feat(batch_processor): Enhance ToBatchProcessor to handle action batching

- Updated ToBatchProcessor to add batch dimensions to actions in addition to observations.
- Implemented separate methods for processing observations and actions, improving code readability.
- Added comprehensive unit tests to validate action batching functionality across various tensor dimensions and types.

src/lerobot/processor/migrate_policy_normalization.py

@@ -46,6 +46,7 @@ from huggingface_hub import hf_hub_download
 from safetensors.torch import load_file as load_safetensors
 
 from lerobot.configs.types import FeatureType, NormalizationMode, PolicyFeature
+from lerobot.processor.batch_processor import ToBatchProcessor
 from lerobot.processor.normalize_processor import NormalizerProcessor, UnnormalizerProcessor
 from lerobot.processor.pipeline import RobotProcessor
 
@@ -403,14 +404,16 @@ def main():
     preprocessor_steps = [
         NormalizerProcessor(features=input_features, norm_map=norm_map, stats=stats),
         NormalizerProcessor(features=output_features, norm_map=norm_map, stats=stats),
+        ToBatchProcessor(),
     ]
-    preprocessor = RobotProcessor(preprocessor_steps, name=f"{policy_type}_preprocessor")
+    preprocessor = RobotProcessor(preprocessor_steps, name="preprocessor")
 
     # Create postprocessor with unnormalizer for outputs only
     postprocessor_steps = [
         UnnormalizerProcessor(features=output_features, norm_map=norm_map, stats=stats),
+        ToBatchProcessor(),
     ]
-    postprocessor = RobotProcessor(postprocessor_steps, name=f"{policy_type}_postprocessor")
+    postprocessor = RobotProcessor(postprocessor_steps, name="postprocessor")
 
     # Determine hub repo ID if pushing to hub
     if args.push_to_hub: