free-form LLM rephrasing). On the downstream low-level
policy that collapses the action expert's conditioning to noise: every
episode pairs a different paraphrase with similar motions, so the
expert learns a flat scene-prior that ignores the subtask string —
then at inference the high-level head invents *yet another* paraphrase
and the expert produces tiny "uncertain hover" chunks.
Add a vocabulary-discovery phase (phase 0) that runs once per dataset:
- watches the first ``vocabulary.sample_episodes`` (default 3)
episode videos as one Qwen-VL prompt,
- asks the VLM to derive ~``n_subtask_target`` canonical imperative
subtask labels and ~``n_memory_target`` first-person past-tense
memory milestones that recur across the demos,
- persists them to ``meta/canonical_vocabulary.json`` (human-
inspectable, hand-editable), and
- wires the resulting ``Vocabulary`` into the ``plan`` module so
every per-episode subtask + memory call is constrained to those
exact strings (both as prompt-side instructions *and* post-VLM
validation: paraphrases snap to the closest canonical entry via
token-set overlap; below a 0.5 Jaccard floor the subtask is
dropped rather than warped into something semantically wrong).
Operator workflow:
- first run discovers the vocabulary, writes the JSON, and runs
the ``plan`` module against it,
- subsequent runs reuse the on-disk file (``reuse_existing=True``
default) so hand-edits stick,
- set ``--vocabulary.enabled=False`` to fall back to free-form
generation (the original behaviour).
The discovery prompt forbids gerunds / third-person / adverbs and
caps the lists to the requested counts, matching the Hi-Robot /
π0.6-MEM convention of small per-environment vocabularies. The
``plan`` module's subtask + memory prompts grow a conditional
``{vocabulary_block}`` slot rendered only when a vocabulary is
present; without one the templates collapse to their previous
free-form form.
Tests: 11 new unit tests under tests/annotations/test_vocabulary.py
cover the on-disk round-trip, discovery against the fixture dataset,
``reuse_existing`` short-circuit, paraphrase canonicalisation, off-
vocab subtask dropping, and the no-vocabulary pass-through path.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Co-authored-by: Cursor <cursoragent@cursor.com>
- Fixed broken API examples in Lerobot Imitation Learning Documentation
- Teleoperation with cameras improved by adding a fixed frequency in the loop (without it the cameras feed gets very slow)
- Wrapped record example script in main() to avoid problems on Mac
- Previously teleoperation example was using SO-ARM and teleoperation with cameras was using Koch. I changed it to use SO-ARM in all of the examples.
- Added section on how to train with HF Jobs - CLI and Python examples
- Replaced lerobot-record with lerobot-rollout in policies examples
* Add extensive language support
* Address review: split persistent/event schemas, drop event timestamps
- recipe.py: derive _VALID_ROLES/_VALID_STREAMS from MessageRole/MessageStream Literals
- dataset_metadata.py: keep CODEBASE_VERSION at v3.0
- language.py: remove RESERVED_STYLES; split arrow/feature schemas into
persistent (with timestamp) and event (without timestamp); add docstrings
- language_render.py: events use frame-row timestamp implicitly; no
per-event timestamp filtering or sorting
- converters.py: drop unused subtask_key passthrough
- add docstrings to new public APIs (recipe, render_messages_processor, collate)
- update tests for split schemas; revert uv.lock
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* Add docstrings to all new helpers; revert uv.lock
Covers private helpers in recipe.py, language.py, language_render.py,
and render_messages_processor.py. Also reverts uv.lock to main (it was
re-generated by `uv run` during local checks).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* feat(language): add motion (persistent) and trace (event-only) styles
Promote the previously-reserved motion/trace styles to first-class core
styles. motion routes to language_persistent (it tracks robot state over
time); trace routes to language_events (single-moment annotations).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* feat(language): per-camera tagging on view-dependent styles
Adds a nullable `camera` field to the language row struct (both persistent
and event variants) so view-dependent styles like `vqa` can carry which
`observation.images.*` view they were grounded against. Without this,
multi-camera datasets ended up with multiple `(vqa, role)` rows at the
same timestamp that the resolver could not disambiguate.
- `language.py`: add `camera` to PERSISTENT_ROW_FIELDS / EVENT_ROW_FIELDS,
to both Arrow struct types and the HF datasets feature mappings;
introduce VIEW_DEPENDENT_STYLES = {vqa, motion, trace} plus
`is_view_dependent_style` and `validate_camera_field` helpers (camera
required iff style is view-dependent).
- `language_render.py`: thread an optional `camera=` kwarg through every
resolver (`active_at`, `emitted_at`, `nth_prev`, `nth_next`) and through
`_matching_rows` / `_select_*`, so recipes can disambiguate per-camera
VQA with `emitted_at(t, style=vqa, role=assistant, camera=...)`.
Without a `camera` filter, multi-row matches keep raising the existing
ambiguity error — which is the desired behaviour on multi-camera data.
- `recipes/pi05_hirobot.yaml`: replace the single `ask_vqa` branch with
`ask_vqa_top` and `ask_vqa_wrist` per-camera sub-recipes (each carrying
the matching image block), keeping the original 0.20 budget and
documenting the customization point for datasets with different cameras.
- Tests: schema test asserts the new field order; new tests cover
`is_view_dependent_style`, `validate_camera_field` (both required and
forbidden directions), per-camera `emitted_at` filtering, and the
ambiguity error when two cameras emit `(vqa, assistant)` at the same
timestamp without a `camera=` filter. RenderMessagesStep + dataset
passthrough fixtures updated to include the new field.
- `docs/source/language_and_recipes.mdx`: document the `camera` field,
the per-camera resolver pattern, and the canonical recipe convention.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* fix(language): drop motion from VIEW_DEPENDENT_STYLES
Motion primitives are described in robot-frame (joint / Cartesian) terms,
not pixel space, so they are camera-agnostic. Only `vqa` (event) and
`trace` (event, pixel-trajectory) are view-dependent.
The `camera` field stays on PERSISTENT_ROW_FIELDS for schema symmetry —
the validator, resolver, and HF feature mapping behave identically across
the two columns regardless of which styles populate `camera` today —
but persistent rows now always have `camera=None` in practice.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* feat(language): task_aug style + automatic ${task} rephrasing rotation
Adds task-prompt diversity (Xiao 2022 / CAST) without touching
``meta/tasks.parquet`` or forcing recipes to opt in. The plan reserved
``task_aug`` as a future style; this lands it now.
- ``language.py``: add ``task_aug`` to ``CORE_STYLES`` and
``PERSISTENT_STYLES``. ``column_for_style("task_aug")`` returns
``language_persistent`` so PR 2 writers route it correctly.
- ``language_render.py``: ``_resolve_task`` now consults the persistent
slice for rows of ``style="task_aug", role="user"``. When any exist
it picks one deterministically by ``sample_idx`` (blake2b-keyed, not
Python's randomized hash) so an epoch sees every rephrasing of every
episode while the same sample still resolves identically across
reruns. Falls back to the canonical ``meta/tasks.parquet`` task when
no rephrasings are present, so existing datasets and unannotated runs
keep their behaviour. Explicit ``task=`` overrides still win.
- Tests: rephrasing coverage across samples, determinism on repeat
``sample_idx``, fallback when persistent has no ``task_aug`` rows,
and explicit override priority.
Recipes get this for free: any ``${task}`` placeholder rotates through
the available rephrasings. Recipes that want the literal canonical task
can override the binding.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* feat(language): tool catalog in meta/info.json + LeRobotDatasetMetadata.tools
Stores OpenAI-style function schemas at ``meta/info.json["tools"]`` so
datasets can declare which tools are available (today: just ``say``;
tomorrow: per-dataset extensions). The ``DEFAULT_TOOLS`` constant
fills in for unannotated datasets so chat-template consumers don't
have to special-case anything.
Three pieces:
- ``language.py``: ``SAY_TOOL_SCHEMA`` and ``DEFAULT_TOOLS``
constants. Single source of truth — PR 2's writer and PR 3's
runtime tool registry will both import from here instead of
duplicating the dict.
