**#1 Plan-update phase reports correct skip count.**
``_run_plan_update_phase`` only ran ``run_plan_updates`` for episodes
with at least one interjection but hardcoded ``episodes_skipped=0``.
The summary undercounted skipped episodes. Now returns
``len(records) - processed`` so processed + skipped == total.
**#2 ``run_hf_job.py`` installs ``openai``.**
The ``CMD`` block does ``pip install --no-deps lerobot[branch]`` then
explicitly lists transitive deps. ``openai`` was missing — and since
``VlmConfig.backend`` defaults to ``"openai"``, the job would have
``ImportError``'d when ``vlm_client._make_openai_client`` ran.
**#3 Dedupe subtask-span reconstruction.**
Module 1's ``_reconstruct_subtasks_from_rows`` (no ``and spans`` guard)
and Module 2's ``_read_subtask_spans`` (with the guard) had near-
identical logic. Promoted to ``reconstruct_subtask_spans`` in
``reader.py`` using the safer guarded form. Both modules now import
the single helper.
**#5 Atomic staging.py JSONL writes.**
Mirroring the parquet-writer fix from an earlier review round:
``EpisodeStaging.write`` now writes to a sibling ``.tmp`` and
``Path.replace`` atomically. A crash mid-write can no longer leave a
half-written JSONL that ``read()`` would then fail to parse.
**#6 Atomic ``info.json`` write.**
Same pattern in ``executor._ensure_annotation_metadata_in_info`` —
``info.json`` is load-bearing for dataset metadata, so partial writes
brick the dataset.
**#7 Writer's role-key guard.**
``_normalize_persistent_row`` and ``_normalize_event_row`` accessed
``row["role"]`` directly while every other field used ``.get()``.
Pre-validate ``"role" in row`` and raise a friendly ``ValueError``
naming the row, so a future module that accidentally drops ``role``
fails with a triagable message instead of a bare KeyError deep in the
writer.
**#8 Last subtask span's ``end`` extends to episode end.**
``reconstruct_subtask_spans`` (the new shared helper) takes an optional
``episode_end_t``. When provided, the final span's ``end`` is closed
to that timestamp instead of equalling its own ``start`` (zero
duration). Both Module 1's plan-update pass and Module 2's interjection
anchoring pass ``record.frame_timestamps[-1]``, so downstream "current
subtask at refresh_t" lookups no longer miss refreshes that land
inside the final span.
Sweep: 66 passed, 0 failed. Pre-commit clean.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Both tests were stale relative to design changes that landed earlier on
this branch. Update the tests to match the current production contract.
**``test_module1_attaches_video_block_to_subtask_prompt``**
The test took ``captured[0]`` and asserted on its content blocks, but
Module 1 issues several sub-prompts and the rephrasings call (which is
text-only, no video block) usually lands first. Two fixes:
* The test's intent is "the subtask prompt carries the video block" —
not "the first prompt carries it". Pick the call by content
(``"atomic subtasks"`` keyword in the text block) so the test is
resilient to future reordering of unrelated sub-prompts.
* Set ``n_task_rephrasings=0`` so the rephrasings call is skipped
entirely — keeps the test focused on ``_generate_subtasks``.
**``test_module2_mid_episode_emits_paired_interjection_and_speech``**
Two issues both rooted in design changes on the branch:
1. ``InterjectionsAndSpeechModule._mid_episode_interjections`` now
anchors interjections on subtask boundaries from Module 1's staging
tree, bailing out with zero rows when no spans exist. The production
executor runs Module 1 first; the test ran Module 2 in isolation.
Reproduce the contract by seeding two ``style=subtask`` rows in the
staging before calling Module 2 — gives it the single ``0 → 1``
boundary it needs.
2. The test's stub responder used the marker ``"ONE realistic
interruption"`` to match the interjection prompt, but that string is
from a previous prompt version. The current
``module_2_interjection.txt`` says ``"Write ONE interjection..."`` —
the old prompt asked for counterfactual interjections (e.g. "skip the
wipe"), the new one anchors on the upcoming subtask. Marker updated
to ``"Write ONE interjection"``; canned response wording aligned to
the new design.
Sweep on the language stack: 66 passed, 0 failed (was 64 passed, 2
failed). Pre-commit clean.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
**Critical: video_for_episode was unreachable dead code.**
``video_for_episode`` was indented inside ``_decode_pyav_direct``, after
its ``return`` statement — Python parsed it as a nested function that
never executed. Module 1's ``_episode_video_block`` calls
``self.frame_provider.video_for_episode(record, target_count)`` on the
``use_video_url=False`` path, which would have AttributeError'd on any
real dataset. Tests passed only because they used ``_StubFrameProvider``
/ ``_NullProvider`` which have the method. Moved it to be a proper
method of ``VideoFrameProvider`` (right after ``frames_at``).
**Thread safety on VideoFrameProvider.**
The executor runs Module 1/2/3 phases under a ``ThreadPoolExecutor``, so
the per-instance ``_cache`` dict and the one-shot ``_warned_decode_fail``
flag were exposed to concurrent reads/writes. Added a ``threading.Lock``
field, wrapped cache reads/writes and the warn-flag check-and-set in
``with self._lock:``. Stub fixtures unaffected.
**episode_clip_path is now a method of VideoFrameProvider.**
Used to be a free function reaching into ``provider._meta.episodes`` and
``provider._meta.get_video_file_path`` from outside the class. As a
method it just uses ``self._meta``. The only caller (Module 1) updated;
no external callers.
**Atomic write in LanguageColumnsWriter.**
``pq.write_table(new_table, path)`` was overwriting the parquet shard
in place — a crash mid-write would corrupt the file. Now writes to a
sibling ``.tmp`` and ``Path.replace`` atomically.
**Smaller items:**
* ``executor.py`` docstring opened with "four phases" but listed six.
Now says "six phases" to match.
* ``[annotations]`` extra in ``pyproject.toml`` now includes
``openai>=1.40,<2.0``. Default ``VlmConfig.backend`` is ``"openai"``,
so without it ``_make_openai_client`` would ImportError on a fresh
``uv sync --extra annotations``.
* ``_snap_to_frame`` was duplicated identically in
``plan_subtasks_memory.py`` and ``interjections_and_speech.py``.
Promoted to ``snap_to_frame`` in ``reader.py`` (next to
``EpisodeRecord``); both modules now import it. Backwards-compat alias
not needed — no external callers.
* ``EpisodeRecord.frames_df()`` was re-reading the full parquet on every
call. Now memoizes via a private dataclass field so repeat calls from
different modules pay the cost once. Method signature unchanged.
* ``_extract_first_json_object`` had a redundant ``and not escape`` guard
that was dead because the prior block already handled and reset
``escape``. Replaced with a comment explaining the invariant.
**Pre-existing lint cleanups surfaced once these files entered
pre-commit's scope:**
* dead local ``client = clients[0]`` in ``_make_openai_client`` (the
real round-robin uses ``clients[rr_counter[...]]``).
* ``cmd = ... if "{port}" in cmd else f"...{port}"`` ternary collapse in
``_spawn_parallel_inference_servers``.
* ``seek_pts = 0 if stream.time_base is None else int(...)`` ternary
collapse in ``_decode_pyav_direct``.
* ``# nosec B310`` on the localhost ``urllib.request.urlopen`` probe in
``_server_is_up`` — the URL is the user-configured local-server endpoint
the CLI itself spawned, not arbitrary user input.
**Test added.**
``tests/annotations/test_frames.py`` pins the regression on
``VideoFrameProvider``: asserts ``video_for_episode`` and
``episode_clip_path`` are callable methods (not nested dead code or
free functions), and that the ``_lock`` field is a real
``threading.Lock``.
Sweep: 64 passed, 2 failed (same pre-existing module-impl bugs as
before this commit). Pre-commit clean.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Five Module 1 sub-prompts (`_derive_task_from_video`,
`_generate_task_rephrasings`, `_generate_subtasks`, `_generate_plan`,
`_generate_memory`) all repeated the same shape:
result = self.vlm.generate_json([messages])[0]
if isinstance(result, dict) and isinstance(result.get(<field>), <type>):
...
…each spelled with slightly different field names + post-processing.
Three small helpers replace it:
* `_vlm_field(messages, field)` — single VLM call, returns
``result[field]`` or ``None``. Centralizes the
``generate_json([m])[0]`` + ``isinstance(dict)`` dance.
* `_text_message(text)` — wraps a string in the canonical user-message
shape every text-only prompt builds inline.
* `_video_message(record, prompt)` — combines the episode video block
with a prompt; replaces the duplicated video-block construction
inside `_generate_subtasks` (which previously inlined the same
``use_video_url``/``frames_per_second``/``max_video_frames`` branches
that `_episode_video_block` already implements).
Net -35 LOC. Each call site now is 3-5 lines instead of 10-20. The
public method signatures are unchanged so tests don't move.
Drive-by: `_task_seems_bad` collapsed via SIM103 fix; `zip` in
`run_plan_updates` annotated `strict=True` per ruff B905.
Tests: same 2 pre-existing module-impl failures
(`test_module1_attaches_video_block_to_subtask_prompt`,
`test_module2_mid_episode_emits_paired_interjection_and_speech`) —
they were failing on `origin/feat/language-annotation-pipeline` before
this commit and continue to do so for the same reasons. 61/63 in the
language stack pass; pre-commit clean.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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>
* fix(ci): run multi-task benchmark evals 5-at-a-time in parallel
The eval script supports running tasks concurrently via a
ThreadPoolExecutor (env.max_parallel_tasks). Apply it to the four
multi-task benchmark CI jobs (RoboTwin, RoboCasa, RoboMME, LIBERO-plus
— 8-10 tasks/task_ids each) so they finish in ~2 waves of 5 instead of
running sequentially. Single-task jobs (Libero, MetaWorld, RoboCerebra)
are unchanged.
* fix(ci): cap VLABench smoke eval at 50 steps per task
VLABench's default episode_length is 500 steps; with 10 tasks at ~1 it/s
the smoke eval took ~80 minutes of rollouts on top of the image build.
The eval is a pipeline smoke test (running_success_rate stays at 0% on
this short rollout anyway), so we don't need full episodes — cap each
task at 50 steps to bring total rollout time down ~10x.
* fix(ci): run VLABench tasks 5-at-a-time in parallel
The eval script already supports running multiple tasks concurrently via
a ThreadPoolExecutor (env.max_parallel_tasks). Set it to 5 so the 10
VLABench tasks finish in ~2 waves instead of running sequentially.
* feat: add pretrained vision encoder weights for diffusion and vqbet
* fix test by re-generating artifacts
---------
Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>
The robotwin benchmark Dockerfile still installed cuda-nvcc-12-4 and
cuda-cudart-dev-12-4 after #3505 upgraded the base image to CUDA 12.6.3
on Ubuntu 24.04. Those packages aren't available in the ubuntu2404 CUDA
repo, so the build failed at apt-get install. Bumping both to -12-6 to
match the base image.
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>
* **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>
* **#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>
`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>
* `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>
`_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>
* 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>
Ensure annotated datasets advertise language columns in meta/info.json so non-streaming dataset loads cast against the rewritten parquet schema.
Co-authored-by: Cursor <cursoragent@cursor.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>
Module 1 now produces ``task_aug`` rows (registered in PR 1) so the
PR-1 ``${task}`` resolver can rotate phrasings deterministically per
``sample_idx``. Plus an opt-in video-derived task that bypasses the
canonical ``meta/tasks.parquet`` task when it's empty, low-quality, or
explicitly disabled — every downstream Module-1 prompt then uses the
derived task as its grounding.
- ``Module1Config``: adds ``n_task_rephrasings`` (default 10) and
``derive_task_from_video`` ∈ ``{off, if_short, always}`` (default
``if_short``: triggers when canonical is empty, < 3 words, or matches
a placeholder string like ``debug`` / ``unnamed`` / ``tbd``).
- ``plan_subtasks_memory.py``: ``run_episode`` now resolves an
``effective_task`` (canonical OR video-derived) and threads it
through ``_generate_subtasks`` / ``_generate_plan`` /
``_generate_memory`` so subtasks, plans, and memory are all grounded
in the same task string. Then generates ``n`` rephrasings of the
effective task and writes them as ``task_aug`` rows at ``t=0`` with
``role=user``. The effective task itself is included as the first
variant so the rotation is guaranteed to cover the source-of-truth
phrasing.
- New prompts: ``module_1_video_task.txt`` (one-shot video → task),
``module_1_task_rephrasings.txt`` (text-only paraphraser, ``n`` per
call).
- ``meta/tasks.parquet`` is NOT modified — derived tasks live only in
``language_persistent``.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
qwen36moe-11 surfaced a deeper semantic problem with mid-episode
interjections: they were generated as *counterfactual* user requests
("actually skip the wipe", "use the blue one instead") but teleop data
is frozen — the robot in the video already executed everything,
including the steps the user "asked to skip". The training signal was
therefore self-contradictory: interjection text said one thing, the
robot's subsequent action stream did the opposite.
Flip the framing. Anchor every interjection at a subtask boundary and
write it as a natural user request for the *upcoming* subtask. The
robot's visible next behavior IS the interjection's effect, so:
interjection text → plan refresh → action stream
are all consistent with the same observed video.
Concretely:
- ``interjections_and_speech.py``: instead of sampling random
timestamps from ``frame_timestamps``, walk Module 1's subtask spans
and sample from the (subtask N → subtask N+1) transitions. Pass both
the just-finished and the upcoming subtask texts into the prompt.
- ``_window_timestamps``: re-center the multi-frame video window on
the boundary itself (half the frames cover the end of the previous
subtask, half cover the start of the next one) so the VLM has the
same visual conditioning the policy will see at training time.
- ``module_2_interjection.txt``: rewritten. The prompt now states
explicitly that this is offline data, the robot already committed to
the next subtask, and the interjection must be a natural request
that aligns with — not contradicts — the next subtask. Removes the
"negative task / situated correction" Hi Robot framing because those
scenarios require online execution to be coherent.
Plan-refresh logic from the previous commit (forwarding interjection
text into the refresh prompt) is unchanged and now reinforces the same
direction: the refreshed plan emphasizes the upcoming subtask the
interjection just asked for.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
qwen36moe-10 showed three Module-2 / plan-refresh quality issues that
are not architecture problems — they're prompt-grounding bugs:
1. Interjection prompt passed ``current_subtask = record.episode_task``
(the WHOLE-episode task), not the actual subtask in force at the
chosen timestamp. The VLM had no signal about what was visible at
that moment, so its interjections were generic ("actually skip X"
where X had nothing to do with the visible activity).
2. Interjection prompt only attached a single frame
(``frames_at(record, [t_snap])``). With one frozen image the VLM
couldn't read the ongoing motion. Module 1 already gets the whole
episode video for subtask decomposition, which is why subtasks are
well-grounded; Module 2 was the outlier.
3. The plan-refresh prompt told the model "a plan refresh after a user
interjection at t=X.YZs" but never showed it the interjection
*text*. So the refreshed plan couldn't actually reflect the user's
correction — at best it recombined the same step list.
Fix:
- ``interjections_and_speech.py``: Module 2 reads Module 1's subtask
rows from the same staging tree (executor orders module_1 → module_2
so they're already there) and resolves the actual ``current_subtask``
at each chosen timestamp. Pulls a small clip
(``interjection_window_seconds`` × ``interjection_window_frames``,
defaulting to 4 frames over the leading 2 s) instead of one frame.
Drops the silently-zeroing ``len(candidate_ts) // 4`` cap on the
interjection count.
- ``module_2_interjection.txt``: prompt is rewritten to reference the
multi-frame visual context and require the interjection to mention
something visible OR named in the current subtask, not invented.
- ``plan_subtasks_memory.py``: ``run_plan_updates`` now accepts and
threads through interjection texts. ``_generate_plan(refresh_t,
interjection)`` injects both the current subtask AND the interjection
text into the prompt so the refreshed plan can drop / reorder /
constrain steps to match the user's correction. (Plan still refreshes
ONLY at user interjections — subtask generation runs ~1 Hz at
inference, plan re-emission is event-driven.)
- ``executor.py``: forwards ``interjection_texts`` alongside
``interjection_times`` to ``run_plan_updates``.
- ``Module2Config``: bumps ``max_interjections_per_episode`` default
from 1 to 3 and exposes ``interjection_window_seconds`` /
``interjection_window_frames``.
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>
``lerobot.datasets.video_utils.decode_video_frames`` routes
``backend="pyav"`` through ``decode_video_frames_torchvision`` →
``torchvision.io.VideoReader``, but ``VideoReader`` was removed in
torchvision >= 0.22 (the vllm/vllm-openai:latest container ships with
torchvision 0.25). That made every Module 3 frame decode raise
``AttributeError: module 'torchvision.io' has no attribute 'VideoReader'``,
which the previous catch-all silently turned into an empty image list,
which then made every Module 3 prompt skip via the
``not _has_image_block(messages)`` branch and produce zero VQA rows.
Bypass ``video_utils`` entirely. The annotation pipeline only needs
a handful of PIL frames per (episode, ts), so a direct PyAV decode is
both simpler and insulated from torchvision API churn. ``av`` is already
in the install set, no new dependency.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
VideoFrameProvider._decode used to swallow every exception silently and
return []. That made Module 3 (VQA) produce zero rows whenever local
video decoding broke (codec, backend, missing file, ...) because every
prompt got skipped via the ``not _has_image_block(messages)`` branch in
general_vqa.py — without any signal in the job log.
Log the first failure with full exception info (subsequent failures
stay quiet to avoid log spam) so this fast-path is debuggable.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Print the default and full camera list once at the top of every run so a
silent Module-3-no-op (cam_keys=[]) is visible in the job log instead of
only being discoverable by counting parquet rows after upload.
Also warn loudly when Module 3 is enabled but no cameras resolved, with
a hint about the --vlm.camera_key fallback.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Module 3 fast-pathed out (50 episodes in 0.6s) when
``frame_provider.camera_keys`` came back empty even though Module 1/2
worked, because they use ``frame_provider.camera_key`` (singular) and
were happy with the explicit ``--vlm.camera_key=...`` override.
Two fixes:
- ``frames.py``: read ``meta.camera_keys`` (covers both video- and
image-stored cameras) instead of ``meta.video_keys`` (video-only),
matching :class:`LeRobotDatasetMetadata`'s canonical accessor. If
metadata still surfaces nothing but the caller explicitly passed
``--vlm.camera_key=<key>``, fall back to ``[<key>]`` — the key is by
definition known to exist on the dataset.
- ``general_vqa.py``: emit a one-time WARNING log when Module 3 sees
zero cameras so this never silently produces zero VQA again.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
A ready-to-run example of launching the annotation pipeline on a
Hugging Face job (h200x2) with two vllm replicas serving
Qwen3.6-35B-A3B-FP8. Lives next to other end-to-end recipes under
examples/.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Module 3 now produces one (vqa, user) + (vqa, assistant) pair per
emission tick *per camera* rather than only against the dataset's first
camera. Each emitted row carries the `camera` field added in PR 1
(language-columns), so the resolver can disambiguate per-camera VQA via
`emitted_at(t, style=vqa, role=assistant, camera=...)` without ambiguity.
- `frames.py`: `FrameProvider` Protocol gains a `camera_keys` property
and a `camera_key=` argument on `frames_at` / `video_for_episode`.
`VideoFrameProvider` exposes every `observation.images.*` key the
dataset declares (not just the first) and keys its decode cache on
`(episode, camera, timestamp)` so per-camera reads don't collide.
Module 1 / 2 keep their old single-camera behaviour by leaving
`camera_key=None` (falls back to the default camera).
