fix(rollout): device and task propagation + feature pos + warn fps + move rename_map config

src/lerobot/configs/dataset.py

@@ -14,7 +14,7 @@
 
 """Shared dataset recording configuration used by both ``lerobot-record`` and ``lerobot-rollout``."""
 
-from dataclasses import dataclass, field
+from dataclasses import dataclass
 from datetime import datetime
 from pathlib import Path
 
@@ -68,8 +68,6 @@ class DatasetRecordConfig:
     # Number of threads per encoder instance. None = auto (codec default).
     # Lower values reduce CPU usage, maps to 'lp' (via svtav1-params) for libsvtav1 and 'threads' for h264/hevc..
     encoder_threads: int | None = None
-    # Rename map for the observation to override the image and state keys
-    rename_map: dict[str, str] = field(default_factory=dict)
 
     def __post_init__(self) -> None:
         if self.repo_id:

src/lerobot/rollout/configs.py

@@ -26,6 +26,7 @@ from lerobot.configs import PreTrainedConfig, parser
 from lerobot.configs.dataset import DatasetRecordConfig
 from lerobot.robots.config import RobotConfig
 from lerobot.teleoperators.config import TeleoperatorConfig
+from lerobot.utils.device_utils import auto_select_torch_device, is_torch_device_available
 
 from .inference import InferenceEngineConfig, SyncInferenceConfig
 
@@ -205,6 +206,8 @@ class RolloutConfig:
     # Use vocal synthesis to read events
     play_sounds: bool = True
     resume: bool = False
+    # Rename map for mapping robot/dataset observation keys to policy keys
+    rename_map: dict[str, str] = field(default_factory=dict)
 
     # Torch compile
     use_torch_compile: bool = False
@@ -285,6 +288,31 @@ class RolloutConfig:
         if self.policy is None:
             raise ValueError("--policy.path is required for rollout")
 
+        # --- Task resolution ---
+        # When --dataset.rename_map (or any --dataset.* flag) is passed, draccus
+        # creates a DatasetRecordConfig with single_task="".  If the user set
+        # the task via the top-level --task flag, propagate it so that all
+        # downstream consumers (inference engine, dataset frame builders) see it.
+        if self.dataset is not None and not self.dataset.single_task and self.task:
+            logger.info("Propagating top-level task '%s' to dataset config", self.task)
+            self.dataset.single_task = self.task
+        elif self.dataset is not None and self.dataset.single_task and not self.task:
+            logger.info("Propagating dataset single_task '%s' to top-level task", self.dataset.single_task)
+            self.task = self.dataset.single_task
+
+        # --- Device resolution ---
+        # Resolve device from the policy config when not explicitly set so all
+        # components (policy.to, preprocessor, inference engine) use the same
+        # device string instead of inconsistent fallbacks.
+        if self.device is None or not is_torch_device_available(self.device):
+            resolved = self.policy.device
+            if resolved:
+                self.device = resolved
+                logger.info("Resolved device from policy config: %s", self.device)
+            else:
+                self.device = auto_select_torch_device().type
+                logger.info("No policy config to resolve device from; auto-selected device: %s", self.device)
+
     @classmethod
     def __get_path_fields__(cls) -> list[str]:
         return ["policy"]

src/lerobot/rollout/context.py

@@ -272,11 +272,16 @@ def build_rollout_context(
     #         )
 
     # --- 4. Features + action-key reconciliation ---------------------
+    # TODO(Steven): 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.
     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.
@@ -293,7 +298,7 @@ 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,
@@ -301,7 +306,7 @@ def build_rollout_context(
     )
 
     # Validate visual features if no rename_map is active
-    rename_map = cfg.dataset.rename_map if cfg.dataset else {}
+    rename_map = cfg.rename_map
     if not rename_map:
         expected_visuals = {k for k, v in full_config.input_features.items() if v.type == FeatureType.VISUAL}
         provided_visuals = {
@@ -366,7 +371,7 @@ def build_rollout_context(
     if dataset is not None:
         dataset_stats = rename_stats(
             dataset.meta.stats,
-            cfg.dataset.rename_map if cfg.dataset else {},
+            cfg.rename_map,
         )
 
