chore(dataset): add check dataset shape

src/lerobot/datasets/compute_stats.py

@@ -519,8 +519,8 @@ def compute_episode_stats(
         if features[key]["dtype"] in {"string", "language"}:
             continue
 
-        # Features with a zero-width dimension (e.g. shape=(0,)) carry no
-        # statistics-bearing values. Skip them like strings instead of letting
+        # 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", ())):

src/lerobot/datasets/feature_utils.py

@@ -64,6 +64,11 @@ 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:

tests/datasets/test_compute_stats.py

@@ -687,21 +687,16 @@ def test_compute_episode_stats_string_features_skipped():
     assert "q01" in stats["action"]
 
 
-def test_compute_episode_stats_zero_width_feature_skipped():
-    """Features with a zero-width dimension carry no values and must be skipped, not crash.
-
-    Regression test for https://github.com/huggingface/lerobot/issues/3654:
-    a feature declared with shape=(0,) previously reached RunningQuantileStats.update,
-    where ``batch.reshape(-1, batch.shape[-1])`` raised
-    "ValueError: cannot reshape array of size 0 into shape (0)".
-    """
+@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, 0), dtype=np.float32),  # zero-width feature
+        "target": np.zeros((100, *shape), dtype=np.float32),
     }
     features = {
         "action": {"dtype": "float32", "shape": (5,)},
-        "target": {"dtype": "float32", "shape": (0,)},
+        "target": {"dtype": "float32", "shape": shape},
     }
 
     stats = compute_episode_stats(episode_data, features)

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
@@ -190,12 +191,7 @@ def test_save_multiple_episodes(tmp_path):
 
 
 def test_save_episode_with_zero_width_feature(tmp_path):
-    """save_episode() succeeds when a feature has a zero-width dimension (shape=(0,)).
-
-    Regression test for https://github.com/huggingface/lerobot/issues/3654: such a
-    feature previously crashed stats computation with
-    "ValueError: cannot reshape array of size 0 into shape (0)".
-    """
+    """A one-dimensional empty numeric feature round-trips and has no statistics."""
     features = {
         **SIMPLE_FEATURES,
         "target": {"dtype": "float32", "shape": (0,), "names": None},
@@ -204,19 +200,26 @@ def test_save_episode_with_zero_width_feature(tmp_path):
     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()  # previously raised ValueError on the zero-width 'target' feature
+    dataset.save_episode()
     dataset.finalize()
 
     assert dataset.meta.total_episodes == 1
     assert dataset.meta.total_frames == 4
 
-    # The zero-width feature round-trips back as an empty vector and is excluded from stats.
     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)
+
+
 # ── clear / lifecycle ────────────────────────────────────────────────