fix(language): address review — tools accessor, motion docs, conditional collate

* **`meta.tools` actually reads `info.json["tools"]`.** `DatasetInfo`
  had no `tools` field, so `from_dict` silently dropped the key (it
  warned about unknown fields then discarded them) and the property
  always returned `DEFAULT_TOOLS`. Added `tools: list[dict] | None`
  to the dataclass; `to_dict()` drops it when unset so existing
  datasets keep a clean `info.json`. Fixed the accessor to read
  `self.info.tools` (the previous `.get(...)` would have raised
  AttributeError on the dataclass anyway). Added regression tests:
  fallback when absent, round-trip from disk, and round-trip
  through `DatasetInfo.from_dict` / `to_dict`.

* **`motion` is not view-dependent — fix the docs.** The mdx claimed
  rows of style `motion` must carry `camera`, but `VIEW_DEPENDENT_STYLES
  = {"vqa", "trace"}` and the validator agrees: motion primitives are
  joint/Cartesian-frame, not pixel-space. Updated both call-out
  paragraphs in `language_and_recipes.mdx`.

* **Conditional `collate_fn` swap.** Added `meta.has_language_columns`
  and gate the `lerobot_collate_fn` swap in `lerobot_train.py` on it,
  so non-language datasets keep PyTorch's `default_collate`. Also
  added a pass-through test in `test_collate.py` that asserts on a
  plain tensor batch the custom collate matches `default_collate`
  key-for-key, plus a test for the `None`-sample drop path.

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

tests/utils/test_collate.py

@@ -38,3 +38,47 @@ def test_lerobot_collate_preserves_messages_and_drops_raw_language():
     assert out["target_message_indices"] == [[0], [0]]
     assert "language_persistent" not in out
     assert "language_events" not in out
+
+
+def test_lerobot_collate_passes_through_standard_batch():
+    """On a non-language batch, the collate must match ``default_collate``.
+
+    Guards against silent regressions: ``lerobot_train.py`` only opts into
+    ``lerobot_collate_fn`` when the dataset declares language columns, but
+    if a future change ever wires it in unconditionally we want the
+    behavior to remain a transparent pass-through for ordinary tensor
+    batches.
+    """
+    from torch.utils.data._utils.collate import default_collate
+
+    batch = [
+        {
+            "observation.image": torch.zeros(3, 4, 4),
+            "action": torch.tensor([0.0, 1.0]),
+            "index": torch.tensor(0),
+        },
+        {
+            "observation.image": torch.ones(3, 4, 4),
+            "action": torch.tensor([2.0, 3.0]),
+            "index": torch.tensor(1),
+        },
+    ]
+
+    custom = lerobot_collate_fn(batch)
+    expected = default_collate(batch)
+
+    assert custom.keys() == expected.keys()
+    for key in expected:
+        assert torch.equal(custom[key], expected[key]), f"key={key} diverged"
+
+
+def test_lerobot_collate_drops_none_samples():
+    """Recipes that yielded no target message return ``None`` — those samples
+    must be filtered out, and an entirely-``None`` batch must collapse to ``None``.
+    """
+    batch = [None, {"index": torch.tensor(0)}, None]
+    out = lerobot_collate_fn(batch)
+    assert out is not None
+    assert out["index"].tolist() == [0]
+
+    assert lerobot_collate_fn([None, None]) is None