debug fixes

src/lerobot/rollout/context.py

@@ -272,11 +272,17 @@ def build_rollout_context(
     #         )
 
     # --- 4. Features + action-key reconciliation ---------------------
+    # Only `.pos` joint features are used for policy inference — velocity and
+    # torque channels are observation-only and must be excluded from the state
+    # and action tensors that the policy sees.  This matches the filtering
+    # applied by the old ``hil_data_collection`` script.
     all_obs_features = robot.observation_features
     observation_features_hw = {
-        k: v for k, v in all_obs_features.items() if v is float or isinstance(v, tuple)
+        k: v
+        for k, v in all_obs_features.items()
+        if isinstance(v, tuple) or (v is float and k.endswith(".pos"))
     }
-    action_features_hw = robot.action_features
+    action_features_hw = {k: v for k, v in robot.action_features.items() if k.endswith(".pos")}
 
     # The action side is always needed: sync inference reads action names from
     # ``dataset_features[ACTION]`` to map policy tensors back to robot actions.
@@ -293,13 +299,50 @@ def build_rollout_context(
     )
     dataset_features = combine_feature_dicts(action_dataset_features, observation_dataset_features)
     hw_features = hw_to_dataset_features(observation_features_hw, "observation")
-    raw_action_keys = list(robot.action_features.keys())
+    raw_action_keys = list(action_features_hw.keys())
     policy_action_names = getattr(policy_config, "action_feature_names", None)
     ordered_action_keys = _resolve_action_key_order(
         list(policy_action_names) if policy_action_names else None,
         raw_action_keys,
     )
 
+    # --- Diagnostic logging ---
+    _act_ft = dataset_features.get("action", {})
+    _obs_ft = dataset_features.get("observation.state", {})
+    logger.info(
+        "Feature reconciliation: action_dim=%d, obs_state_dim=%d, ordered_action_keys=%d",
+        _act_ft.get("shape", (0,))[0],
+        _obs_ft.get("shape", (0,))[0],
+        len(ordered_action_keys),
+    )
+    logger.info("  action names  : %s", _act_ft.get("names", []))
+    logger.info("  obs state names: %s", _obs_ft.get("names", []))
+    logger.info("  ordered keys   : %s", ordered_action_keys)
+    logger.info(
+        "  policy.action_feature_names: %s",
+        list(policy_action_names) if policy_action_names else "None (using raw_action_keys)",
+    )
+    if full_config.input_features:
+        logger.info("  policy input_features: %s", list(full_config.input_features.keys()))
+    else:
+        logger.warning("  policy input_features is EMPTY — policy may not process images!")
+    if full_config.output_features:
+        for k, v in full_config.output_features.items():
+            logger.info("  policy output_features[%s]: shape=%s", k, v.shape)
+    # Validate action dimension consistency
+    if full_config.output_features:
+        for ft in full_config.output_features.values():
+            policy_action_dim = ft.shape[0]
+            if len(ordered_action_keys) != policy_action_dim:
+                logger.error(
+                    "ACTION DIM MISMATCH: policy expects %d dims, hardware produces %d keys. "
+                    "First 5 keys: %s",
+                    policy_action_dim,
+                    len(ordered_action_keys),
+                    ordered_action_keys[:5],
+                )
+            break
+
     # Validate visual features if no rename_map is active
     rename_map = cfg.dataset.rename_map if cfg.dataset else {}
     if not rename_map:

src/lerobot/rollout/inference/sync.py

@@ -97,10 +97,30 @@ class SyncInferenceEngine(InferenceEngine):
                 observation, self._device, self._task, self._robot_type
             )
             observation = self._preprocessor(observation)
-            action = self._policy.select_action(observation)
-            action = self._postprocessor(action)
+            action_raw = self._policy.select_action(observation)
+            action = self._postprocessor(action_raw)
         action_tensor = action.squeeze(0).cpu()
 
+        if not hasattr(self, "_log_count"):
+            self._log_count = 0
+        if self._log_count < 3:
+            raw_flat = action_raw.squeeze(0).cpu()
+            logger.info(
+                "[Sync tick %d] raw action (first 5): %s | post-processed (first 5): %s",
+                self._log_count,
+                raw_flat[:5].tolist(),
+                action_tensor[:5].tolist(),
+            )
+            obs_state = obs_frame.get("observation.state")
+            if obs_state is not None:
+                logger.info(
+                    "[Sync tick %d] obs_frame['observation.state'] (first 5): %s | shape: %s",
+                    self._log_count,
+                    obs_state[:5].tolist() if hasattr(obs_state, "tolist") else str(obs_state)[:80],
+                    obs_state.shape if hasattr(obs_state, "shape") else "?",
+                )
+            self._log_count += 1
+
         # Reorder to match dataset action ordering so the caller can treat
         # the returned tensor uniformly across backends.
         action_dict = make_robot_action(action_tensor, self._dataset_features)

src/lerobot/rollout/strategies/core.py

@@ -268,5 +268,17 @@ def send_next_action(
 
     action_dict = {k: interp[i].item() for i, k in enumerate(ordered_keys) if i < len(interp)}
     processed = ctx.processors.robot_action_processor((action_dict, obs_raw))
+
+    if not hasattr(send_next_action, "_log_count"):
+        send_next_action._log_count = 0
+    if send_next_action._log_count < 3:
+        sample = {k: round(v, 4) for k, v in list(processed.items())[:5]}
+        logger.info(
+            "[send_next_action tick %d] action sent to robot (first 5): %s",
+            send_next_action._log_count,
+            sample,
+        )
+        send_next_action._log_count += 1
+
     ctx.hardware.robot_wrapper.send_action(processed)
     return action_dict