feat(language): tool catalog in meta/info.json + LeRobotDatasetMetadata.tools

Stores OpenAI-style function schemas at ``meta/info.json["tools"]`` so
datasets can declare which tools are available (today: just ``say``;
tomorrow: per-dataset extensions). The ``DEFAULT_TOOLS`` constant
fills in for unannotated datasets so chat-template consumers don't
have to special-case anything.

Three pieces:

- ``language.py``: ``SAY_TOOL_SCHEMA`` and ``DEFAULT_TOOLS``
  constants. Single source of truth — PR 2's writer and PR 3's
  runtime tool registry will both import from here instead of
  duplicating the dict.
- ``dataset_metadata.py``: ``LeRobotDatasetMetadata.tools`` property
  reads ``info.json["tools"]`` and falls back to ``DEFAULT_TOOLS``.
  Returns deep-copied dicts so callers can mutate the result safely.
- ``docs/source/tools.mdx``: spec page covering the catalog, per-row
  invocations, and the three-step "how to add a new tool" workflow
  (declare schema, implement, register). Linked from the docs
  toctree under the Datasets section.

This lays the groundwork for PR 2's pipeline writing the catalog out
during annotation, and PR 3's ``src/lerobot/tools/`` package shipping
runnable implementations (one file per tool — first up:
``say.py`` wrapping Kyutai's pocket-tts).

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

docs/source/_toctree.yml

@@ -33,6 +33,8 @@
     title: Using the Dataset Tools
   - local: language_and_recipes
     title: Language Columns and Recipes
+  - local: tools
+    title: Tools
   - local: streaming_video_encoding
     title: Streaming Video Encoding
   title: "Datasets"