- ``dataset_metadata.py``: ``LeRobotDatasetMetadata.tools`` property
reads ``info.json["tools"]`` and falls back to ``DEFAULT_TOOLS``.
Returns deep-copied dicts so callers can mutate the result safely.
- ``docs/source/tools.mdx``: spec page covering the catalog, per-row
invocations, and the three-step "how to add a new tool" workflow
(declare schema, implement, register). Linked from the docs
toctree under the Datasets section.
This lays the groundwork for PR 2's pipeline writing the catalog out
during annotation, and PR 3's ``src/lerobot/tools/`` package shipping
runnable implementations (one file per tool — first up:
``say.py`` wrapping Kyutai's pocket-tts).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* Apply ruff and prettier formatting after merge
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* refactor(language): unify resolver dispatch and prune redundant test scaffolding
* Drop the unused `events` kwarg from `active_at`/`nth_prev`/`nth_next`;
only `emitted_at` actually consults events. The dispatcher in
`_resolve_spec` now passes events conditionally.
* Replace the dual `_persistent_sort_key`/`_event_sort_key` pair with a
single `_row_sort_key` and drop the `sort_key` parameter from
`_select_one`. Event rows lack `timestamp` (it is implicit in the
frame) and now default to `0.0` for sort purposes — the
`(style, role)` tiebreaker is unchanged.
* Inline `_select_latest` into `active_at` (its only caller).
* Collapse `emitted_at`'s dual-branch into one `_select_one` call.
* Tighten `_validate_persistent_resolver` to a single
`column_for_style(style) != LANGUAGE_PERSISTENT` check.
* Parameterize `test_per_camera_blend_renders_both_views` over the two
cameras and factor the sub-recipe builder into `_vqa_subrecipe` so
the test no longer hand-rolls two near-identical recipe blocks.
Net -98 LOC; behavior, public resolver names, and test expectations
unchanged.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* fix(language): always raise on ambiguous resolver matches
`_select_one` previously skipped its ambiguity check whenever any of
`role`/`tool_name`/`camera` was set, on the assumption that the caller
had already pinned down a unique row. That left a real ambiguity hole
for VQA: with two cameras emitting `(vqa, assistant)` at the same
frame, `emitted_at(..., role="assistant")` silently picked the first
sorted row instead of telling the recipe to add `camera=...`. The
existing `test_emitted_at_raises_on_ambiguous_per_camera_vqa` test
already encoded the desired behavior.
Tighten the check: any time `len(rows) > 1` we now raise with the
selectors echoed back, so users see exactly which fields they passed
and that more is needed to disambiguate.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* chore: fix CI — collapse short ValueError to one line, refresh uv.lock
* `ruff format` on CI (newer version) wants the short `camera=None`
ValueError on a single line.
* `uv.lock` was stale relative to `pyproject.toml`'s `datasets>=4.7.0`
pin (and picked up upstream `s390x` marker fixes for cuda packages).
CI runs `uv sync --locked` which rejected the divergence.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* fix(language): keep base install green — drop processor re-export, gate dataset-extra tests
`lerobot.processor` re-exported `RenderMessagesStep` at the package
level, so importing anything from `lerobot.processor` pulled in
`lerobot.datasets.language` → `lerobot.datasets/__init__.py` →
`require_package("datasets")`, which fails in the Tier 1 base install
that intentionally omits the `[dataset]` extra. The chain bricked
collection for unrelated suites (`tests/policies/pi0_pi05/...`,
`tests/envs/...`, etc.).
* Stop re-exporting `RenderMessagesStep` from `lerobot.processor`. The
only consumer (the test) already imports from the submodule.
Document the deliberate omission in the module docstring.
* Add `pytest.importorskip("datasets", ...)` (and `pandas` where
needed) at the top of the four PR-added tests that exercise the
language stack:
- tests/datasets/test_language.py
- tests/datasets/test_language_render.py
- tests/processor/test_render_messages_processor.py
- tests/utils/test_collate.py
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* fix(language): address review — tools accessor, motion docs, conditional collate
* **`meta.tools` actually reads `info.json["tools"]`.** `DatasetInfo`
had no `tools` field, so `from_dict` silently dropped the key (it
warned about unknown fields then discarded them) and the property
always returned `DEFAULT_TOOLS`. Added `tools: list[dict] | None`
to the dataclass; `to_dict()` drops it when unset so existing
datasets keep a clean `info.json`. Fixed the accessor to read
`self.info.tools` (the previous `.get(...)` would have raised
AttributeError on the dataclass anyway). Added regression tests:
fallback when absent, round-trip from disk, and round-trip
through `DatasetInfo.from_dict` / `to_dict`.
* **`motion` is not view-dependent — fix the docs.** The mdx claimed
rows of style `motion` must carry `camera`, but `VIEW_DEPENDENT_STYLES
= {"vqa", "trace"}` and the validator agrees: motion primitives are
joint/Cartesian-frame, not pixel-space. Updated both call-out
paragraphs in `language_and_recipes.mdx`.
* **Conditional `collate_fn` swap.** Added `meta.has_language_columns`
and gate the `lerobot_collate_fn` swap in `lerobot_train.py` on it,
so non-language datasets keep PyTorch's `default_collate`. Also
added a pass-through test in `test_collate.py` that asserts on a
plain tensor batch the custom collate matches `default_collate`
key-for-key, plus a test for the `None`-sample drop path.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* review: dedupe regex, centralize column names, harden collate, more tests
* **#2 — dedupe `_PLACEHOLDER_RE`.** The same regex was compiled in
`recipe.py` and `language_render.py`. Promote to module-level
`PLACEHOLDER_RE` in `recipe.py` (its primary owner — declares
template syntax) and import from `language_render.py`.
* **#3 — centralize language column names.** `io_utils.py` had
hardcoded `{"language_persistent", "language_events"}` literals at
two sites. Replace with `LANGUAGE_COLUMNS` import so a future column
rename can't silently desync.
* **#4 — defensive collate preserved-keys.** `lerobot_collate_fn`
silently filtered language fields from samples that didn't have
them, which would hand downstream consumers a preserved list
shorter than the tensor batch. Now: if any sample carries a key,
every sample in the batch must carry it; otherwise raise a
`ValueError` so the upstream rendering bug surfaces at the boundary.
* **#5 — `_scalar` rejects non-singleton lists.** Previously a zero-
or multi-element list fell through and triggered confusing
`float([])` errors downstream. Now raises `ValueError` with the
actual length.
* **#6 — refactor `_extract_complementary_data`.** Replace 11 lines
of `key = {... if ... else {}}` plus an 11-line splat dict with a
single `_COMPLEMENTARY_KEYS` tuple iterated once.
* **#7 — document `EXTENDED_STYLES`.** Was an empty `set()` with no
comment. Add a docstring explaining it's an intentional extension
point: downstream modules append project-local styles before
`column_for_style` is called.
* **#9 — `tools.mdx` notes the runtime layer is future work.** The
page referenced `src/lerobot/tools/`, `registry.py`, and
`get_tools(meta)` — none exist in this PR. Added a callout at the
start of "How to add your own tool" plus a note on the
implementations paragraph.
* **#10 — tests for YAML round-trip, malformed rows, blend
validation.** `test_recipe.py` grew from 1 case to 12 covering:
blend-or-messages exclusivity, target-turn requirement, blend
emptiness, weight presence/positivity, nested-blend rejection,
`from_dict` with nested blends, `from_yaml` / `load_recipe`
agreement, top-level non-mapping rejection. Added a malformed-row
test for `_normalize_rows` that asserts non-dict entries raise
`TypeError`.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* review: emitted_at uses 0.1s tolerance; MessageTurn requires stream at construction
* **Float tolerance in `emitted_at` for persistent styles.** The
``_timestamp(row) == t`` exact-equality check silently missed any
caller that derived ``t`` arithmetically (e.g. ``frame_idx / fps``)
even though the parquet timestamp would only differ by ULPs. Added
``EMITTED_AT_TOLERANCE_S = 0.1`` and check ``abs(...) <= tolerance``
instead, with a docstring explaining why exact equality wasn't
enough and why 0.1 s is safe at typical 30–100 Hz control rates.
