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feat(annotate): let the VLM decide vocabulary size

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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>
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pepijn
2026-05-22 11:46:31 +00:00
parent 369ab17110
commit 54221ceea2
5 changed files with 31 additions and 27 deletions
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docs/source/annotation_pipeline.mdx
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@@ -24,16 +24,17 @@ rewrites the data shards in place:
The `plan` module is constrained to a **canonical vocabulary** discovered
once per dataset by the `vocabulary` module (phase 0). It watches a few
sample episode videos (`--vocabulary.sample_episodes`, default `3`) and
asks the VLM to derive a small set of imperative subtask labels
(~`--vocabulary.n_subtask_target`, default `10`) and first-person memory
milestones (~`--vocabulary.n_memory_target`, default `6`) that recur
across the demos. The result lands at
`meta/canonical_vocabulary.json` (human-readable / hand-editable) and is
reused on every subsequent run. The `plan` module then constrains both
subtask + memory generation to those exact strings — the downstream
low-level policy sees a small, repeatable target distribution instead of
thousands of LLM paraphrases. Disable with `--vocabulary.enabled=False`
to fall back to free-form generation.
asks the VLM to derive a small set of imperative subtask labels and
first-person memory milestones that recur across the demos. The VLM
picks the right number of entries itself based on what it sees in the
clips — short pick-and-place demos get ~6 subtask labels, longer
multi-step recipes get more. The result lands at
`meta/canonical_vocabulary.json` (human-readable / hand-editable) and
is reused on every subsequent run. The `plan` module then constrains
both subtask + memory generation to those exact strings — the
downstream low-level policy sees a small, repeatable target
distribution instead of thousands of LLM paraphrases. Disable with
`--vocabulary.enabled=False` to fall back to free-form generation.
The writer does **not** add a `tools` column to the parquet — the tool
catalog lives at `meta/info.json["tools"]` instead (see