diff --git a/cyclo_brain/policy/lerobot/lerobot_engine/prediction.py b/cyclo_brain/policy/lerobot/lerobot_engine/prediction.py
index 6e298f3..9db6547 100644
--- a/cyclo_brain/policy/lerobot/lerobot_engine/prediction.py
+++ b/cyclo_brain/policy/lerobot/lerobot_engine/prediction.py
@@ -21,22 +21,43 @@ logger = logging.getLogger("lerobot_engine")
 class PredictionMixin:
     """Policy input batch -> action chunk."""
 
+    # Policies whose predict_action_chunk cannot be called standalone: it
+    # stacks internal observation queues (self._queues) that ONLY select_action
+    # populates, so calling it directly raises "stack expects a non-empty
+    # TensorList" and the robot never receives an action.
+    #
+    #   DiffusionPolicy.predict_action_chunk:
+    #       batch = {k: torch.stack(list(self._queues[k]), dim=1) for k in batch ...}
+    #   VQBeTPolicy.predict_action_chunk: same, plus a combined OBS_IMAGES key
+    #       that only select_action builds.
+    #
+    # ACT does not use queues (n_obs_steps=1), which is why it works with the
+    # direct call. Route the queue-based ones through select_action instead;
+    # they manage their own action-chunk queue internally.
+    _QUEUE_BASED_POLICIES = {"VQBeTPolicy", "DiffusionPolicy"}
+
     def _predict_chunk(self, batch: Dict[str, torch.Tensor]) -> torch.Tensor:
         """Return a chunk tensor of shape (1, T, A)."""
         assert self._policy is not None
+        if type(self._policy).__name__ in self._QUEUE_BASED_POLICIES:
+            return self._select_action_chunk(batch)
         try:
             action = self._policy.predict_action_chunk(batch)
             if action.dim() == 2:
                 action = action.unsqueeze(1)
             return action
-        except (NotImplementedError, AttributeError):
+        except (NotImplementedError, AttributeError, RuntimeError, AssertionError):
             logger.debug(
-                "predict_action_chunk unavailable; falling back to select_action"
+                "predict_action_chunk unavailable/failed; falling back to select_action"
             )
-            action = self._policy.select_action(batch)
-            if action.dim() == 1:
-                action = action.unsqueeze(0)
-            return action.unsqueeze(1)
+            return self._select_action_chunk(batch)
+
+    def _select_action_chunk(self, batch: Dict[str, torch.Tensor]) -> torch.Tensor:
+        """select_action -> (1, 1, A) chunk."""
+        action = self._policy.select_action(batch)
+        if action.dim() == 1:
+            action = action.unsqueeze(0)
+        return action.unsqueeze(1)
 
     @staticmethod
     def _to_numpy_chunk(action: torch.Tensor) -> np.ndarray: