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lerobot/src/lerobot/annotations/steerable_pipeline/prompts/plan_subtasks.txt
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Pepijn 279c6c7af3 feat(annotate): improve VLM subtask annotation (legible contact sheets, seeded relabeling, self-hosted vLLM recipe) (#3896)
* feat(annotate): WGO-tuned subtask prompt (atomic completed-events + duration prior)

Rework the plan-module subtask segmentation prompt toward the WGO-Bench
atomic annotation protocol: segment by completed world-state changes
(grasp/place/open/close/pour/insert), fold approach+retreat into their
event, keep separate events separate, and add a 2-10s duration prior.
Drops the pi0.7 "fewer larger composites preferred" bias that drove
under-segmentation on the benchmark. Output JSON shape unchanged.

Co-authored-by: Cursor <cursoragent@cursor.com>

* feat(annotate): seeded-relabeling second pass for subtasks

Add an opt-in relabel pass (plan.subtask_seeded_relabel) that, after
segmentation, re-labels each span using previous/current/next segment
contact sheets and the seed label as a strong prior, minimally correcting
it. Mirrors macrodata's best end-to-end labeling step. Boundaries are
untouched; one extra VLM call per span. Off by default.

Co-authored-by: Cursor <cursoragent@cursor.com>

* feat(annotate): robust OpenAI-compat client for hosted VLMs

Guard against a choice with no message (safety filter or a thinking model
that spends its whole budget before emitting content) so one empty reply
no longer crashes the whole annotation run; treat it as an empty response
and let the existing JSON-retry path handle it.

Add an optional `reasoning_effort` knob on VlmConfig, forwarded to the
server when set, to cap a thinking model's reasoning (needed for Gemini
via its OpenAI-compatible endpoint).

Co-authored-by: Cursor <cursoragent@cursor.com>

* feat(annotate): legible tile-scaled timestamp on contact sheets

The burned-in timestamp used the ~10px bitmap default font, which blurs
once the model downsamples a full contact sheet into 768px tiles, so the
VLM can no longer read the exact source time a boundary depends on. Scale
the timestamp to the tile height (with a graceful fallback on older
Pillow) so the visual time cue stays readable at sheet resolution.

Co-authored-by: Cursor <cursoragent@cursor.com>

* feat(annotate): lean GEPA-aligned subtask segmentation prompt

Replace the verbose, label-heavy segmentation prompt with a lean
adaptation of the blog's GEPA-found completed_events_duration_prior
recipe: focus on completed manipulation events, explicit no-split /
no-merge rules, a 2-10s duration prior, and an instruction to prioritize
temporally correct boundaries over label wording. The previous prompt
over-weighted label guidance, which traded away boundary precision.

Co-authored-by: Cursor <cursoragent@cursor.com>

* revert: restore original subtask segmentation prompt

The lean GEPA-aligned paraphrase (dd4b0110d) regressed Gemini on the
30-ep subset: Seg F1 0.259 -> 0.189 and E2E 0.184 -> 0.135, driven by
worse under-segmentation (224 -> 188 preds). The blog's 0.306 came from
the actual GEPA-search artifact, which a hand paraphrase does not
reproduce. Restore the original prompt, which remains our best config.

Co-authored-by: Cursor <cursoragent@cursor.com>

* feat(annotate): env-var override for prompt templates

Allow LEROBOT_PROMPT_OVERRIDE_<name> to supersede the packaged prompt
file at load time. Enables prompt search (GEPA) to inject candidate
segmentation prompts into a remote annotate job via an env secret,
without committing a branch per candidate.

Co-authored-by: Cursor <cursoragent@cursor.com>

* docs(annotate): genericize hosted-VLM comments (no model name)

Co-authored-by: Cursor <cursoragent@cursor.com>

* docs(annotate): document seeded-relabel and reasoning_effort flags

Co-authored-by: Cursor <cursoragent@cursor.com>

* test(annotate): update subtask-prompt marker to match WGO-tuned prompt

The three plan-module tests keyed the canned VLM responder on the
literal 'atomic subtasks', which the WGO-tuned segmentation prompt no
longer contains (it now segments 'COMPLETED manipulation events'). Point
the fixture markers at the current wording so the subtask call is matched
again.

Co-authored-by: Cursor <cursoragent@cursor.com>

---------

Co-authored-by: Cursor <cursoragent@cursor.com>
2026-07-15 11:38:49 +02:00

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You are annotating a teleoperated robot demonstration shown as
timestamped contact sheets (each tile has its time in seconds burned
into the top-left corner). The operator's goal was: "{episode_task}"
{observation_block}Reconstruct the sequence of COMPLETED manipulation events the robot
performs, in chronological order. Output one segment per event with a
[start, end] time in seconds and a short action label.
GROUNDING — read first, it overrides everything below:
- Label ONLY events you can SEE in the frames. The instruction is the
goal; the VIDEO is the ground truth for what actually happened.
- Do NOT invent, anticipate, or pad steps that are not shown.
Granularity — segment by completed events, not by motion:
- Start a NEW segment whenever the world state changes: an object is
grasped, lifted, transported, placed, or released; a held object
changes; a drawer/door/lid/container opens or closes; contents move
between containers (poured); a tool starts or stops acting on a
surface. Watch the gripper open/close transitions — they usually mark
boundaries.
- Do NOT split approach, reach, grasp adjustment, small repositioning,
hesitation, or retreat into their own segments. Fold each into the
event it belongs to (the approach is part of the pick; the retreat is
part of the place).
- Do NOT merge separate completed events. Each distinct pick, place,
open, close, pour, push, wipe, or insert is its own segment, even when
they repeat on different objects or locations.
- Most segments last 2-10 seconds. Shorter segments are okay ONLY for
fast pick / place / open / close / release events. Never emit a
segment shorter than {min_subtask_seconds} seconds; merge a too-short
candidate into its neighbour instead.
- Skip idle time, pure camera motion, and tiny hand jitter.
Labels — short imperative phrases:
- One concise command naming the action and the manipulated object, e.g.
"pick up the red cup", "put the cup on the shelf", "open the top
drawer", "pour water into the glass", "insert the plug into the
socket".
- Include source, destination, side, direction, or the final
open/closed state when it is visible and central to the event.
- Prefer these verbs (extend only when none fits): pick up, put, place,
push, pull, turn, press, open, close, pour, insert, wipe, stack.
Disambiguate by what you SEE:
* STACK vs PUT: object placed ON TOP OF another object -> "stack".
* INSERT vs PUT: object pushed INTO a fitted slot/hole/socket -> "insert".
* PICK UP vs PUT (direction): gripper CLOSES and object moves WITH
the hand -> "pick up"; gripper OPENS and object stays -> "put".
* POUR vs PUT: source is tilted and contents flow -> "pour".
- Use the exact object nouns implied by the task; stay consistent across
the episode (don't switch "cube" to "block").
- Write imperative commands, never third person ("the robot ..."), and
drop articles/adverbs.
Timing:
- Use the burned-in timestamps to set start and end. Boundaries should
land on or near a printed time, and every [start, end] must lie within
[0.0, {episode_duration}] seconds, be non-overlapping, and cover the
episode in order.
- Emit at most {max_steps} segments.
Output strictly valid JSON of shape:
{{
"subtasks": [
{{"text": "<short imperative action label>", "start": <float>, "end": <float>}},
...
]
}}