Test asserts the new behavior at half-window (matches) and
double-window (no match) using the constant so it stays in sync.
* **`MessageTurn.stream` is required at construction.** It was typed
``MessageStream | None = None`` so YAML could omit ``stream:`` and
pass the dataclass invariant — but ``_validate_rendered`` rejected
``None`` streams later, surfacing the error at the first sample
instead of at recipe load. Now ``__post_init__`` raises
``ValueError`` if ``stream`` is ``None``, with the list of valid
streams in the message. The redundant late-stage check in
``_validate_rendered`` is replaced with a one-line comment that
cites the upstream invariant. Test pins the new construction-time
rejection.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* docs(tools): drop follow-up-PR references
Reword the two callouts in `tools.mdx` to describe the runtime layer
in present tense ("not part of the catalog layer shipped today",
"those modules don't yet exist in the tree") instead of pointing at a
specific follow-up PR. Keeps the doc honest about what works now
without coupling it to a particular release order.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* review: address CarolinePascal feedback
- language timestamps: float64 -> float32 to match LeRobotDataset frame
timestamps (Arrow struct + HF feature)
- dataset_metadata: hoist `.language` imports to module top — language.py
has no lerobot imports, so there is no circular-import risk
- dataset_metadata: add a `meta.tools` setter that persists the catalog to
info.json and reloads `meta.info`
- feature_utils: validate the `language` dtype instead of returning "" —
warn (non-fatal) when a non-empty value is written at record time
- centralize the scalar-unwrap helper as `lerobot.utils.utils.unwrap_scalar`,
shared by render_messages_processor and language_render
- docs: move `## Layer 2 — recipe anatomy` ahead of the resolver sections,
which describe recipe bindings rather than dataset layout
- language_render: note in EMITTED_AT_TOLERANCE_S that persistent rows change
on a human-action timescale, not the camera frame rate
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* feat(robots): natively integrate Seeed Studio reBot B601-DM arm
Add first-class LeRobot support for the Seeed Studio reBot arm, replacing
the out-of-tree `lerobot-robot-seeed-b601` / `lerobot-teleoperator-rebot-arm-102`
plugin packages.
New devices:
- robot `rebot_b601_follower` — single-arm B601-DM follower (6-DOF + gripper,
Damiao CAN motors via `motorbridge`)
- robot `bi_rebot_b601_follower` — bimanual follower composing two single arms
- teleoperator `rebot_102_leader` — single-arm StarArm102 / reBot Arm 102 leader
(FashionStar UART servos via `motorbridge-smart-servo`)
- teleoperator `bi_rebot_102_leader` — bimanual leader composing two single arms
The bimanual variants reuse the single-arm classes and namespace each arm's
observation/action keys with `left_` / `right_` prefixes, so a bimanual
StarArm102 leader can teleoperate a bimanual reBot B601 follower.
Optional SDK imports are guarded; a `rebot` extra installs `motorbridge` and
`motorbridge-smart-servo`.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* docs: add reBot B601-DM calibration & dual-arm teleoperation guide
Add docs/source/rebot_b601.mdx covering single-arm and bimanual
calibration and teleoperation for the reBot B601-DM follower and
reBot Arm 102 leader, with zero-position reference images from the
Seeed Studio wiki. Register the page in the docs toctree.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* docs: fix reBot B601 MDX build (move JSON example out of <Tip>)
The doc-builder parses `{...}` inside MDX component children as a
Svelte expression, so the joint_directions JSON example broke the
build. Move it into a top-level fenced code block.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* docs: apply prettier formatting to reBot B601 page
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* docs: remove duplicate colocated reBot B601 page
docs/source/rebot_b601.mdx is the canonical, toctree-registered page;
the colocated rebot_b601.md was a redundant thinner copy.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* docs: clarify 6-DOF leader fallback comment in reBot B601 follower
Explain that holding wrist_yaw at zero is what lets a 6-DOF leader
(e.g. so100_leader / so101_leader) teleoperate the 7-DOF follower.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* refactor: address Caroline's PR review on reBot B601 integration
- leader: remove _validate_config (no other lerobot device validates its
config; a key mismatch now surfaces as a plain KeyError)
- leader: simplify _round_to_valid_range to direct modular arithmetic
instead of a bidirectional search loop
- leader: inline the single-use _clamp helper
- follower & leader: write MotorCalibration range_min/range_max from the
configured joint_limits / joint_ranges instead of a fixed [-90, 90]
- docs: add a "Find the USB ports" section (lerobot-find-port) and move
the brltty/permissions tip there; link the OpenArm page for SocketCAN
adapter configuration
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* Enhance documentation with Lance format details
Added information about Lance format and `lerobot-lancedb` package for multimodal AI datasets.
Signed-off-by: Quentin Lhoest <42851186+lhoestq@users.noreply.github.com>
- name the three modules everywhere (plan / interjections / vqa) instead
of module_1/2/3 — config classes, config fields, executor params,
staging keys and phase names now carry the module name
- rename examples/annotation -> examples/annotations; add the Apache
header to run_hf_job.py
- drop the unused GeneralVqaModule._generate_one
- remove "PR 1" references from comments/docstrings
- frames.py: rely on the always-defined LeRobotDatasetMetadata.camera_keys
- executor.py: read/write meta/info.json via load_info / write_info
- reader.py: load meta/tasks.parquet via io_utils.load_tasks
- make --push_to_hub a bool; push the annotated dataset back to --repo_id
- move the on-disk test dataset builder into tests/fixtures
(build_annotation_dataset); run_e2e_smoke reuses it
- clarify in the docs that the vqa module grounds each pair on a single
frame (K = per-tick anchor count)
- hoist stdlib dynamic imports to module scope
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* chore(video backend): renaming codec into video_backend in get_safe_default_video_backend()
* feat(pyav utils): adding suport for PyAV encoding parameters validation
* feat(VideoEncoderConfig): creating a VideoEncoderConfig to encapsulate encoding parameters
* feat(VideoEncoderConfig): propagating the VideoEncoderConfig in the codebase
* chore(docs): updating the docs
* feat(metadata): adding encoding parameters in dataset metadata
* fix(concatenation compatibility): adding compatibility check when concatenating video files
* feat(VideoEncoderConfig init): making VideoEncoderConfig more robust and adaptable to multiple backends
* feat(pyav checks): making pyav parameters checks more robust
* chore(duplicate): removing duplicate get_codec_options definition
* test(existing): adapting existing tests
* test(new): adding new tests for encoding related features
* chore(format): fixing formatting issues
* chore(PyAV): cleaning up PyAV utils and encoding parameters checks to stick to the minimun required tooling.
* chore(format): formatting code
* chore(doctrings): updating docstrings
* fix(camera_encoder_config): Removing camera_encoder_config from LeRobotDataset, as it's only required in LeRobotDatasetWriter.
* feat(default values): applying a consistent naming convention for default RGB cameras video encoder parameters
* fix(rollout): propagating VideoEncoderConfig to the latest recording modes
* chore(format): formatting code, fixing error messages and variable names
* fix(arguments order): reverting changes in arguments order in StreamingVideoEncoder
* chore(relative imports): switching to relative local imports within lerobot.datasets
* test(artifacts): cleaning up artifacts for the video encoding tests
* chore(docs): updating docs
* chore(fromat): formatting code
* fix(imports): refactoring the file architecture to avoid circular imports. VideoEncoderConfig is now defined in lerobot.configs and lazily imports av at runtime.
* fix(typos): fixing typos and small mistakes
* test(factories): updating factories
* feat(aggregate): updating dataset aggregation procedure. Encoding tuning paramters (crf, g,...) are ignored for validation and changed to None in the aggregated dataset if incompatible.
