diff --git a/src/lerobot/scripts/lerobot_eval.py b/src/lerobot/scripts/lerobot_eval.py
index aff733c34..3121d9123 100644
--- a/src/lerobot/scripts/lerobot_eval.py
+++ b/src/lerobot/scripts/lerobot_eval.py
@@ -185,27 +185,28 @@ def rollout(
         observation[f"observation.images.image2"] = observation[f"observation.images.image2"] * 255
         # TODO: works with SyncVectorEnv but not AsyncVectorEnv
         observation = add_envs_task(env, observation)
-        inputs = processor([observation[f"observation.images.image"], observation[f"observation.images.image2"]], observation["task"])
+        # inputs = processor([observation[f"observation.images.image"], observation[f"observation.images.image2"]], observation["task"])
         observation = preprocessor(observation)
-        inputs_1 = policy._build_model_inputs(observation)
-        for k in inputs.keys() & inputs_1.keys():   # intersection of keys
-            a = inputs[k].to("cuda")
-            b = inputs_1[k].to("cuda")
+        observation["domain_id"] = torch.tensor([int(3)], dtype=torch.long).to("cuda")
+        # inputs_1 = policy._build_model_inputs(observation)
+        # for k in inputs.keys() & inputs_1.keys():   # intersection of keys
+        #     a = inputs[k].to("cuda")
+        #     b = inputs_1[k].to("cuda")
 
-            print(f"\n🔎 Key: {k}")
+        #     print(f"\n🔎 Key: {k}")
 
-            # Check shape
-            print("  shape:", a.shape, b.shape)
+        #     # Check shape
+        #     print("  shape:", a.shape, b.shape)
 
-            # Check if close
-            if torch.allclose(a, b, atol=1e-5, rtol=1e-5):
-                print("  ✔️ tensors are equal (allclose)")
-            else:
-                diff = torch.abs(a - b)
-                print("  ❌ tensors differ")
-                print("  max diff:", diff.max().item())
-                print("  mean diff:", diff.mean().item())
-        breakpoint()
+        #     # Check if close
+        #     if torch.allclose(a, b, atol=1e-5, rtol=1e-5):
+        #         print("  ✔️ tensors are equal (allclose)")
+        #     else:
+        #         diff = torch.abs(a - b)
+        #         print("  ❌ tensors differ")
+        #         print("  max diff:", diff.max().item())
+        #         print("  mean diff:", diff.mean().item())
+        # breakpoint()
         with torch.inference_mode():
             action = policy.select_action(observation).to("cpu").numpy()
             # if len(action_queue) == 0:
@@ -230,7 +231,6 @@ def rollout(
         # target_axis_1 = Rotate6D_to_AxisAngle(action_1[:, 3:9])
         # target_act_1 = action_1[:, 9:10]
         # action_numpy_1 = np.concatenate([target_eef_1, target_axis_1, target_act_1], axis=-1)
-        breakpoint()
 
         # Convert to CPU / numpy.
         # action_numpy: np.ndarray = action.to("cpu").numpy()