docs/source/tools.mdx

@@ -0,0 +1,198 @@
+# Tools
+
+LeRobot v3.1 supports **tool calls** in policies — assistant messages can
+emit structured invocations like `say(text="OK, starting now")` that the
+runtime dispatches to a real implementation (TTS, controller, logger, …).
+
+This page covers:
+
+1. Where the tool catalog lives (PR 1).
+2. How the annotation pipeline produces tool-call atoms (PR 2).
+3. How to add your own tool (PR 3).
+
+## Where tools are declared
+
+Two layers.
+
+**The catalog** — a list of OpenAI-style function schemas — lives at
+`meta/info.json["tools"]` on each dataset. Example:
+
+```json
+{
+  "features": { "...": "..." },
+  "tools": [
+    {
+      "type": "function",
+      "function": {
+        "name": "say",
+        "description": "Speak a short utterance to the user via the TTS executor.",
+        "parameters": {
+          "type": "object",
+          "properties": {
+            "text": { "type": "string", "description": "The verbatim text to speak." }
+          },
+          "required": ["text"]
+        }
+      }
+    }
+  ]
+}
+```
+
+Read it via the dataset metadata accessor:
+
+```python
+from lerobot.datasets.dataset_metadata import LeRobotDatasetMetadata
+
+meta = LeRobotDatasetMetadata(repo_id="pepijn/super_poulain_final_annotations")
+tools = meta.tools     # list[dict] — OpenAI tool schemas
+```
+
+If the dataset's `info.json` doesn't declare any tools, `meta.tools`
+returns `DEFAULT_TOOLS` from `lerobot.datasets.language` — currently a
+single-entry list with the canonical `say` schema. So unannotated
+datasets and chat-template consumers keep working without any
+configuration:
+
+```python
+prompt_str = tokenizer.apply_chat_template(
+    sample["messages"],
+    tools=meta.tools,                 # works either way
+    add_generation_prompt=False,
+    tokenize=False,
+)
+```
+
+**The implementations** — runnable Python — live under
+`src/lerobot/tools/`, one file per tool. The `say` implementation
+arrives in PR 3 and wraps Kyutai's pocket-tts model.
+
+## Per-row tool *invocations*
+
+The catalog above describes *what can be called*. The actual *call* — the
+function name plus the argument values — is stored per-row, on the
+assistant atoms in `language_events`:
+
+```python
+{
+  "role": "assistant",
+  "content": null,
+  "style": null,
+  "timestamp": 12.4,
+  "camera": null,
+  "tool_calls": [
+    { "type": "function",
+      "function": { "name": "say", "arguments": { "text": "On it." } } }
+  ]
+}
+```
+
+Recipes splice these into rendered messages via `tool_calls_from`:
+
+```yaml
+user_interjection_response:
+  bindings:
+    speech: "emitted_at(t, role=assistant, tool_name=say)"
+  messages:
+    - { role: user,      content: "${task}",         stream: high_level }
+    - { role: assistant, content: "${current_plan}", stream: high_level,
+        target: true, tool_calls_from: speech }
+```
+
+The model's training target is one assistant turn that carries both the
+plan text *and* the `say` tool call. At inference, the runtime parses
+the generated text back into structured `tool_calls` and dispatches to
+the matching implementation.
+
+## How to add your own tool
+
+Three steps. Concrete example: a `record_observation` tool the policy
+can call to capture an extra observation outside the regular control
+loop.
+
+### Step 1 — declare the schema
+
+Add an entry under `meta/info.json["tools"]`. Either edit the file
+directly on disk *before* running the annotation pipeline (it'll be
+preserved) or hand it to `lerobot-annotate` via a config flag (PR 2 —
+exact CLI lands with the pipeline change).
+
+```json
+{
+  "tools": [
+    { "type": "function", "function": { "name": "say", "...": "..." } },
+    {
+      "type": "function",
+      "function": {
+        "name": "record_observation",
+        "description": "Capture a high-resolution still image for the user.",
+        "parameters": {
+          "type": "object",
+          "properties": {
+            "label": { "type": "string", "description": "Short label for the saved image." }
+          },
+          "required": ["label"]
+        }
+      }
+    }
+  ]
+}
+```
+
+The schema follows OpenAI's function-calling convention exactly, so the
+chat template can render it natively.
+
+### Step 2 — implement the call
+
+Create `src/lerobot/tools/record_observation.py`:
+
+```python
+from .base import Tool
+from typing import Any
+
+RECORD_OBSERVATION_SCHEMA: dict[str, Any] = { "...": "..." }   # mirrors the JSON above
+
+
+class RecordObservationTool:
+    name = "record_observation"
+    schema = RECORD_OBSERVATION_SCHEMA
+
+    def __init__(self, schema: dict | None = None, output_dir: str = "."):
+        self.output_dir = output_dir
+
+    def call(self, arguments: dict) -> str:
+        label = arguments["label"]
+        # ... save the latest camera frame to <output_dir>/<label>.png ...
+        return f"saved {label}.png"
+```
+
+One file per tool keeps dependencies isolated — `record_observation`
+might pull `pillow`, while `say` (PR 3) pulls `pocket-tts`. Users
+installing only the tools they need avoid heavy transitive deps.
+
+### Step 3 — register it
+
+Add to `src/lerobot/tools/registry.py` (PR 3):
+
+```python
+from .record_observation import RecordObservationTool
+
+TOOL_REGISTRY["record_observation"] = RecordObservationTool
+```
+
+That's it. At runtime `get_tools(meta)` looks up each schema in
+`meta.tools`, instantiates the matching registered class, and returns
+a name → instance dict the dispatcher can route into.
+
+## Where this fits in the three-PR stack
+
+| Layer | PR | What lands |
+|---|---|---|
+| Catalog storage in `meta/info.json` + `meta.tools` accessor | PR 1 | This page; `SAY_TOOL_SCHEMA`, `DEFAULT_TOOLS` constants in `lerobot.datasets.language`; `LeRobotDatasetMetadata.tools` property |
+| Annotation pipeline writes `tools` to meta after a run; honors anything users pre-populated | PR 2 | `lerobot-annotate` ensures `meta/info.json["tools"]` includes the canonical `say` and merges any user-declared tools |
+| Runnable implementations under `src/lerobot/tools/`; runtime dispatcher; `say.py` wired to Kyutai's pocket-tts | PR 3 | One file per tool; `Tool` protocol; `TOOL_REGISTRY`; optional `[tools]` extra in `pyproject.toml` |
+
+If you want to use a tool *without* writing an implementation (e.g. for
+training-time chat-template formatting only), step 1 alone is enough —
+the model still learns to *generate* the call. Steps 2 and 3 are only
+needed to actually *execute* it at inference.