- `modules/general_vqa.py`: `run_episode` iterates `frame_provider
.camera_keys` for each emission tick, builds one prompt per camera,
batches all of them through the VLM, and stamps the resulting rows
with `camera=<that key>`. Empty `camera_keys` (null provider) makes
the module a no-op rather than silently emitting untagged rows.
- `writer.py`: `_normalize_persistent_row` / `_normalize_event_row`
carry `camera` through and call `validate_camera_field` so the
invariant is enforced at the writer boundary. Event sort key now
includes `camera` for deterministic ordering when several cameras
share `(timestamp, style, role)`. `speech_atom` sets `camera=None`.
- `validator.py`: `StagingValidator` gains a `dataset_camera_keys`
field; `_check_camera_field` enforces the invariant and cross-checks
every view-dependent row's `camera` against the dataset's known video
keys. New `_check_vqa_uniqueness_per_frame_camera` flags duplicate
`(vqa, role)` pairs at the same `(t, camera)`.
- `lerobot_annotate.py`: passes the live frame provider's
`camera_keys` into the validator so the cross-check uses the actual
dataset camera set.
- Tests: `_StubFrameProvider` exposes `camera_keys` and accepts the new
`camera_key=` kwarg. `test_module3_vqa_unique_per_frame_and_camera`
configures two cameras and asserts both are represented, that every
emitted row has a `camera` tag, and that uniqueness holds per
`(timestamp, camera, role)`.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Modern LeRobot datasets store videos in AV1, which vllm's libav build
cannot decode (the video processor returns 0 frames and downstream
chokes with ZeroDivisionError). Re-encode each per-episode subclip
with libx264 (preset ultrafast, crf 23) so the resulting mp4 is
universally decodable. Strip audio with -an for a smaller payload.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Adds VlmConfig.num_gpus so parallel_servers can exceed the physical
GPU count. Replicas are round-robin-assigned to GPUs (e.g.
parallel_servers=4 + num_gpus=2 → replicas pinned to GPUs 0,1,0,1).
Backward-compatible: num_gpus=0 keeps the existing 1-replica-per-GPU
behavior.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Lets callers pass per-request template flags such as
{"enable_thinking": false} for Qwen3.5/Qwen3.6 models, where the
default thinking preamble otherwise consumes the entire max_new_tokens
budget before any JSON is emitted.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The setuptools package-data declaration only listed envs/*.json, so
pip-installed wheels (including HF Jobs runs) were missing the
module_1_subtasks/plan/memory and module_2/3 prompt templates,
causing FileNotFoundError at runtime.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Default backend is now a local OpenAI-compatible server (vllm /
transformers) which auto_serve spawns. Removes the
use_hf_inference_providers config flag and the router.huggingface.co
routing branch.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
After the pipeline completes, optionally create/locate a dataset repo
and upload the dataset root (excluding .annotate_staging/). Add
push_private and push_commit_message knobs.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Saturates parallel_servers + client_concurrency. Previously the
executor processed one episode at a time, so each Module 1 episode's
3-5 dependent VLM calls hit a single server with the others idle. Now
defaults to 16 episodes in flight; configurable via
ExecutorConfig.episode_parallelism.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
vllm with --uvicorn-log-level warning suppresses the "Uvicorn running"
banner that the readiness watcher waited for, so the spawn helper hung
forever even after the API was live. Add an HTTP probe in parallel with
the log watcher and broaden the log markers to include vllm's own
"Starting vLLM API server" / "Available routes are" lines.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
8 server-streaming threads writing chars unsynchronized cause UTF-8
sequences from different servers to interleave mid-byte, garbling the
terminal output. Switch to line-buffered reads with a single shared
print lock — output stays readable, ready-marker detection still works
on the line containing 'Uvicorn running' / 'Application startup
complete'.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Adds vlm.client_concurrency (default 16) which uses a ThreadPoolExecutor
to fan out batched chat.completions calls. vllm batches them internally
on the server side, giving big throughput wins on a single TP=1 server
without needing DP/TP and the NCCL setup it requires.
Module 3 now batches all per-episode VQA calls into a single
generate_json invocation so they fire in parallel.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Pepijn