feat(record): Integrate RobotProcessor into recording loop and update policy handling

- Added support for RobotProcessor in the record_loop function to enhance data processing capabilities.
- Updated the logic to reset both policy and processor when provided, ensuring proper state management.
- Modified action prediction to utilize the processor, improving the overall functionality of the recording process.
- Adjusted the save_checkpoint function to include preprocessor state saving, enhancing checkpointing capabilities.

src/lerobot/utils/control_utils.py

@@ -31,6 +31,7 @@ from termcolor import colored
 from lerobot.datasets.lerobot_dataset import LeRobotDataset
 from lerobot.datasets.utils import DEFAULT_FEATURES
 from lerobot.policies.pretrained import PreTrainedPolicy
+from lerobot.processor import RobotProcessor
 from lerobot.robots import Robot
 
 
@@ -101,6 +102,7 @@ def predict_action(
     observation: dict[str, np.ndarray],
     policy: PreTrainedPolicy,
     device: torch.device,
+    processor: RobotProcessor,
     use_amp: bool,
     task: str | None = None,
     robot_type: str | None = None,
@@ -122,6 +124,8 @@ def predict_action(
         observation["task"] = task if task else ""
         observation["robot_type"] = robot_type if robot_type else ""
 
+        observation = processor(observation)
+
         # Compute the next action with the policy
         # based on the current observation
         action = policy.select_action(observation)