chore(dataset): add check dataset shape

Declaring a numeric feature with `shape=(0,)` crashed `save_episode()` in two
distinct places, leaving `dtype: "string"` as the only (type-lossy) workaround:

  - `compute_episode_stats` -> `RunningQuantileStats.update` reshaped a size-0
    array, raising "ValueError: cannot reshape array of size 0 into shape (0)".
  - `get_hf_features_from_features` mapped it to a fixed-size Arrow list of
    length 0, which pyarrow rejects ("list_size needs to be a strict positive
    integer").

The issue only reported the first error; the second surfaces once the first is
fixed. This change handles both:

  - Skip zero-width features during episode stats, exactly as string/language
    features are already skipped.
  - Store 1-D zero-width features as a variable-length sequence (length=-1) so
    each per-frame value is simply an empty list.

Adds a unit test (stats layer) and an integration test that records, saves, and
reads back a zero-width feature, asserting it round-trips as an empty vector and
is excluded from stats.

Fixes #3654

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

src/lerobot/datasets/compute_stats.py

@@ -519,6 +519,13 @@ def compute_episode_stats(
         if features[key]["dtype"] in {"string", "language"}:
             continue
 
+        # Features with a zero-width dimension contain no statistics-bearing
+        # values. Skip them like strings instead of letting
+        # get_feature_stats -> RunningQuantileStats.update reshape a size-0 array,
+        # which raises "ValueError: cannot reshape array of size 0".
+        if any(dim == 0 for dim in features[key].get("shape", ())):
+            continue
+
         if features[key]["dtype"] in ["image", "video"]:
             ep_ft_array = sample_images(data)
             axes_to_reduce = (0, 2, 3)

src/lerobot/datasets/feature_utils.py

@@ -64,12 +64,20 @@ def get_hf_features_from_features(features: dict) -> datasets.Features:
             continue
         elif ft["dtype"] == "image":
             hf_features[key] = datasets.Image()
+        elif len(ft["shape"]) > 1 and any(dim == 0 for dim in ft["shape"]):
+            raise ValueError(
+                f"Multidimensional features with a zero-width dimension are not supported: "
+                f"'{key}' has shape {ft['shape']}. Only the one-dimensional shape (0,) is supported."
+            )
         elif ft["shape"] == (1,):
             hf_features[key] = datasets.Value(dtype=ft["dtype"])
         elif len(ft["shape"]) == 1:
-            hf_features[key] = datasets.Sequence(
-                length=ft["shape"][0], feature=datasets.Value(dtype=ft["dtype"])
-            )
+            # A zero-width feature (shape=(0,)) has no fixed-size Arrow representation:
+            # pyarrow rejects a fixed-size list of length 0 ("list_size needs to be a
+            # strict positive integer"). Store it as a variable-length sequence
+            # (length=-1) so each per-frame value is simply an empty list.
+            seq_length = ft["shape"][0] if ft["shape"][0] > 0 else -1
+            hf_features[key] = datasets.Sequence(length=seq_length, feature=datasets.Value(dtype=ft["dtype"]))
         elif len(ft["shape"]) == 2:
             hf_features[key] = datasets.Array2D(shape=ft["shape"], dtype=ft["dtype"])
         elif len(ft["shape"]) == 3:

tests/datasets/test_compute_stats.py

@@ -687,6 +687,26 @@ def test_compute_episode_stats_string_features_skipped():
     assert "q01" in stats["action"]
 
 
+@pytest.mark.parametrize("shape", [(0,), (0, 2), (2, 0), (1, 0, 2)])
+def test_compute_episode_stats_zero_width_feature_skipped(shape):
+    """Features with any zero-width dimension carry no values and are skipped."""
+    episode_data = {
+        "action": np.random.normal(0, 1, (100, 5)).astype(np.float32),
+        "target": np.zeros((100, *shape), dtype=np.float32),
+    }
+    features = {
+        "action": {"dtype": "float32", "shape": (5,)},
+        "target": {"dtype": "float32", "shape": shape},
+    }
+
+    stats = compute_episode_stats(episode_data, features)
+
+    # Zero-width features are skipped, just like strings; non-empty features are unaffected.
+    assert "target" not in stats
+    assert "action" in stats
+    assert "q01" in stats["action"]
+
+
 def test_aggregate_feature_stats_with_quantiles():
     """Test aggregating feature stats that include quantiles."""
     stats_ft_list = [

tests/datasets/test_dataset_writer.py

@@ -27,6 +27,7 @@ pytest.importorskip("datasets", reason="datasets is required (install lerobot[da
 
 from lerobot.configs import VideoEncoderConfig
 from lerobot.datasets.dataset_writer import _encode_video_worker
+from lerobot.datasets.feature_utils import get_hf_features_from_features
 from lerobot.datasets.lerobot_dataset import LeRobotDataset
 from lerobot.datasets.utils import DEFAULT_IMAGE_PATH
 from tests.fixtures.constants import DEFAULT_FPS, DUMMY_REPO_ID
@@ -189,6 +190,36 @@ def test_save_multiple_episodes(tmp_path):
     assert dataset.meta.total_frames == total_frames
 
 
+def test_save_episode_with_zero_width_feature(tmp_path):
+    """A one-dimensional empty numeric feature round-trips and has no statistics."""
+    features = {
+        **SIMPLE_FEATURES,
+        "target": {"dtype": "float32", "shape": (0,), "names": None},
+    }
+    root = tmp_path / "ds"
+    dataset = LeRobotDataset.create(repo_id=DUMMY_REPO_ID, fps=DEFAULT_FPS, features=features, root=root)
+    for _ in range(4):
+        dataset.add_frame(_make_frame(features))
+    dataset.save_episode()
+    dataset.finalize()
+
+    assert dataset.meta.total_episodes == 1
+    assert dataset.meta.total_frames == 4
+
+    reloaded = LeRobotDataset(repo_id=DUMMY_REPO_ID, root=root)
+    target = np.asarray(reloaded[0]["target"])
+    assert target.shape == (0,)
+    assert "target" not in (reloaded.meta.stats or {})
+
+
+@pytest.mark.parametrize("shape", [(0, 2), (2, 0), (1, 0, 2)])
+def test_multidimensional_zero_width_feature_rejected(shape):
+    features = {"target": {"dtype": "float32", "shape": shape, "names": None}}
+
+    with pytest.raises(ValueError, match="Multidimensional features with a zero-width dimension"):
+        get_hf_features_from_features(features)
+
+
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