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>
Module 1 can now send the episode's actual mp4 file as a video_url
content block instead of pre-decoded frames. The server (transformers
serve / vllm serve / ktransformers serve) handles frame sampling at
the configured fps. Default fps=1 (one frame per second is enough for
subtask-boundary detection on manipulation episodes).
A per-episode subclip is extracted to <root>/.annotate_staging/.video_clips/
via ffmpeg stream-copy (no re-encode) so the model sees only this
episode's frames, not the whole shard.
Enable with --module_1.use_video_url=true (and --vlm.backend=openai).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
pyav (and sometimes torchcodec) decode can return fewer frames than
requested timestamps when some timestamps fall outside the video file's
content range. Drop the strict=True on the zip and rely on the
None-filter to discard missing frames.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
torchcodec's __init__ bad-allocs on the cu128/torch-2.8 stack in some
environments (Lustre/conda combos). The annotation pipeline calls
decode_video_frames many times per episode, so this is a hard blocker.
Default to pyav (always available via the av package) and let users
opt back into torchcodec via LEROBOT_VIDEO_BACKEND=torchcodec.
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>
