lerobot-clone/docs/source/annotation_pipeline.mdx

# Annotation Pipeline

`lerobot-annotate` watches each episode's video with a vision-language
model (VLM) and writes natural-language annotations back into your
dataset. It fills the two language columns from the
[Language Columns and Recipes](./language_and_recipes) page —
`language_persistent` and `language_events` — straight into
`data/chunk-*/file-*.parquet`.

In short: point it at a LeRobot dataset, and it adds subtasks, plans,
memory, interjections, speech, and visual Q&A that a policy can be
trained on.

## How it fits together

```text
  your dataset                  lerobot-annotate
  (LeRobot v3.1)
        │
        ▼
  ┌─────────────────────────────────────────────────────┐
  │                    read episodes                     │
  └──────────────────────────┬──────────────────────────┘
                             │
        ┌────────────────────┼────────────────────┐
        ▼                    ▼                     ▼
  ┌──────────┐      ┌───────────────┐        ┌──────────┐       one shared Qwen-VL
  │   plan   │      │ interjections │        │   vqa    │  ◀──   server (vLLM, OpenAI
  └────┬─────┘      └───────┬───────┘        └────┬─────┘        API) drives all three
       └────────────────────┼─────────────────────┘
                            │   each module stages raw JSONL
                            ▼   into .annotate_staging/
                  ┌─────────────────┐
                  │    validator    │  ◀──  checks everything
                  └────────┬────────┘
                           ▼
                  ┌─────────────────┐
                  │     writer      │
                  └────────┬────────┘
                           ▼
              data/chunk-*/file-*.parquet
              (+ meta/info.json tools)
```

Three modules (`plan`, `interjections`, `vqa`) all talk to **one** shared
VLM. Each module stages its output to disk, a validator checks it, and a
single writer rewrites the dataset shards in place.

## What the pipeline produces

Each module emits a few kinds of annotation ("styles"), routed to one of
the two language columns:

| Style / atom                                | Column                | Module          |
| ------------------------------------------- | --------------------- | --------------- |
| `subtask` (Pi0.7-style "how, not what")     | `language_persistent` | `plan`          |
| `plan` (initial + refresh on interjection)  | `language_persistent` | `plan`          |
| `memory` (MEM-style compression)            | `language_persistent` | `plan`          |
| `task_aug` (rephrasings of the task)        | `language_persistent` | `plan`          |
| `interjection`                              | `language_events`     | `interjections` |
| speech tool-call atom (`style=null`, `say`) | `language_events`     | `interjections` |
| `vqa` (user / assistant pair)               | `language_events`     | `vqa`           |

### How subtasks are generated

The `plan` module doesn't ask the VLM for subtasks in one shot. Instead
it uses a two-step **describe → segment** flow:

1. **Describe** — the VLM narrates only what it actually sees in the
   chosen camera (no guessing about the task).
2. **Segment** — that description is fed back in, and the VLM splits the
   episode into consecutive atomic subtasks.

The resulting spans are then stitched into a gap-free, full-episode
cover, so **every frame has exactly one active subtask**. See
[`run_hf_job.py`](https://github.com/huggingface/lerobot/blob/main/examples/annotations/run_hf_job.py)
for the production settings (single camera, embedded frames, windowed
subtask generation).

### Tools

The writer does **not** add a `tools` column to the parquet. The tool
catalog lives in `meta/info.json["tools"]` instead (see [Tools](./tools)).
After every run, the pipeline makes sure the canonical `say` schema is in
that list, keeping any tools you declared beforehand.

Want to add your own tool? Edit `meta/info.json["tools"]` directly — the
pipeline preserves whatever is already there. That makes the tool visible
to the chat template, so the model can learn to _generate_ the call. The
runtime layer that actually _executes_ a generated call (the `Tool`
protocol / `TOOL_REGISTRY` under `src/lerobot/tools/`) is not part of
this PR — the [Tools](./tools) doc marks those pieces as
not-yet-implemented.

## Running on Hugging Face Jobs

Annotation runs on [Hugging Face Jobs](https://huggingface.co/docs/hub/en/jobs).
The repo ships a launcher script you copy and tweak for your dataset:

```bash
HF_TOKEN=hf_... uv run python examples/annotations/run_hf_job.py
```

[`run_hf_job.py`](https://github.com/huggingface/lerobot/blob/main/examples/annotations/run_hf_job.py)
starts a single-GPU `h200` job (bump it to `h200x4` for big datasets)
that:

1. installs `lerobot` (from `main`) plus the annotation extras,
2. boots one vLLM server per GPU (using the `vllm/vllm-openai` image) and
   drives it over the OpenAI-compatible API,
3. runs the `plan` / `interjections` / `vqa` modules across the dataset
   with `lerobot-annotate`,
4. with `--push_to_hub=true`, uploads the result to `--new_repo_id` (or
   back to `--repo_id` in place if you leave that unset).

