Blame for LABELS.md

# Failure labels for the 399 rollouts

Per-episode files next to `rollout.npz` / `rollout.mp4` in each `<task>/<ep>/` folder, plus `manifest.csv` (one row per episode with outcome and onset).

| file | content |
|---|---|
| `labels.json` | `success` (human annotation), `semantic_timeline` (3 s keyword phrases per segment with control / policy step ranges), `vlm_failure` (failed episodes: mode, onset time and frame, reason / recovery / prevention text with short summaries, confidence, raw Gemini responses, token usage), `failure_annotation` (onset mapped to `control_step`, `policy_step`, `chunk_index`), `labeler` (model, backend, date, the prompts) |
| `labels.npz` | the same fields as arrays: `fail_onset_frame`, `fail_onset_seconds`, `fail_mode`, `fail_reason`, `fail_recovery`, `fail_prevention` (+ `_summary`), `sem_t_start`, `sem_t_end`, `sem_control_start`, `sem_control_end`, `sem_phrase` |
| `example.md` | a readable rendering of the above for that episode |

## Contents

- 399 episodes: 145 successes (no onset), 254 failures with a failure onset.
- Failure modes (Gemini, one per failed episode): grasp_failure 144, stuck_or_no_progress 31, placement_or_insertion_failure 29, object_displacement 24, open_close_failure 19, timeout_or_insufficient_progress 4, wrong_object 3.
- Onset types: obvious_mistake 223, operator_intervention 27, timeout 4.
- Per task (episodes / failures): basket 50/42, blocks 50/44, closer_drawer 50/22, eggplant 50/25, mushroom 49/36, pineapple_out_of_drawer 50/38, push_pineapple 50/23, tomato 50/24.

## Provenance and caveats

- Labeller: Gemini (`gemini-3.5-flash`), prompts in `GEMINI_PROMPTS.md`. 399 episode(s) via `GeminiBackend` (labelled 2026-10-20 to 2026-10-22).
- The labels are model outputs and have not been individually verified by a person; the success flag comes from the operator. Onset times are the first visible mistake.
- `vlm_failure_onset_frame` is a control-step index at the recorded control rate (about 50 Hz); the policy step is stored in `failure_annotation.failure_policy_step` and should be used instead of dividing the frame by `replan_steps` (the robot executes about 5.8 control steps per policy call on average, not the nominal 5 or 6).
- A few onsets are at the very start or very end of a video (`operator_intervention` at the first frames, `timeout` in the last seconds); these are worth a manual look before using them as supervision.
- Successes carry a keyword-phrase timeline but no failure fields.