* docs(typos): fixing typos
* fix(deletion): reverting unwanted deletion
* fix(typos): fixing multiple typos
* feat(codec options): passing codec options to lerobot_edit_dataset episode deletion tool
* typo(typo): typo
* fix(typos): fixing remaining typos
* chore(rename): renaming camera_encoder_config to camera_encoder
* docs(clean): cleaning and formating docs
* docs(dataset): addind details about datasets
* chore(format): formatting code
* docs(warning): adding warning regarding encoding parameters modification
* fix(re-encoding): removing inconsistent re-encoding option in lerobot_edit_dataset
* typos(typos): typos
* chore(format): resolving prettier issues
* fix(h264_nvenc): fixing crf handling for h264_nvenc
* docs(clean): removing too technical parts of the docs
* fix(imports): fixing imports at the __init__ level
* fix(imports): fixing not very pretty imports in video config file
* fix(config): add lora_alpha to PeftConfig
PeftConfig was missing the lora_alpha field, causing the PEFT library
to default to alpha=8 regardless of the LoRA rank, which dampens the
adaptation signal for high-rank adapters (e.g., r=128).
This adds lora_alpha: int | None = None to PeftConfig, allowing users
to specify --peft.lora_alpha <value> on the CLI.
Closes#3551
* fix(docs): add lora_alpha to peft training example + clarify scaling formula
- Add --peft.lora_alpha=64 to docs/source/peft_training.mdx example to
prevent new users from hitting the alpha=8 default dampening bug
- Clarify lora_alpha comment in default.py with scaling = lora_alpha / r
* docs: mention both --peft.r and --peft.lora_alpha in LoRA description
---------
Co-authored-by: Cheng Yin <yin@users.noreply.github.com>
* refactor: RL stack refactoring — RLAlgorithm, RLTrainer, DataMixer, and SAC restructuring
* chore: clarify torch.compile disabled note in SACAlgorithm
* fix(teleop): keyboard EE teleop not registering special keys and losing intervention state
Fixes#2345
Co-authored-by: jpizarrom <jpizarrom@gmail.com>
* fix: remove leftover normalization calls from reward classifier predict_reward
Fixes#2355
* fix: add thread synchronization to ReplayBuffer to prevent race condition between add() and sample()
* refactor: update SACAlgorithm to pass action_dim to _init_critics and fix encoder reference
* perf: remove redundant CPU→GPU→CPU transition move in learner
* Fix: add kwargs in reward classifier __init__()
* fix: include IS_INTERVENTION in complementary_info sent to learner for offline replay buffer
* fix: add try/finally to control_loop to ensure image writer cleanup on exit
* fix: use string key for IS_INTERVENTION in complementary_info to avoid torch.load serialization error
* fix: skip tests that require grpc if not available
* fix(tests): ensure tensor stats comparison accounts for reshaping in normalization tests
* fix(tests): skip tests that require grpc if not available
* refactor(rl): expose public API in rl/__init__ and use relative imports in sub-packages
* fix(config): update vision encoder model name to lerobot/resnet10
* fix(sac): clarify torch.compile status
* refactor(rl): update shutdown_event type hints from 'any' to 'Any' for consistency and clarity
* refactor(sac): simplify optimizer return structure
* perf(rl): use async iterators in OnlineOfflineMixer.get_iterator
* refactor(sac): decouple algorithm hyperparameters from policy config
* update losses names in tests
* fix docstring
* remove unused type alias
* fix test for flat dict structure
* refactor(policies): rename policies/sac → policies/gaussian_actor
* refactor(rl/sac): consolidate hyperparameter ownership and clean up discrete critic
* perf(observation_processor): add CUDA support for image processing
* fix(rl): correctly wire HIL-SERL gripper penalty through processor pipeline
(cherry picked from commit 9c2af818ff)
* fix(rl): add time limit processor to environment pipeline
(cherry picked from commit cd105f65cb)
* fix(rl): clarify discrete gripper action mapping in GripperVelocityToJoint for SO100
(cherry picked from commit 494f469a2b)
* fix(rl): update neutral gripper action
(cherry picked from commit 9c9064e5be)
* fix(rl): merge environment and action-processor info in transition processing
(cherry picked from commit 30e1886b64)
* fix(rl): mirror gym_manipulator in actor
(cherry picked from commit d2a046dfc5)
* fix(rl): postprocess action in actor
(cherry picked from commit c2556439e5)
* fix(rl): improve action processing for discrete and continuous actions
(cherry picked from commit f887ab3f6a)
* fix(rl): enhance intervention handling in actor and learner
(cherry picked from commit ef8bfffbd7)
* Revert "perf(observation_processor): add CUDA support for image processing"
This reverts commit 38b88c414c.
* refactor(rl): make algorithm a nested config so all SAC hyperparameters are JSON-addressable
* refactor(rl): add make_algorithm_config function for RLAlgorithmConfig instantiation
* refactor(rl): add type property to RLAlgorithmConfig for better clarity
* refactor(rl): make RLAlgorithmConfig an abstract base class for better extensibility
* refactor(tests): remove grpc import checks from test files for cleaner code
* fix(tests): gate RL tests on the `datasets` extra
* refactor: simplify docstrings for clarity and conciseness across multiple files
* fix(rl): update gripper position key and handle action absence during reset
* fix(rl): record pre-step observation so (obs, action, next.reward) align in gym_manipulator dataset
* refactor: clean up import statements
* chore: address reviewer comments
* chore: improve visual stats reshaping logic and update docstring for clarity
* refactor: enforce mandatory config_class and name attributes in RLAlgorithm
* refactor: implement NotImplementedError for abstract methods in RLAlgorithm and DataMixer
* refactor: replace build_algorithm with make_algorithm for SACAlgorithmConfig and update related tests
* refactor: add require_package calls for grpcio and gym-hil in relevant modules
* refactor(rl): move grpcio guards to runtime entry points
* feat(rl): consolidate HIL-SERL checkpoint into HF-style components
Make `RLAlgorithmConfig` and `RLAlgorithm` `HubMixin`s, add abstract
`state_dict()` / `load_state_dict()` for critic ensemble, target nets
and `log_alpha`, and persist them as a sibling `algorithm/` component
next to `pretrained_model/`. Replace the pickled `training_state.pt`
with an enriched `training_step.json` carrying `step` and
`interaction_step`, so resume restores actor + critics + target nets +
temperature + optimizers + RNG + counters from HF-standard files.
* refactor(rl): move actor weight-sync wire format from policy to algorithm
* refactor(rl): update type hints for learner and actor functions
* refactor(rl): hoist grpcio guard to module top in actor/learner
* chore(rl): manage import pattern in actor (#3564)
* chore(rl): manage import pattern in actor
* chore(rl): optional grpc imports in learner; quote grpc ServicerContext types
---------
Co-authored-by: Khalil Meftah <khalil.meftah@huggingface.co>
* update uv.lock
* chore(doc): update doc
---------
Co-authored-by: jpizarrom <jpizarrom@gmail.com>
Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>
* chore(deps): ceiling + cuda
* ci: bump cuda version docker image
* ci: add cpu wheel to release workflow
* chore(deps): update uv.lock
* docs: update installation with cuda note
Resolve conflicts and pull in the latest PR 1 fixes.
Conflicts:
- pyproject.toml: PR 1 added `lerobot-rollout` and PR 2 added
`lerobot-annotate` to the same `[project.scripts]` block. Kept both.
- uv.lock: dropped both sides and regenerated against the merged
`pyproject.toml` (PR 2 dropped the `datatrove` dep when distribution
moved to HF Jobs; PR 1's lock didn't have it).
Test follow-up:
- `tests/annotations/test_pipeline_recipe_render.py` — PR 1 deleted
`src/lerobot/configs/recipes/pi05_hirobot.yaml` (review feedback:
remove the canonical-recipe file; recipes are user-supplied). The
cross-PR contract this test guards is "the recipe DSL renders
non-empty messages from pipeline output", which doesn't depend on
any specific YAML, so the test now builds an inline blend recipe
with the same coverage. Passes.
Sweep: 82 passed, 2 failed (pre-existing module-impl bugs:
`test_module1_attaches_video_block_to_subtask_prompt`,
`test_module2_mid_episode_emits_paired_interjection_and_speech`).