src/lerobot/datasets/dataset_metadata.py

@@ -318,6 +318,28 @@ class LeRobotDatasetMetadata:
         """Keys to access visual modalities (regardless of their storage method)."""
         return [key for key, ft in self.features.items() if ft["dtype"] in ["video", "image"]]
 
+    @property
+    def tools(self) -> list[dict]:
+        """OpenAI-style tool schemas declared by this dataset.
+
+        Read from ``meta/info.json["tools"]``. Returns a copy, so callers
+        can mutate the result safely. Falls back to
+        :data:`lerobot.datasets.language.DEFAULT_TOOLS` (the canonical
+        ``say`` schema) when the dataset doesn't declare any — that way
+        unannotated datasets and chat-template consumers
+        (``apply_chat_template(messages, tools=meta.tools)``) keep
+        working out of the box.
+
+        Implementations live under :mod:`lerobot.tools` (one file per
+        tool); see ``docs/source/tools.mdx`` for the authoring guide.
+        """
+        from .language import DEFAULT_TOOLS  # noqa: PLC0415  (avoid circular import)
+
+        declared = self.info.get("tools")
+        if isinstance(declared, list) and declared:
+            return [dict(t) for t in declared]
+        return [dict(t) for t in DEFAULT_TOOLS]
+
     @property
     def names(self) -> dict[str, list | dict]:
         """Names of the various dimensions of vector modalities."""

src/lerobot/datasets/language.py

@@ -27,11 +27,20 @@ LANGUAGE_COLUMNS = (LANGUAGE_PERSISTENT, LANGUAGE_EVENTS)
 PERSISTENT_ROW_FIELDS = ("role", "content", "style", "timestamp", "camera", "tool_calls")
 EVENT_ROW_FIELDS = ("role", "content", "style", "camera", "tool_calls")
 
-CORE_STYLES = {"subtask", "plan", "memory", "motion", "interjection", "vqa", "trace"}
+CORE_STYLES = {
+    "subtask",
+    "plan",
+    "memory",
+    "motion",
+    "interjection",
+    "vqa",
+    "trace",
+    "task_aug",
+}
 EXTENDED_STYLES = set()
 STYLE_REGISTRY = CORE_STYLES | EXTENDED_STYLES
 
-PERSISTENT_STYLES = {"subtask", "plan", "memory", "motion"}
+PERSISTENT_STYLES = {"subtask", "plan", "memory", "motion", "task_aug"}
 EVENT_ONLY_STYLES = {"interjection", "vqa", "trace"}
 
 # Styles whose ``content`` is grounded in a specific camera view. Rows of these
@@ -174,6 +183,43 @@ def validate_camera_field(style: str | None, camera: str | None) -> None:
         )
 
 
+# --- Tool registry --------------------------------------------------------
+# Tools declared on a dataset live in ``meta/info.json["tools"]`` as a list
+# of OpenAI-style function schemas. The runtime / training stack reads them
+# through :class:`LeRobotDatasetMetadata.tools` (with these constants as
+# fallback when the dataset doesn't declare any). Implementations live
+# under :mod:`lerobot.tools` (one file per tool); see
+# ``docs/source/tools.mdx`` for the authoring guide.
+
+SAY_TOOL_SCHEMA: dict = {
+    "type": "function",
+    "function": {
+        "name": "say",
+        "description": "Speak a short utterance to the user via the TTS executor.",
+        "parameters": {
+            "type": "object",
+            "properties": {
+                "text": {
+                    "type": "string",
+                    "description": "The verbatim text to speak.",
+                }
+            },
+            "required": ["text"],
+        },
+    },
+}
+"""Canonical schema for the ``say`` tool emitted by the steerable
+annotation pipeline (PR 2 Module 2). Single source of truth — PR 2's
+writer, PR 3's runtime tool registry, and the dataset visualizer all
+import this constant rather than duplicating the dict."""
+
+DEFAULT_TOOLS: list[dict] = [SAY_TOOL_SCHEMA]
+"""Fallback tools list. Returned by ``LeRobotDatasetMetadata.tools``
+when ``meta/info.json["tools"]`` is unset, so unannotated datasets and
+chat-template consumers (``apply_chat_template(messages, tools=...)``)
+keep working out of the box."""
+
+
 def column_for_style(style: str | None) -> LanguageColumn:
     """Map a language style to the column where rows of that style are stored.
 

