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Pepijn
c5042a6850
Three compounding bugs made RoboCasa annotation produce off-task
subtasks ('move stove to stove with left arm') and drifting
augmentations ('wander around the kitchen' for 'Navigate to the stove').
1. action_records.replace_subtask_text now defaults False.
Overwriting the VLM's subtask text with a reconstruction of
hallucinated {verb,object,arm,grasp,dest} fields is high-risk:
navigation / non-manipulation tasks don't fit the schema and render
to nonsense. Records are now additive by default (emit_record_row),
never silently replacing subtask text. Flip replace_subtask_text on
only for manipulation datasets verified to render cleanly.
2. _render_action_record_to_subtask_text drops a degenerate
destination that just echoes the object (verb=move object=stove
destination=stove -> 'move stove' instead of 'move stove to stove').
Also routes 'navigate' through the 'to <dest>' preposition family.
3. module_1_task_aug_axes.txt hardened: variants MUST preserve the
goal/destination. Explicitly forbids 'Navigate to the stove' ->
'wander around the kitchen'. Only wording / arm / orientation /
grasp may vary; verb meaning, object, and destination are fixed.
examples/annotations/run_hf_job.py — corrected for RoboCasa:
* derive_task_from_video=off (was =always). The dataset task string
is authoritative and is what eval conditions on; =always threw it
away, re-derived a hallucinated task from the video, and poisoned
every downstream subtask/plan row. THIS was the dominant cause.
* n_task_rephrasings=0 + task_aug_axes left off — RoboCasa eval uses
exact task strings, so augmentation is unused/harmful.
* action_records left off — manipulation schema doesn't fit atomic /
navigation tasks.
* plan_max_steps=6 to keep atomic-task decomposition tight.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
96 lines
4.0 KiB
Python
96 lines
4.0 KiB
Python
#!/usr/bin/env python
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"""Launch ``lerobot-annotate`` on a Hugging Face job (vllm + Qwen3.6 MoE).
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Spawns one ``h200x2`` job that:
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1. installs this branch of ``lerobot`` plus the annotation extras,
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2. boots two vllm servers (one per GPU) with Qwen3.6-35B-A3B-FP8,
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3. runs the plan / interjections / vqa modules across the dataset
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in free-form mode (each episode generates its own subtasks +
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memory),
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4. uploads the annotated dataset to ``--dest_repo_id`` (when set)
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or back to ``--repo_id``.
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Usage:
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HF_TOKEN=hf_... uv run python examples/annotations/run_hf_job.py
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Adjust ``CMD`` below to point at your own dataset / target hub repo.
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"""
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import os
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from huggingface_hub import get_token, run_job
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token = os.environ.get("HF_TOKEN") or get_token()
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if not token:
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raise RuntimeError("No HF token. Run `huggingface-cli login` or `export HF_TOKEN=hf_...`")
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CMD = (
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"apt-get update -qq && apt-get install -y -qq git ffmpeg && "
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"pip install --no-deps "
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"'lerobot @ git+https://github.com/huggingface/lerobot.git@feat/language-annotation-pipeline' && "
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"pip install --upgrade-strategy only-if-needed "
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"datasets pyarrow av jsonlines draccus gymnasium torchcodec mergedeep pyyaml-include toml typing-inspect "
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"openai && "
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"export VLLM_MEMORY_PROFILER_ESTIMATE_CUDAGRAPHS=0 && "
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"export VLLM_VIDEO_BACKEND=pyav && "
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"lerobot-annotate "
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"--repo_id=pepijn223/robocasa_smoke_2atomic_v3 "
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"--dest_repo_id=pepijn223/robocasa_smoke_2atomic_v3_ann "
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"--push_to_hub=true "
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"--vlm.backend=openai "
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"--vlm.model_id=Qwen/Qwen3.6-35B-A3B-FP8 "
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"--vlm.parallel_servers=2 "
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"--vlm.num_gpus=2 "
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'--vlm.serve_command="vllm serve Qwen/Qwen3.6-35B-A3B-FP8 '
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"--tensor-parallel-size 1 --max-model-len 32768 "
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'--gpu-memory-utilization 0.8 --uvicorn-log-level warning --port {port}" '
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"--vlm.serve_ready_timeout_s=1800 "
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"--vlm.client_concurrency=128 "
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"--vlm.max_new_tokens=512 "
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"--vlm.temperature=0.7 "
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"--executor.episode_parallelism=16 "
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"--vlm.chat_template_kwargs='{\"enable_thinking\": false}' "
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"--vlm.camera_key=observation.images.robot0_agentview_right "
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# Phase 1 — plan module (subtasks + plan + memory).
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"--plan.frames_per_second=1.0 "
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"--plan.use_video_url=true "
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"--plan.use_video_url_fps=1.0 "
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# IMPORTANT for RoboCasa: the dataset's task string ("Navigate to the
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# stove", "Pick the mug...") is authoritative and is what eval uses.
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# ``derive_task_from_video=off`` keeps that canonical task driving
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# subtask generation. Do NOT use ``always`` here — it throws the real
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# task away, asks the VLM "what is this video about?" with no hint,
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# and the hallucinated task then poisons every subtask + plan row.
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"--plan.derive_task_from_video=off "
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# NO task augmentation for RoboCasa: eval conditions on the exact task
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# strings, so synthetic rephrasings are unused at best and (when they
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# drift, e.g. "wander around the kitchen") harmful. 0 rephrasings +
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# axes disabled = the policy only ever sees the canonical task.
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"--plan.n_task_rephrasings=0 "
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# action_records OFF: the structured {verb,object,arm,grasp,dest}
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# schema is a manipulation schema; RoboCasa navigation / atomic tasks
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# don't fit it and the VLM hallucinates (e.g. "move stove to stove").
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# Leave off unless annotating long composite manipulation tasks you've
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# verified render cleanly (and even then replace_subtask_text stays
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# off by default so records are additive, never overwriting subtasks).
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# Keep subtask decomposition tight for atomic tasks:
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"--plan.plan_max_steps=6 "
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# Phase 2 — interjections + speech.
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"--interjections.max_interjections_per_episode=6 "
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# Phase 4 — general VQA.
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"--vqa.K=1 "
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"--vqa.vqa_emission_hz=1.0"
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)
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job = run_job(
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image="vllm/vllm-openai:latest",
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command=["bash", "-c", CMD],
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flavor="h200x2",
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secrets={"HF_TOKEN": token},
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timeout="2h",
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)
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print(f"Job URL: {job.url}")
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print(f"Job ID: {job.id}")
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