feat(record): add transition features to dataset and handle scalar vs array formatting in converters (#1861)

- Introduced new transition features (`next.reward`, `next.done`, `next.truncated`) in the dataset during recording.
- Updated the `transition_to_dataset_frame` function to handle scalar values correctly, ensuring compatibility with expected array formats for reward, done, and truncated features.

src/lerobot/processor/converters.py

@@ -359,11 +359,26 @@ def transition_to_dataset_frame(
 
     # Add transition metadata
     if tr.get(TransitionKey.REWARD) is not None:
-        batch[REWARD] = _from_tensor(tr[TransitionKey.REWARD])
+        reward_val = _from_tensor(tr[TransitionKey.REWARD])
+        # Check if features expect array format, otherwise keep as scalar
+        if REWARD in features and features[REWARD].get("shape") == (1,):
+            batch[REWARD] = np.array([reward_val], dtype=np.float32)
+        else:
+            batch[REWARD] = reward_val
+
     if tr.get(TransitionKey.DONE) is not None:
-        batch[DONE] = _from_tensor(tr[TransitionKey.DONE])
+        done_val = _from_tensor(tr[TransitionKey.DONE])
+        if DONE in features and features[DONE].get("shape") == (1,):
+            batch[DONE] = np.array([done_val], dtype=bool)
+        else:
+            batch[DONE] = done_val
+
     if tr.get(TransitionKey.TRUNCATED) is not None:
-        batch[TRUNCATED] = _from_tensor(tr[TransitionKey.TRUNCATED])
+        truncated_val = _from_tensor(tr[TransitionKey.TRUNCATED])
+        if TRUNCATED in features and features[TRUNCATED].get("shape") == (1,):
+            batch[TRUNCATED] = np.array([truncated_val], dtype=bool)
+        else:
+            batch[TRUNCATED] = truncated_val
 
     # Complementary data flags and task
     comp = tr.get(TransitionKey.COMPLEMENTARY_DATA) or {}

src/lerobot/record.py

@@ -406,7 +406,15 @@ def record(cfg: RecordConfig) -> LeRobotDataset:
 
     action_features = hw_to_dataset_features(robot.action_features, "action", cfg.dataset.video)
     obs_features = hw_to_dataset_features(robot.observation_features, "observation", cfg.dataset.video)
-    dataset_features = {**action_features, **obs_features}
+
+    # Add next.* features that are generated during recording
+    transition_features = {
+        "next.reward": {"dtype": "float32", "shape": (1,), "names": None},
+        "next.done": {"dtype": "bool", "shape": (1,), "names": None},
+        "next.truncated": {"dtype": "bool", "shape": (1,), "names": None},
+    }
+
+    dataset_features = {**action_features, **obs_features, **transition_features}
 
     if cfg.resume:
         dataset = LeRobotDataset(