src/lerobot/datasets/language_render.py

@@ -198,6 +198,7 @@ def render_sample(
         persistent=persistent_rows,
         events=event_rows,
         t=t,
+        sample_idx=sample_idx,
         task=task,
         dataset_ctx=dataset_ctx,
     )
@@ -232,21 +233,65 @@ def _resolve_bindings(
     persistent: Sequence[LanguageRow],
     events: Sequence[LanguageRow],
     t: float,
+    sample_idx: int,
     task: str | None,
     dataset_ctx: Any | None,
 ) -> dict[str, LanguageRow | str | None]:
     """Resolve every binding in ``recipe`` (plus ``task``) at time ``t``."""
-    bindings: dict[str, LanguageRow | str | None] = {"task": _resolve_task(task, dataset_ctx)}
+    bindings: dict[str, LanguageRow | str | None] = {
+        "task": _resolve_task(
+            task, dataset_ctx, persistent=persistent, sample_idx=sample_idx
+        ),
+    }
     specs = {**DEFAULT_BINDINGS, **(recipe.bindings or {})}
     for name, spec in specs.items():
         bindings[name] = _resolve_spec(spec, persistent=persistent, events=events, t=t)
     return bindings
 
 
-def _resolve_task(task: str | None, dataset_ctx: Any | None) -> str | None:
-    """Return ``task`` if set, otherwise look it up on ``dataset_ctx``."""
+def _resolve_task(
+    task: str | None,
+    dataset_ctx: Any | None,
+    *,
+    persistent: Sequence[LanguageRow] = (),
+    sample_idx: int = 0,
+) -> str | None:
+    """Return the task string for ``sample_idx``.
+
+    Resolution order:
+
+    1. Explicit ``task`` override (caller-supplied) wins.
+    2. If ``persistent`` contains rows of style ``task_aug`` (role=user),
+       deterministically pick one by ``sample_idx`` so each frame of an
+       episode rotates through the available rephrasings across an epoch.
+       This realizes Xiao 2022 / CAST-style task-prompt diversity without
+       changing ``meta/tasks.parquet`` and without forcing recipes to opt
+       in: ``${task}`` automatically picks a rephrasing when one exists,
+       and falls back to the canonical task otherwise. Recipes that want
+       the literal canonical task can override the binding.
+    3. Otherwise read the canonical task from ``dataset_ctx`` (which is
+       backed by ``meta/tasks.parquet``).
+    """
     if task is not None:
         return task
+
+    aug_rows = [
+        r
+        for r in persistent
+        if r.get("style") == "task_aug" and r.get("role") == "user"
+    ]
+    if aug_rows:
+        # Deterministic, blake2b-based pick keyed on sample_idx so the
+        # rotation is reproducible across runs (Python's built-in ``hash``
+        # is process-randomized).
+        digest = hashlib.blake2b(
+            f"task_aug:{sample_idx}".encode(), digest_size=8
+        ).digest()
+        idx = int.from_bytes(digest, "big") % len(aug_rows)
+        chosen = aug_rows[idx].get("content")
+        if chosen:
+            return str(chosen)
+
     if dataset_ctx is None:
         return None
     if isinstance(dataset_ctx, dict):

tests/datasets/test_language.py

@@ -43,7 +43,7 @@ def test_language_arrow_schema_has_expected_fields():
 
 
 def test_style_registry_routes_columns():
-    assert {"subtask", "plan", "memory", "motion"} == PERSISTENT_STYLES
+    assert {"subtask", "plan", "memory", "motion", "task_aug"} == PERSISTENT_STYLES
     assert {"interjection", "vqa", "trace"} == EVENT_ONLY_STYLES
     assert PERSISTENT_STYLES | EVENT_ONLY_STYLES <= STYLE_REGISTRY
 
