Subtask prompt (``module_1_subtasks.txt``):
- Lock the verb vocabulary to composite atomic actions (``pick up``,
``put``/``place``, ``push``/``pull``, ``turn``, ``press``, ``open``/
``close``, ``pour``, ``insert``, ``go to``).
- Add an explicit ``Forbidden ultra-fine splits`` block instructing
the VLM to fold ``move to X`` / ``reach for X`` / ``grasp X`` /
``lift X`` / ``release X`` into the parent composite. Previous
examples actively encouraged the over-segmentation pattern.
- Rewrite the Good/Bad examples around the composite contract.
Job config (``examples/annotations/run_hf_job.py``):
- Point at ``pepijn223/robocasa_smoke_2atomic_v3`` on ``h200x4``.
- ``--vlm.camera_key=robot0_agentview_left`` (real key for the
dataset; the prior ``observation.images.wrist`` did not exist
and would have silenced the VQA module).
- ``--vlm.serve_command`` ``--max-model-len 131072`` (4x): keeps
90 s @ 1 Hz episode video blocks under context even at full
Qwen vision resolution. On 1x H200 (144 GB) the 35B-FP8 model
has plenty of room for the bigger KV cache.
- ``--vocabulary.enabled=false`` — heterogeneous dataset, no
benefit from a single canonical vocabulary.
- ``--plan.derive_task_from_video=off``, ``--plan.n_task_rephrasings=0``
— reuse the dataset's own ``episode_task`` strings as-is.
- ``--plan.min_subtask_seconds=3.0``, ``--plan.plan_max_steps=6`` —
give the new composite-action rules room to land (1.5 s floor
was too small to host a full grasp-or-place composite).
- ``--vqa.vqa_emission_hz=3.0`` — denser VQA grounding.
- Timeout 24h, episode_parallelism=64, client_concurrency=256 to
scale to the 25k-trajectory regime when the same recipe is
pointed at a larger dataset.
Co-authored-by: Cursor <cursoragent@cursor.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>
Three stale things in the launcher script:
- ``--module_1/2/3.*`` no longer exist; review commit fd18beb renamed
the CLI namespaces to ``--plan/interjections/vqa``. Forwarded all
eight existing args to their new names.
- ``--push_to_hub`` is now a bool; the destination repo lives at
``--dest_repo_id``. Split the single positional into both args.
- ``openai`` was missing from the pip install list, which the prior
review review (claude bot, 2026-05-08) flagged — the default vlm
backend is ``openai`` so the job would have ImportError'd. Added.
Also expose the new phase 0 (canonical vocabulary discovery) knobs
explicitly: ``--vocabulary.sample_episodes``, ``--n_subtask_target``,
``--n_memory_target``. Defaults are sane (3 / 10 / 6) but worth
flagging in the example so the operator knows what they're running.
Update the docstring + section comments to match the current phase
layout (vocabulary → plan → interjections → vqa → writer).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Co-authored-by: Cursor <cursoragent@cursor.com>
Adds an optional `dest_repo_id` to AnnotationPipelineConfig. When set,
`push_to_hub` uploads the annotated dataset there instead of overwriting
the source `repo_id`, restoring separate source/destination repos.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.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>
pepijn