diff --git a/lerobot/common/datasets/utils.py b/lerobot/common/datasets/utils.py
index a765a7e1..542daf2a 100644
--- a/lerobot/common/datasets/utils.py
+++ b/lerobot/common/datasets/utils.py
@@ -462,7 +462,7 @@ def dataset_to_policy_features(features: dict[str, dict]) -> dict[str, PolicyFea
 
             names = ft["names"]
             # Backward compatibility for "channel" which is an error introduced in LeRobotDataset v2.0 for ported datasets.
-            if names and names[2] in ["channel", "channels"]:  # (h, w, c) -> (c, h, w)
+            if names[2] in ["channel", "channels"]:  # (h, w, c) -> (c, h, w)
                 shape = (shape[2], shape[0], shape[1])
         elif key == "observation.environment_state":
             type = FeatureType.ENV
diff --git a/lerobot/scripts/server/gym_manipulator.py b/lerobot/scripts/server/gym_manipulator.py
index 3836d995..1f3f7820 100644
--- a/lerobot/scripts/server/gym_manipulator.py
+++ b/lerobot/scripts/server/gym_manipulator.py
@@ -1949,6 +1949,10 @@ def record_dataset(env, policy, cfg, success_collection_steps=15):
     # Setup initial action (zero action if using teleop)
     action = env.action_space.sample() * 0.0
 
+    action_names = ["delta_x_ee", "delta_y_ee", "delta_z_ee"]
+    if cfg.wrapper.use_gripper:
+        action_names.append("gripper_delta")
+
     # Configure dataset features based on environment spaces
     features = {
         "observation.state": {
@@ -1958,8 +1962,8 @@ def record_dataset(env, policy, cfg, success_collection_steps=15):
         },
         "action": {
             "dtype": "float32",
-            "shape": env.action_space.shape,
-            "names": None,
+            "shape": (len(action_names),),
+            "names": action_names,
         },
         "next.reward": {"dtype": "float32", "shape": (1,), "names": None},
         "next.done": {"dtype": "bool", "shape": (1,), "names": None},
@@ -1976,7 +1980,7 @@ def record_dataset(env, policy, cfg, success_collection_steps=15):
             features[key] = {
                 "dtype": "video",
                 "shape": env.observation_space[key].shape,
-                "names": None,
+                "names": ["channels", "height", "width"],
             }
 
     # Create dataset