The PR 1 carryover (`test_emitted_at_raises_on_ambiguous_per_camera_vqa`)
is now passing — the merge brought in PR 1's tightened `_select_one`
ambiguity check.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The executor previously claimed it would "optionally hand off" to
datatrove's LocalPipelineExecutor or SlurmPipelineExecutor — but it
already runs phases inline in every code path, and HF Jobs (see
``examples/annotation/run_hf_job.py``) is the actual distribution
strategy. Stop pretending we have an executor selector.
* `executor.py`: drop `select_executor_class`, the "kind" log line, and
the references to LocalPipelineExecutor / SlurmPipelineExecutor.
Module docstring now says distribution is delegated to HF Jobs.
* `config.py`: drop `auto_threshold`, `force_local`, `slurm_partition`,
`slurm_gpus`, `slurm_time`, `workers`. `ExecutorConfig` keeps only
`episode_parallelism`. While here, prune the longer "why" docstrings
on every field down to the load-bearing bits — full story moves to
`docs/source/annotation_pipeline.mdx`.
* `pyproject.toml`: drop `datatrove>=0.4.0,<2.0.0` from the
`[annotations]` extra; the dep was only there for the (never used)
cluster executors. Comment block notes the new HF-Jobs delegation.
* `reader.py`, `lerobot_annotate.py`: drop their own datatrove /
flavor-namespace mentions.
* `docs/source/annotation_pipeline.mdx`:
- remove the flavor-namespace / sidecar paragraph (out of scope —
"multiple revisions = multiple copies" is dataset-level policy);
- remove the "writer drops the legacy `subtask_index` column" note
(already covered by PR 1's intentional-break call-out);
- remove the chat-template + `apply_chat_template(messages, tools=...)`
line (covered by Tools doc);
- replace the "executor picks Local vs Slurm" paragraph with
`--executor.episode_parallelism` and a pointer to HF Jobs;
- rewrite the style→recipe section to talk about "recipes" generically
instead of pinning a specific YAML;
- add a "Running on Hugging Face Jobs" section pointing at
`examples/annotation/run_hf_job.py`;
- add a "Running locally" example matching the CLI's docstring
(`uv run lerobot-annotate --root=... --vlm.model_id=...`);
- extend the paper-inspirations list with Pi0.7 and Steerable VLA
Policies (Zhao 2025) for Module 3.
Tests: same 3 pre-existing failures as before this commit (2 module
assertions still in flight; 1 carryover from PR 1). 41/44 pass.
Pre-commit clean.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Reword the two callouts in `tools.mdx` to describe the runtime layer
in present tense ("not part of the catalog layer shipped today",
"those modules don't yet exist in the tree") instead of pointing at a
specific follow-up PR. Keeps the doc honest about what works now
without coupling it to a particular release order.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* **#2 — dedupe `_PLACEHOLDER_RE`.** The same regex was compiled in
`recipe.py` and `language_render.py`. Promote to module-level
`PLACEHOLDER_RE` in `recipe.py` (its primary owner — declares
template syntax) and import from `language_render.py`.
* **#3 — centralize language column names.** `io_utils.py` had
hardcoded `{"language_persistent", "language_events"}` literals at
two sites. Replace with `LANGUAGE_COLUMNS` import so a future column
rename can't silently desync.
* **#4 — defensive collate preserved-keys.** `lerobot_collate_fn`
silently filtered language fields from samples that didn't have
them, which would hand downstream consumers a preserved list
shorter than the tensor batch. Now: if any sample carries a key,
every sample in the batch must carry it; otherwise raise a
`ValueError` so the upstream rendering bug surfaces at the boundary.
* **#5 — `_scalar` rejects non-singleton lists.** Previously a zero-
or multi-element list fell through and triggered confusing
`float([])` errors downstream. Now raises `ValueError` with the
actual length.
* **#6 — refactor `_extract_complementary_data`.** Replace 11 lines
of `key = {... if ... else {}}` plus an 11-line splat dict with a
single `_COMPLEMENTARY_KEYS` tuple iterated once.
* **#7 — document `EXTENDED_STYLES`.** Was an empty `set()` with no
comment. Add a docstring explaining it's an intentional extension
point: downstream modules append project-local styles before
`column_for_style` is called.
* **#9 — `tools.mdx` notes the runtime layer is future work.** The
page referenced `src/lerobot/tools/`, `registry.py`, and
`get_tools(meta)` — none exist in this PR. Added a callout at the
start of "How to add your own tool" plus a note on the
implementations paragraph.
* **#10 — tests for YAML round-trip, malformed rows, blend
validation.** `test_recipe.py` grew from 1 case to 12 covering:
blend-or-messages exclusivity, target-turn requirement, blend
emptiness, weight presence/positivity, nested-blend rejection,
`from_dict` with nested blends, `from_yaml` / `load_recipe`
agreement, top-level non-mapping rejection. Added a malformed-row
test for `_normalize_rows` that asserts non-dict entries raise
`TypeError`.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* feat(policies): add EO-1 model
* chore(eo1): adjust policy_eo1_README.md to to avoid duplicate with eo1.mdx
* chore(eo1): remove policy_eo1_README.md, link eo1.mdx in policy folder
---------
Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com>
* **`meta.tools` actually reads `info.json["tools"]`.** `DatasetInfo`
had no `tools` field, so `from_dict` silently dropped the key (it
warned about unknown fields then discarded them) and the property
always returned `DEFAULT_TOOLS`. Added `tools: list[dict] | None`
to the dataclass; `to_dict()` drops it when unset so existing
datasets keep a clean `info.json`. Fixed the accessor to read
`self.info.tools` (the previous `.get(...)` would have raised
AttributeError on the dataclass anyway). Added regression tests:
fallback when absent, round-trip from disk, and round-trip
through `DatasetInfo.from_dict` / `to_dict`.
* **`motion` is not view-dependent — fix the docs.** The mdx claimed
rows of style `motion` must carry `camera`, but `VIEW_DEPENDENT_STYLES
= {"vqa", "trace"}` and the validator agrees: motion primitives are
joint/Cartesian-frame, not pixel-space. Updated both call-out
paragraphs in `language_and_recipes.mdx`.
* **Conditional `collate_fn` swap.** Added `meta.has_language_columns`
and gate the `lerobot_collate_fn` swap in `lerobot_train.py` on it,
so non-language datasets keep PyTorch's `default_collate`. Also
added a pass-through test in `test_collate.py` that asserts on a
plain tensor batch the custom collate matches `default_collate`
key-for-key, plus a test for the `None`-sample drop path.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
After every ``lerobot-annotate`` run, the executor ensures
``meta/info.json["tools"]`` contains at minimum the canonical ``say``
schema, while preserving any tools the user pre-declared on the
dataset. Chat-template consumers (PR 3 SmolVLA2 / Pi0.5 / dataset
visualizer) read the catalog through
``LeRobotDatasetMetadata.tools`` and pass it to
``apply_chat_template(messages, tools=meta.tools, ...)``.
- ``executor.py``: new ``_ensure_tools_in_info`` helper called
after the parquet rewrite. Idempotent and additive — merges by
``function.name``, only writes back if the list changed.
- ``writer.py``: drops the duplicated ``SAY_TOOL_SCHEMA`` /
``DEFAULT_TOOLS`` constants in favour of importing from
``lerobot.datasets.language`` (PR 1's single source of truth).
Re-exported so existing imports keep working.
- ``annotation_pipeline.mdx``: replace the "code constant only" note
with a pointer to the new Tools doc and a description of the
meta/info.json behaviour, including how to pre-declare custom
tools before annotation runs.
This is the storage half of the tools work; PR 3 ships the runnable
implementations under ``src/lerobot/tools/`` (one file per tool,
first up: ``say.py`` wired to Kyutai's pocket-tts).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
PR 2 used to write a top-level ``tools`` column on every parquet shard
holding the JSON schema for the ``say`` tool, broadcast identically
across every row. That extends PR 1's schema for no real information
gain — the schema is a fixed code constant, parquet's RLE/dict encoding
collapses it on disk anyway, and HF/TRL chat-template consumers can
just import the constant directly.
