# Language columns and recipes

LeRobot stores reusable language annotations directly next to frame data in `data/chunk-*/file-*.parquet`.
The two optional columns are:

- `language_persistent`: a list of rows broadcast across every frame in an episode for state that remains active, such as `subtask`, `plan`, and `memory`.
- `language_events`: a list of rows only on the exact frame where an event was emitted, such as `interjection`, `vqa`, and speech tool calls.

Both columns share the same row shape (event rows omit `timestamp` because the
frame the row sits on already provides it):

```text
role: string
content: string | null
style: string | null
timestamp: float64        # persistent rows only
camera: string | null     # observation.images.* feature key, view-dependent rows only
tool_calls: list[Json] | null
```

The `camera` field tags rows whose `content` is grounded in a specific camera
view. Rows of view-dependent styles (`vqa`, and the reserved `motion` /
`trace`) MUST set `camera` to the matching `observation.images.*` feature key.
Rows of every other style MUST leave `camera` as `null`. Pipeline writers and
the validator enforce this via `validate_camera_field(style, camera)`.

`meta/tasks.parquet` remains the canonical source for the task. The special `${task}` recipe binding always reads that task string and does not depend on language annotations.

## Architecture

The language stack has three layers:

1. `lerobot.datasets.language` defines the schema, style registry, and `column_for_style`.
2. `lerobot.datasets.language_render` resolves rows and renders messages.
3. `RenderMessagesStep` turns dataset samples into `messages`, `message_streams`, and `target_message_indices`.

`LeRobotDataset` stays recipe-agnostic. It passes `language_persistent` and `language_events` through when present, and unannotated datasets keep their existing behavior.

## Temporal semantics

Persistent styles are active after emission until replaced:

- `active_at(t, style=subtask)`
- `nth_prev(style=memory, offset=1)`
- `nth_next(style=subtask, offset=1)`

Event styles only exist on their exact timestamp:

- `emitted_at(t, style=interjection)`
- `emitted_at(t, style=vqa, role=user, camera=observation.images.top)`
- `emitted_at(t, role=assistant, tool_name=say)`

Exact event matching has no tolerance window, so writers must stamp event rows with frame timestamps from the parquet data.

## View-dependent resolution

For view-dependent styles (`vqa`, `motion`, `trace`), the resolver gains a
`camera=` filter parallel to `role=` and `tool_name=`. Datasets with multiple
cameras typically emit one (`vqa`, `user`) + (`vqa`, `assistant`) pair per
camera at the same timestamp; without `camera=`, those resolvers see two
matches and raise an ambiguity error. Recipes consume each camera through its
own binding plus a matching image block, e.g.

```yaml
ask_vqa_top:
  bindings:
    vqa_query: "emitted_at(t, style=vqa, role=user, camera=observation.images.top)"
    vqa: "emitted_at(t, style=vqa, role=assistant, camera=observation.images.top)"
  messages:
    - role: user
      stream: high_level
      if_present: vqa_query
      content:
        - { type: image, feature: observation.images.top }
        - { type: text, text: "${vqa_query}" }
    - { role: assistant, content: "${vqa}", stream: high_level, target: true, if_present: vqa }
```

Add one such sub-recipe per camera the dataset records.

## Recipe anatomy

Recipes are YAML files backed by `TrainingRecipe` and `MessageTurn`.

```yaml
messages:
  - { role: user, content: "${task}", stream: high_level }
  - { role: assistant, content: "${subtask}", stream: low_level, target: true }
```

Rendered samples use HF-style chat messages plus LeRobot sidecars:

```python
sample["messages"]
sample["message_streams"]
sample["target_message_indices"]
```

The renderer does not apply a tokenizer chat template. Policy processors decide how to serialize the messages for their backbone.

## Blends

Blend recipes select one weighted sub-recipe deterministically from the sample index.
`recipes/subtasks_vqa.yaml` trains the core blend — high-level subtask prediction, low-level execution, and VQA. `recipes/subtask_mem_vqa_speech.yaml` is the fuller variant that also adds memory updates and spoken interjection responses.

## Graceful absence

If both language columns are missing, `None`, or empty, `RenderMessagesStep` is a no-op.
If an event-scoped branch is selected on a frame without the required event row, rendering returns `None`, allowing a loader to retry another sample.