     preprocessor, postprocessor = make_pre_post_processors(
@@ -374,8 +379,8 @@ def build_rollout_context(
         pretrained_path=cfg.policy.pretrained_path,
         dataset_stats=dataset_stats,
         preprocessor_overrides={
-            "device_processor": {"device": cfg.device or getattr(policy_config, "device", "cpu")},
-            "rename_observations_processor": {"rename_map": cfg.dataset.rename_map if cfg.dataset else {}},
+            "device_processor": {"device": cfg.device},
+            "rename_observations_processor": {"rename_map": cfg.rename_map},
         },
     )
 

src/lerobot/rollout/strategies/base.py

@@ -72,6 +72,10 @@ class BaseStrategy(RolloutStrategy):
             dt = time.perf_counter() - loop_start
             if (sleep_t := control_interval - dt) > 0:
                 precise_sleep(sleep_t)
+            else:
+                logging.warning(
+                    f"Record loop is running slower ({1 / dt:.1f} Hz) than the target FPS ({cfg.fps} Hz). Dataset frames might be dropped and robot control might be unstable. Common causes are: 1) Camera FPS not keeping up 2) Policy inference taking too long 3) CPU starvation"
+                )
 
     def teardown(self, ctx: RolloutContext) -> None:
         """Disconnect hardware and stop inference."""

src/lerobot/rollout/strategies/core.py

@@ -62,6 +62,7 @@ class RolloutStrategy(abc.ABC):
         self._interpolator = ActionInterpolator(multiplier=ctx.runtime.cfg.interpolation_multiplier)
         self._engine = ctx.policy.inference
         logger.info("Starting inference engine...")
+        self._engine.reset()
         self._engine.start()
         self._warmup_flushed = False
         logger.info("Inference engine started")

src/lerobot/rollout/strategies/dagger.py

@@ -508,6 +508,10 @@ class DAggerStrategy(RolloutStrategy):
                     dt = time.perf_counter() - loop_start
                     if (sleep_t := control_interval - dt) > 0:
                         precise_sleep(sleep_t)
+                    else:
+                        logging.warning(
+                            f"Record loop is running slower ({1 / dt:.1f} Hz) than the target FPS ({cfg.fps} Hz). Dataset frames might be dropped and robot control might be unstable. Common causes are: 1) Camera FPS not keeping up 2) Policy inference taking too long 3) CPU starvation"
+                        )
 
             finally:
                 logger.info("DAgger continuous control loop ended — pausing engine")
@@ -648,6 +652,10 @@ class DAggerStrategy(RolloutStrategy):
                     dt = time.perf_counter() - loop_start
                     if (sleep_t := control_interval - dt) > 0:
                         precise_sleep(sleep_t)
+                    else:
+                        logging.warning(
+                            f"Record loop is running slower ({1 / dt:.1f} Hz) than the target FPS ({cfg.fps} Hz). Dataset frames might be dropped and robot control might be unstable. Common causes are: 1) Camera FPS not keeping up 2) Policy inference taking too long 3) CPU starvation"
+                        )
 
             finally:
                 logger.info("DAgger corrections-only loop ended — pausing engine")

src/lerobot/rollout/strategies/highlight.py

@@ -189,6 +189,10 @@ class HighlightStrategy(RolloutStrategy):
                     dt = time.perf_counter() - loop_start
                     if (sleep_t := control_interval - dt) > 0:
                         precise_sleep(sleep_t)
+                    else:
+                        logging.warning(
+                            f"Record loop is running slower ({1 / dt:.1f} Hz) than the target FPS ({cfg.fps} Hz). Dataset frames might be dropped and robot control might be unstable. Common causes are: 1) Camera FPS not keeping up 2) Policy inference taking too long 3) CPU starvation"
+                        )
 
             finally:
                 logger.info("Highlight control loop ended")

src/lerobot/rollout/strategies/sentry.py

@@ -160,6 +160,10 @@ class SentryStrategy(RolloutStrategy):
                     dt = time.perf_counter() - loop_start
                     if (sleep_t := control_interval - dt) > 0:
                         precise_sleep(sleep_t)
+                    else:
+                        logging.warning(
+                            f"Record loop is running slower ({1 / dt:.1f} Hz) than the target FPS ({cfg.fps} Hz). Dataset frames might be dropped and robot control might be unstable. Common causes are: 1) Camera FPS not keeping up 2) Policy inference taking too long 3) CPU starvation"
+                        )
 
             finally:
                 logger.info("Sentry control loop ended — saving final episode")