PR 2 should fill in PR 1's existing schema, not add to it. So:
- ``writer.py``: stop emitting the ``tools`` column. Strip any legacy
``tools`` column from older shards on rerun so the schema converges to
v3.1. ``SAY_TOOL_SCHEMA`` stays as a public constant (now joined by
``DEFAULT_TOOLS = [SAY_TOOL_SCHEMA]``); chat-template policies and the
visualizer import them directly.
- ``test_writer.py``: replace the "tools column present" assertion with
one that explicitly checks the column is absent, plus a new test
asserting the constant's shape.
- ``test_pipeline_recipe_render.py``: drop the tools-column read; assert
it's not present in the rewritten parquet.
- ``annotation_pipeline.mdx``: update the writer description to note the
parquet stays small and the schema lives as a code constant.
If multi-tool-set support ever becomes real (datasets with different
tool inventories), the right home is ``meta/info.json["tools"]`` —
adding it later is non-breaking; ripping out a parquet column already
shipped is not.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Replaces keyframe sampling with a single Qwen-VL video block covering
the whole demonstration. The model pools temporally itself and chooses
where to cut subtasks — no stride, no count, no keyframe count knob to
tune.
- frames.py: ``FrameProvider`` gains ``video_for_episode(record,
max_frames)``; ``VideoFrameProvider`` samples up to ``max_frames``
uniformly across the episode duration; ``_NullProvider`` returns []
for the no-video fallback. New ``to_video_block`` helper.
- Module 1: drops keyframe sampling. The subtask prompt now goes out as
``[{"type":"video", "video":[<frames>]}, {"type":"text", ...}]`` and
the prompt template asks the model to "watch the whole clip, then
segment it" with cut points decided from gripper/contact/regrasp
events the model sees.
- Module1Config: ``keyframes_per_episode`` removed; replaced with
``max_video_frames: int = 32`` (model-capacity bound, not annotation
logic).
- Test: ``test_module1_attaches_video_block_to_subtask_prompt`` locks in
the single-video-block invariant.
- Stub-VLM markers updated: tests now key on "atomic subtasks" instead
of the old "Decompose the demonstration" phrase that no longer
appears in the prompt.
- Docs: updated to describe the whole-episode video-block behavior and
the no-video fallback.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Closes the visual-grounding gap flagged after the initial PR review:
modules now decode actual camera frames at the relevant timestamps and
attach them as `{"type":"image", "image":<PIL>}` content blocks to the
VLM prompts.
- New `frames.py`:
- `FrameProvider` Protocol; `VideoFrameProvider` decodes from the
dataset's first `observation.images.*` stream via
`LeRobotDatasetMetadata.get_video_file_path` and
`decode_video_frames`, with the same `from_timestamp` shift the main
dataset uses.
- Per-process LRU cache so co-timestamped Module 1 plan-update + Module
2 calls share decode work.
- `make_frame_provider` falls back to a null provider when the dataset
has no video tracks → text-only prompts (graceful absence).
- Modules 1/2/3 take an optional `frame_provider` (default null) and
prepend image blocks before the text block.
- Module 1 attaches `keyframes_per_episode` keyframes to the subtask
decomposition prompt.
- Module 2 attaches the frame at the interjection timestamp.
- Module 3 attaches the exact emission frame to each VQA pair.
- VlmConfig: backend now defaults to `vllm`; default model is
`Qwen/Qwen3.6-27B-FP8`. New knobs: `--vlm.tensor_parallel_size`,
`--vlm.camera_key` (override the keyframe stream).
- `_make_vllm_client` honours `tensor_parallel_size` so 27B-FP8 sharded
on 2× GPUs works out of the box.
- `test_module3_attaches_frame_image_block_to_prompt` asserts modules
emit one image block per VQA prompt at the exact emission timestamp.
- Docs: example switched to `imstevenpmwork/super_poulain_draft` +
Qwen3.6-27B-FP8 + tensor_parallel_size=2; documents the keyframe
attachment behaviour and the no-video fallback.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Stores OpenAI-style function schemas at ``meta/info.json["tools"]`` so
datasets can declare which tools are available (today: just ``say``;
tomorrow: per-dataset extensions). The ``DEFAULT_TOOLS`` constant
fills in for unannotated datasets so chat-template consumers don't
have to special-case anything.
Three pieces:
- ``language.py``: ``SAY_TOOL_SCHEMA`` and ``DEFAULT_TOOLS``
constants. Single source of truth — PR 2's writer and PR 3's
runtime tool registry will both import from here instead of
duplicating the dict.
- ``dataset_metadata.py``: ``LeRobotDatasetMetadata.tools`` property
reads ``info.json["tools"]`` and falls back to ``DEFAULT_TOOLS``.
Returns deep-copied dicts so callers can mutate the result safely.
- ``docs/source/tools.mdx``: spec page covering the catalog, per-row
invocations, and the three-step "how to add a new tool" workflow
(declare schema, implement, register). Linked from the docs
toctree under the Datasets section.
This lays the groundwork for PR 2's pipeline writing the catalog out
during annotation, and PR 3's ``src/lerobot/tools/`` package shipping
runnable implementations (one file per tool — first up:
``say.py`` wrapping Kyutai's pocket-tts).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Adds a nullable `camera` field to the language row struct (both persistent
and event variants) so view-dependent styles like `vqa` can carry which
`observation.images.*` view they were grounded against. Without this,
multi-camera datasets ended up with multiple `(vqa, role)` rows at the
same timestamp that the resolver could not disambiguate.
- `language.py`: add `camera` to PERSISTENT_ROW_FIELDS / EVENT_ROW_FIELDS,
to both Arrow struct types and the HF datasets feature mappings;
introduce VIEW_DEPENDENT_STYLES = {vqa, motion, trace} plus
`is_view_dependent_style` and `validate_camera_field` helpers (camera
required iff style is view-dependent).
- `language_render.py`: thread an optional `camera=` kwarg through every
resolver (`active_at`, `emitted_at`, `nth_prev`, `nth_next`) and through
`_matching_rows` / `_select_*`, so recipes can disambiguate per-camera
VQA with `emitted_at(t, style=vqa, role=assistant, camera=...)`.
Without a `camera` filter, multi-row matches keep raising the existing
ambiguity error — which is the desired behaviour on multi-camera data.
- `recipes/pi05_hirobot.yaml`: replace the single `ask_vqa` branch with
`ask_vqa_top` and `ask_vqa_wrist` per-camera sub-recipes (each carrying
the matching image block), keeping the original 0.20 budget and
documenting the customization point for datasets with different cameras.
- Tests: schema test asserts the new field order; new tests cover
`is_view_dependent_style`, `validate_camera_field` (both required and
forbidden directions), per-camera `emitted_at` filtering, and the
ambiguity error when two cameras emit `(vqa, assistant)` at the same
timestamp without a `camera=` filter. RenderMessagesStep + dataset
passthrough fixtures updated to include the new field.