To use a different dataset, model, or hub repo, edit the `CMD` block in
the script. Every flag there maps directly to a `lerobot-annotate` flag
(run `lerobot-annotate --help` for the full list).

## Contributing new modules

The pipeline is built to grow, and **contributions are very welcome** —
a brand-new module (say, trajectory traces or affordances), a new prompt
template, a smarter grounding flow, or quality fixes to the existing
`plan` / `interjections` / `vqa` modules.

Every module lives under
`src/lerobot/annotations/steerable_pipeline/modules/`, shares the VLM
client and the keyframe cache, writes its raw output to the staging
tree, and plugs into the executor as its own phase. Got an idea? Open an
issue or PR on [the repo](https://github.com/huggingface/lerobot).

## How recipes consume the output

The annotations are meant to be read by recipes (see
[Language Columns and Recipes](./language_and_recipes)). Typically:

- low-level / high-level / memory-update branches read
  `subtask` / `plan` / `memory` from `language_persistent`.
- an interjection-response branch reads `interjection` events plus the
  paired speech atom (merged into one assistant turn via `tool_calls_from`)
  and the matching `plan` refresh at the same timestamp.
- a VQA branch reads the `(vqa, user)` and `(vqa, assistant)` pairs from
  `language_events`.

## Why state and events are split

Two ideas shape the design:

1. **Persistent state vs. exact events.** Persistent rows (`subtask`,
   `plan`, `memory`) apply to the whole episode and answer "what's true
   right now?". Event rows (`interjection`, `vqa`, speech) appear only on
   the one frame whose timestamp matches. Timestamps are copied straight
   from the source parquet — never recomputed in floating point.
2. **One VLM pass.** All three modules share a single VLM client (the
   OpenAI-compatible client talking to the job's vLLM server), so you pay
   for one model load per dataset, not three.

## Re-running a single module

Each module stages its raw output to
`<root>/.annotate_staging/episode_{N:06d}/<module>.jsonl`. This makes
prompt iteration cheap: re-running one module overwrites only its own
JSONL, then the writer recomposes the final parquet. Disable modules you
don't want with `--plan.enabled=false` (and likewise
`--interjections.enabled` / `--vqa.enabled`) to test one at a time.

## What the validator checks

Before the writer runs, `StagingValidator` confirms:

- every event row lands exactly on a real frame timestamp;
- no speech / interjection pairs are left orphaned;
- `plan` is refreshed at every interjection timestamp;
- `memory` rows fall on subtask boundaries (a warning, not an error);
- each VQA assistant `content` is valid JSON in one of the
  bbox / keypoint / count / attribute / spatial shapes;
- every row goes to the column chosen by `column_for_style(style)`.

Any error aborts the writer. Pass `--skip_validation=true` to override
while debugging.

## Where each module's ideas come from

- **`plan` — subtasks.** Hi Robot ([Shi 2025](https://arxiv.org/abs/2502.19417))
  for atom granularity ("pick up one piece of lettuce", "place bowl to
  box"); Pi0.7 ([Physical Intelligence 2025](https://pi.website/pi07))
  for "how, not what" detail.
- **`plan` — memory.** MEM ([Torne 2026](https://arxiv.org/abs/2603.03596)):
  keep only the minimal relevant information — preserve outcomes, drop
  specific attributes.
- **`interjections`.** Hi Robot's scenario taxonomy: negative task,
  situated correction, specific constraint, preference. Speech is a
  tool-call-only atom
  (`tool_calls=[{type:function, function:{name:"say", arguments:{text:...}}}]`).
- **`vqa`.** ECoT ([Zawalski 2024](https://arxiv.org/abs/2407.08693)) for
  grounded features (pixel bounding boxes `[x_min, y_min, x_max, y_max]`,
  keypoints) and Steerable VLA Policies
  ([Zhao 2025](https://arxiv.org/abs/2509.07626)) for multi-abstraction
  grounding. Pi0.7 also grounds answers across abstraction levels.

When improving a module, tweak its prompt template in
`src/lerobot/annotations/steerable_pipeline/prompts/` rather than
rewriting from scratch.

## Roughly how much it costs

Per episode, the pipeline makes about `max_steps` plan calls,
`max_interjections_per_episode` interjection calls, and
`vqa_emission_hz × episode_seconds` VQA calls. With the defaults (8
subtasks, 1 interjection, 1 Hz × 3 pairs) on a 30-second episode, that's
~50 VLM calls.

Storage stays small: `language_persistent` is at most tens of KB per
episode (parquet dictionary-encodes the one entry that repeats across
frames), and `language_events` is empty on most frames — its size scales
with the number of emissions, not `num_frames × num_emissions`.