fix(datasets): normalize shape=(1,) numeric values before HF encoding (#3344)

* fix(datasets): normalize shape=(1,) numeric values before save

* test(datasets): cover shape=(1,) int/bool and finalize

Co-authored-by: Copilot <copilot@github.com>

src/lerobot/datasets/dataset_writer.py

@@ -250,7 +250,14 @@ class DatasetWriter:
         for key, ft in self._meta.features.items():
             if key in ["index", "episode_index", "task_index"] or ft["dtype"] in ["image", "video"]:
                 continue
-            episode_buffer[key] = np.stack(episode_buffer[key])
+            stacked_values = np.stack(episode_buffer[key])
+
+            # `shape=(1,)` numeric features are serialized as `datasets.Value`, which expects scalars.
+            # Normalizing to `(N,)` keeps save semantics stable across dependency versions.
+            if tuple(ft["shape"]) == (1,) and ft["dtype"] != "string":
+                stacked_values = stacked_values.reshape(episode_length)
+
+            episode_buffer[key] = stacked_values
 
         # Wait for image writer to end, so that episode stats over images can be computed
         self._wait_image_writer()

tests/datasets/test_datasets.py

@@ -24,6 +24,7 @@ import torch
 
 pytest.importorskip("datasets", reason="datasets is required (install lerobot[dataset])")
 
+import datasets
 from huggingface_hub import HfApi
 from PIL import Image
 from safetensors.torch import load_file
@@ -360,6 +361,41 @@ def test_add_frame_image_pil(image_dataset):
     assert dataset[0]["image"].shape == torch.Size(DUMMY_CHW)
 
 
+@pytest.mark.parametrize(
+    "dtype,np_dtype,values,assert_fn",
+    [
+        ("float32", np.float32, [1.0, 2.0], np.testing.assert_allclose),
+        ("int64", np.int64, [1, 2], np.testing.assert_array_equal),
+        ("bool", np.bool_, [True, False], np.testing.assert_array_equal),
+    ],
+    ids=["float32", "int64", "bool"],
+)
+def test_save_episode_shape_1_scalar_is_scalarized_before_hf_encoding(
+    tmp_path, empty_lerobot_dataset_factory, monkeypatch, dtype, np_dtype, values, assert_fn
+):
+    features = {"state": {"dtype": dtype, "shape": (1,), "names": None}}
+    dataset = empty_lerobot_dataset_factory(root=tmp_path / "test", features=features)
+    dataset.add_frame({"state": np.array([values[0]], dtype=np_dtype), "task": "Dummy task"})
+    dataset.add_frame({"state": np.array([values[1]], dtype=np_dtype), "task": "Dummy task"})
+
+    captured = {}
+    original_from_dict = datasets.Dataset.from_dict
+
+    def _from_dict_spy(cls, mapping, *args, **kwargs):
+        captured["state"] = mapping["state"]
+        return original_from_dict(mapping, *args, **kwargs)
+
+    monkeypatch.setattr(datasets.Dataset, "from_dict", classmethod(_from_dict_spy))
+
+    dataset.save_episode()
+    dataset.finalize()
+
+    assert "state" in captured
+    assert isinstance(captured["state"], np.ndarray)
+    assert captured["state"].shape == (2,)
+    assert_fn(captured["state"], np.array(values, dtype=np_dtype))
+
+
 def test_set_image_transforms_applies_transparently(image_dataset):
     dataset = image_dataset
     dataset.add_frame({"image": np.random.rand(*DUMMY_CHW), "task": "Dummy task"})