- `docs/source/language_and_recipes.mdx`: document the `camera` field,
the per-camera resolver pattern, and the canonical recipe convention.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* feat(rewards): add RewardModelConfig and PreTrainedRewardModel base classes
* refactor(rewards): migrate Classifier from policies/sac/reward_model/ to rewards/classifier/
* refactor(rewards): migrate SARM from policies/sarm/ to rewards/sarm/
* refactor(rewards): add rewards/factory.py and remove reward model code from policies/factory.py
* refactor(rewards): update imports and delete old reward model locations
* test(rewards): add reward model tests and update existing test imports
* fix(rewards): restore full Classifier and SARM implementations
* test(rewards): restore missing CUDA and mixed precision classifier processor tests
* refactor(lerobot_train.py): remove rabc specific configuration and replace it with a generic samplerweight class in lerobot_train
* refactor(lerobot_train.py): add missing sampling weight script
* linter + missing files
* add testing for sampl weighter
* revert some useless changes, improve typing
* update docs
* add automatic detection of the progress path
* remove type exp
* improve comment
* fix: move rabc.py to rewards/sarm/ and update import paths
* refactor(imports): update reward model imports to new module structure
* refactor(imports): update reward model imports to reflect new module structure
* refactor(imports): conditionally import pandas based on availability
* feat(configs): add reward_model field to TrainPipelineConfig and Hub fields to RewardModelConfig
* refactor(policies): remove reward model branches from policy factory and __init__
* refactor(rewards): expand __init__ facade and fix SARMConfig __post_init__ crash
* feat(train): route reward model training through rewards/factory instead of policies/factory
* refactor(train): streamline reward model training logic
* fix(rewards): ensure FileNotFoundError is raised for missing config_file
* refactor(train): update __get_path_fields__ to include reward_model for config loading
* refactor(classifier): remove redundant input normalization in predict_reward method
* fix(train): raise ValueError for non-trainable reward models in train function
* refactor(pretrained_rm): add model card template
* refactor(tests): reward models
* refactor(sarm): update reset method and remove unused action prediction methods
* refactor(wandb): differentiate tags for reward model and policy training in cfg_to_group function
* fix(train): raise ValueError for PEFT usage in reward model training
* refactor(rewards): enhance RewardModelConfig with device handling and delta indices properties
---------
Co-authored-by: Michel Aractingi <michel.aractingi@huggingface.co>
feat(sim): add VLABench benchmark integration
Add VLABench as a new simulation benchmark in LeRobot, following the existing LIBERO and MetaWorld patterns.
This PR wires VLABench end-to-end across environment integration, Docker setup, CI smoke evaluation, and documentation. It also fixes a number of upstream packaging and runtime issues required to make VLABench usable and reproducible in CI.
What’s included
Benchmark integration
Add VLABench as a new simulation benchmark.
Expose supported VLABench tasks through the LeRobot env interface.
Follow the established LIBERO / MetaWorld factory patterns.
Preserve lazy async-env metadata so env.unwrapped.metadata["render_fps"] continues to work.
CI smoke evaluation
Add a VLABench smoke-eval job using lerobot/smolvla_vlabench.
Use the correct rename_map for the 3-camera dataset layout.
Expand smoke coverage from 1 to 10 primitive tasks.
Extract task descriptions after eval so metrics artifacts include per-task labels.
Skip Docker Hub login when secrets are unavailable (e.g. fork PRs).
Docker / install fixes
Install VLABench from GitHub rather than PyPI.
Use uv pip, not pip, in the base image.
Fail loudly on install errors instead of masking them.
Clone VLABench into the non-root user’s home directory.
Use shallow editable installs for VLABench and rrt-algorithms to work around missing __init__.py issues.
Pin upstream clones to exact commit SHAs for reproducibility.
Add undeclared runtime dependencies required by VLABench (open3d, colorlog, scikit-learn, openai).
Unpin open3d so Python 3.12 wheels resolve.
Assets
Support downloading VLABench assets from a Hugging Face Hub mirror via VLABENCH_ASSETS_REPO.
Keep Google Drive download support as fallback.
Install huggingface_hub[hf_xet] so Xet-backed assets download correctly.
Validate required mesh/XML asset subtrees at build time.
Patch VLABench constants to tolerate missing asset directories at import time.
Runtime / env correctness
Import VLABench robots and tasks explicitly so decorator-based registry population happens.
Resize and normalize camera observations so they always match the declared (H, W, 3) uint8 observation space.
Reinstall LeRobot editably inside the image so the new env code is actually used.
Coerce agent_pos / ee_state to the expected shape.
Pad actions when needed to match data.ctrl.
Replace zero-padding fallback with proper dm_control IK for 7D end-effector actions.
Refetch dm_control physics on each step instead of caching weakrefs.
Retry unstable resets with reseeding and handle PhysicsError gracefully at step time.
Dataset / policy alignment
Align VLABench observations and actions with Hugging Face dataset conventions used by lerobot/vlabench_unified:
convert EE position between world frame and robot-base frame at the env boundary,
expose / consume Euler XYZ instead of raw quaternion layout,
align gripper semantics with dataset convention (1 = open, 0 = closed).
This fixes policy/env mismatches that previously caused incorrect IK targets and unstable behavior at evaluation time.
Docs
Add a full docs/source/vlabench.mdx page aligned with the standard benchmark template.
Document task selection forms (single task, comma list, suite shortcut).
Document installation, evaluation, training, and result reproduction.
Point examples at lerobot/smolvla_vlabench.
Add a benchmark banner image.
Remove outdated / misleading references to upstream evaluation tracks.
Document manual install flow instead of a broken vlabench extra.
Packaging cleanup
Remove the unresolvable vlabench extra from pyproject.toml.
Remove the no-op VLABench processor step.
Remove the obsolete env unit test that only covered the dropped gripper remap helper.
Apply formatting / logging / style cleanup from review feedback.
Why this is needed
VLABench is not currently consumable as a normal Python dependency and requires several upstream workarounds:
no PyPI release,
missing package declarations,
undeclared runtime deps,
SSH-only submodule references,
asset downloads outside normal package install flow,
registry population that depends on import side effects,
env outputs that do not always match declared observation shapes,
task resets that can diverge under some random layouts.
This PR makes the benchmark usable in LeRobot despite those constraints, and ensures CI runs are reproducible and informative.
If you want a much shorter squash commit message, I’d use this:
feat(sim): integrate VLABench benchmark with CI, Docker, and docs
Add VLABench as a new LeRobot simulation benchmark, following the existing LIBERO / MetaWorld patterns.
This includes:
LeRobot env integration and task exposure,
CI smoke eval with lerobot/smolvla_vlabench,
Docker install and asset-download fixes,
runtime fixes for registry loading, assets, camera obs, action handling, dm_control IK, and PhysicsError recovery,
alignment of obs/action semantics with HF VLABench datasets,
docs and packaging cleanup.
The PR also incorporates review feedback, improves reproducibility by pinning upstream commits, and makes VLABench usable in CI despite upstream packaging and asset-management issues.
* feat(envs): add LIBERO-plus robustness benchmark integration
- LiberoPlusEnv config (subclass of LiberoEnv, same gym interface)
- Docker image installing LIBERO-plus fork via PYTHONPATH
- CI workflow: 1-episode smoke eval with pepijn223/smolvla_libero_plus
- pyproject.toml: libero_plus extra
* fix(libero): use suite's perturbation-aware init_states loader
LIBERO-plus's Benchmark class exposes a `get_task_init_states(i)` method that
strips perturbation suffixes (`_table_N`, `_tb_N`, `_view_`, `_language_`,
`_light_`, `_add_`, `_level`) and loads the underlying base `.pruned_init`
file — the on-disk name for a perturbation variant doesn't exist as a file,
only the base does. lerobot's loader was bypassing that logic and trying to
read the suffix-bearing filename directly, which failed for every non-zero
task id and killed the eval before any rollout video could be written.
Delegate to the suite's method when it exists; fall back to the path-based
loader for vanilla LIBERO (which does not provide the method).
Also drop the hf-libero install + init_files copy from the LIBERO-plus
Dockerfile — the LIBERO-plus clone already ships both `bddl_files/` and
`init_files/` for all five suites, so the copy was unnecessary and the
`cp -r` into an existing dir produced a confusing nested layout.
* fix(libero): resolve LIBERO-plus perturbation init_states path ourselves
Delegating to `task_suite.get_task_init_states(i)` works for path resolution
but LIBERO-plus's method calls `torch.load(path)` without `weights_only=False`,
which fails on PyTorch 2.6+ because the pickled init_states contains numpy
objects not in the default allowlist:
_pickle.UnpicklingError: Weights only load failed.
WeightsUnpickler error: Unsupported global:
GLOBAL numpy.core.multiarray._reconstruct was not an allowed global.
Mirror LIBERO-plus's suffix-stripping logic (`_table_N`, `_tb_N`, `_view_`,
`_language_`, `_light_`, `_add_`, `_level`) in our own helper so we can pass
`weights_only=False` ourselves. Vanilla LIBERO task names don't contain any
of these patterns except for `_table_` when followed by the word `center`
(e.g. `pick_up_the_black_bowl_from_table_center_...`), and the regex
requires `_table_\\d+` so semantic uses are preserved.