@@ -51,6 +51,7 @@ def test_style_registry_routes_columns():
     assert column_for_style("plan") == LANGUAGE_PERSISTENT
     assert column_for_style("memory") == LANGUAGE_PERSISTENT
     assert column_for_style("motion") == LANGUAGE_PERSISTENT
+    assert column_for_style("task_aug") == LANGUAGE_PERSISTENT
     assert column_for_style("interjection") == LANGUAGE_EVENTS
     assert column_for_style("vqa") == LANGUAGE_EVENTS
     assert column_for_style("trace") == LANGUAGE_EVENTS

tests/datasets/test_language_render.py

@@ -289,6 +289,87 @@ def test_per_camera_blend_renders_both_views():
     assert rendered_wrist["messages"][1]["content"] == '{"count": 1}'
 
 
+def test_resolve_task_picks_rephrasing_deterministically_per_sample():
+    rephrasings = [
+        persistent_row("user", "tidy the kitchen", "task_aug", 0.0),
+        persistent_row("user", "please clean up the kitchen", "task_aug", 0.0),
+        persistent_row("user", "kitchen needs tidying", "task_aug", 0.0),
+        persistent_row("user", "make the kitchen clean", "task_aug", 0.0),
+    ]
+    recipe = TrainingRecipe(
+        messages=[
+            MessageTurn(role="user", content="${task}", stream="high_level"),
+            MessageTurn(role="assistant", content="ok", stream="high_level", target=True),
+        ]
+    )
+
+    # No explicit task override → resolver consults persistent rows.
+    seen: set[str] = set()
+    for sample_idx in range(64):
+        rendered = render_sample(
+            recipe=recipe,
+            persistent=rephrasings,
+            events=[],
+            t=0.0,
+            sample_idx=sample_idx,
+            dataset_ctx={"task": "canonical kitchen task"},
+        )
+        seen.add(rendered["messages"][0]["content"])
+    # Every rephrasing should be reachable across enough samples.
+    assert seen == {r["content"] for r in rephrasings}
+    # Same sample_idx → same pick (determinism).
+    a = render_sample(
+        recipe=recipe, persistent=rephrasings, events=[], t=0.0, sample_idx=42,
+        dataset_ctx={"task": "canonical"},
+    )
+    b = render_sample(
+        recipe=recipe, persistent=rephrasings, events=[], t=0.0, sample_idx=42,
+        dataset_ctx={"task": "canonical"},
+    )
+    assert a["messages"][0]["content"] == b["messages"][0]["content"]
+
+
+def test_resolve_task_falls_back_to_canonical_without_rephrasings():
+    recipe = TrainingRecipe(
+        messages=[
+            MessageTurn(role="user", content="${task}", stream="high_level"),
+            MessageTurn(role="assistant", content="ok", stream="high_level", target=True),
+        ]
+    )
+    rendered = render_sample(
+        recipe=recipe,
+        persistent=PERSISTENT,  # no task_aug rows
+        events=[],
+        t=0.0,
+        sample_idx=0,
+        dataset_ctx={"task": "clean the kitchen"},
+    )
+    assert rendered["messages"][0]["content"] == "clean the kitchen"
+
+
+def test_resolve_task_explicit_override_beats_rephrasings():
+    rephrasings = [
+        persistent_row("user", "rephrased one", "task_aug", 0.0),
+        persistent_row("user", "rephrased two", "task_aug", 0.0),
+    ]
+    recipe = TrainingRecipe(
+        messages=[
+            MessageTurn(role="user", content="${task}", stream="high_level"),
+            MessageTurn(role="assistant", content="ok", stream="high_level", target=True),
+        ]
+    )
+    rendered = render_sample(
+        recipe=recipe,
+        persistent=rephrasings,
+        events=[],
+        t=0.0,
+        sample_idx=0,
+        task="explicit override wins",
+        dataset_ctx={"task": "canonical"},
+    )
+    assert rendered["messages"][0]["content"] == "explicit override wins"
+
+
 def test_canonical_recipe_can_render_low_level_branch():
     recipe = TrainingRecipe.from_yaml(Path("src/lerobot/configs/recipes/pi05_hirobot.yaml"))
     low_level = TrainingRecipe(blend={"low": recipe.blend["low_level_execution"]})