* fix(libero-plus): download perturbation assets from Sylvest/LIBERO-plus
LIBERO-plus's bddl_base_domain.py resolves scene XMLs with
`os.path.join(DIR_PATH, "../assets")`, so the `assets` key in config.yaml
has no effect on scene lookup — MuJoCo always opens
`<clone>/libero/libero/assets/scenes/...`. With no such directory present,
every perturbation task fails on:
FileNotFoundError: No such file or directory:
.../libero-plus/libero/libero/assets/scenes/tabletop_table_Cobblestone01_GLOSS_6K.xml
These textures, views, and extra objects ship only in the 6.4 GB `assets.zip`
published at `Sylvest/LIBERO-plus` (the LIBERO-plus README explicitly says
to download and unzip it into the package dir). Fetch it via `hf_hub_download`,
unzip into `${LIBERO_PLUS_ROOT}/`, install `unzip`, and point config.yaml at
the extracted dir so everything stays consistent. The download lives in its
own Docker layer so subsequent rebuilds reuse the cached assets.
Drops the lerobot/libero-assets snapshot_download — that mirror only has
vanilla LIBERO textures and is ignored for scene loading anyway.
* fix(libero-plus): flatten deep path prefix from Sylvest/LIBERO-plus assets.zip
The 6.4 GB zip ships with every entry prefixed by
`inspire/hdd/project/embodied-multimodality/public/syfei/libero_new/release/dataset/LIBERO-plus-0/assets/...`
(the author's internal filesystem layout, not the layout the LIBERO-plus
README promises), so the previous `unzip -d ${LIBERO_PLUS_ROOT}/` created
`${LIBERO_PLUS_ROOT}/inspire/.../assets/` — robosuite still opened
`${LIBERO_PLUS_ROOT}/assets/scenes/tabletop_table_Cobblestone01_GLOSS_6K.xml`
and hit the same FileNotFoundError.
Extract to a scratch dir, then `mv` the nested `assets/` subtree to the
expected location. Verified the target file exists in the zip central
directory under that exact prefix.
* refactor(libero): inline init_states resolver behind single regex
Collapse the three-style suffix stripper (split/re.sub/in) into one
compiled regex, drop the (Path, bool) tuple return, and move the
`_add_`/`_level` reshape branch into the caller so each branch loads
its own file and returns directly. Net: -11 lines, one fewer helper.
* refactor(libero-plus): rebase docker image on huggingface/lerobot-gpu
Mirror the libero/metaworld/robomme pattern: start from the nightly GPU
image (apt deps, python, uv, venv, lerobot[all] already there) and only
layer on what LIBERO-plus uniquely needs — its wand/ImageMagick build
deps, the non-extra runtime pips (robosuite==1.4.1, bddl, …), the
PYTHONPATH-shadowed fork, and the 6.4 GB assets.zip.
Drops ~50 lines of duplicated base setup (CUDA FROM, apt python, uv
install, user creation, venv init) the nightly already provides.
123 → 73 lines.
Also:
- Add libero_plus to docs/source/_toctree.yml under Benchmarks so
doc-builder's TOC integrity check stops failing.
- Repoint the docs dataset link from pepijn223/libero_plus_lerobot to
the canonical lerobot/libero_plus.
- Revert the stray uv.lock churn (revision/marker diff that crept in
from an unrelated resolve — unrelated to LIBERO-plus).
* fix(libero-plus): stop touching pyproject + uv.lock
The fast-tests job was rejecting the branch because pyproject.toml had a
[libero_plus] extra whose git dep wasn't represented in uv.lock.
The Docker image no longer needs the extra — it clones LIBERO-plus
directly and PYTHONPATH-shadows hf-libero. Drop [libero_plus] from
pyproject and restore pyproject.toml + uv.lock to exactly what's on
origin/main, so `uv sync --locked --extra test` is a no-op for this PR.
Also repoint the doc/CI/env comments that still mentioned the extra at
the Docker install path.
* fix(libero-plus): strip perturbation metadata from task descriptions
LIBERO-plus builds task.language by space-joining the perturbation-variant
filename, so every non-_language_ variant inherits a trailing blob like
"view 0 0 100 0 0 initstate 0 noise 45" or "add 16". That shows up in the
dashboard video labels and no longer matches the base instruction stored
in the training dataset.
Strip those tokens in extract_task_descriptions.py with an end-anchored
regex over the {view,initstate,noise,add,tb,table,light,level}(+digits)
vocabulary. The anchor preserves mid-sentence literal uses of those words
(e.g. "from table center and place it on the plate") — only the trailing
metadata chain is removed. _language_ variants carry real BDDL-sourced
text and are left untouched.
* ci: point benchmark eval checkpoints at the lerobot/ org mirrors
pepijn223/smolvla_* → lerobot/smolvla_* across every benchmark job in
this branch (libero, metaworld, and the per-branch benchmark). The
checkpoints were mirrored into the lerobot/ org and that's the canonical
location going forward.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* fix: integrate PR #3313 review feedback
- docs: fix paper link to arxiv, add benchmark image, add suite descriptions,
add LIBERO-plus replacement warning, restructure eval section to match
LIBERO doc style, fix policy I/O section, remove false try/except claim
- docker: fix shell grouping for hf-libero uninstall, replace hardcoded
asset path with dynamic find
- ci: add Docker Hub login step, add HF_USER_TOKEN guard on eval step
- envs: add is_libero_plus param to get_task_init_states so vanilla LIBERO
always takes the simple path
* fix(docs): use correct LIBERO-plus teaser image URL
* ci(libero-plus): drop redundant hf auth login step
The standalone login step ran `hf auth login` in a throwaway
`docker run --rm` container, so no credentials persisted. Auth is
already performed inside the eval step's container. Removing the
redundant step per PR #3313 review feedback.
* fix(envs): preserve AsyncVectorEnv metadata/unwrapped in lazy eval envs
Port of #3416 onto this branch. Without these attributes eval crashes
when calling `env.unwrapped.metadata["render_fps"]` with async vector
envs. Adds `metadata` / `unwrapped` to `_LazyAsyncVectorEnv` and
caches the metadata alongside obs/action spaces in the LIBERO and
MetaWorld factories.
* ci: gate Docker Hub login on secret availability
Fork PRs cannot access `secrets.DOCKERHUB_LEROBOT_{USERNAME,PASSWORD}`,
which made every benchmark job fail at the login step before any of
the actual build/eval work could run. Gate the login on the env-var
expansion of the username so the step is skipped (not failed) when
secrets are absent. Mirrors the existing pattern in the VLABench job.
* Update .github/workflows/benchmark_tests.yml
Co-authored-by: Khalil Meftah <khalil.meftah@huggingface.co>
Signed-off-by: Pepijn <138571049+pkooij@users.noreply.github.com>
* Update scripts/ci/extract_task_descriptions.py
Co-authored-by: Khalil Meftah <khalil.meftah@huggingface.co>
Signed-off-by: Pepijn <138571049+pkooij@users.noreply.github.com>
* Update .github/workflows/benchmark_tests.yml
Co-authored-by: Khalil Meftah <khalil.meftah@huggingface.co>
Signed-off-by: Pepijn <138571049+pkooij@users.noreply.github.com>
* Update docker/Dockerfile.benchmark.libero_plus
Co-authored-by: Khalil Meftah <khalil.meftah@huggingface.co>
Signed-off-by: Pepijn <138571049+pkooij@users.noreply.github.com>
* Update .github/workflows/benchmark_tests.yml
Co-authored-by: Khalil Meftah <khalil.meftah@huggingface.co>
Signed-off-by: Pepijn <138571049+pkooij@users.noreply.github.com>
* fix(libero-plus): address review feedback
* ci(libero-plus): fix YAML indentation in upload-artifact steps
The `uses:` key on two upload-artifact steps was at column 0 instead
of nested under the step, causing `pre-commit run check-yaml` to fail
with "expected <block end>, but found '<block mapping start>'".
Signed-off-by: Pepijn <138571049+pkooij@users.noreply.github.com>
Co-authored-by: Khalil Meftah <khalil.meftah@huggingface.co>
