chore(processor): add type alias RobotProcessorPipeline and PolicyProcessorPipeline (#1859)

* feat(processor): introduce PolicyProcessorPipeline and RobotProcessorPipeline as type aliases for DataProcessorPipeline

- Added PolicyProcessorPipeline and RobotProcessorPipeline type aliases to enhance clarity and maintainability in the processor module.
- Updated the __all__ list to include the new pipelines for better module export consistency.

* refactor(processor): replace DataProcessorPipeline with PolicyProcessorPipeline across multiple modules

- Updated all instances of DataProcessorPipeline to PolicyProcessorPipeline in various processor files for consistency and clarity.
- Adjusted function signatures to reflect the new pipeline type, enhancing maintainability and readability.

* refactor(processor): update hotswap_stats function to use PolicyProcessorPipeline

- Changed the parameter name from robot_processor to policy_processor for clarity.
- Ensured consistency with recent updates to the processor module by reflecting the new pipeline type in the function signature.

* refactor(processor): replace DataProcessorPipeline with PolicyProcessorPipeline in migrate_policy_normalization.py

- Updated the preprocessor and postprocessor to use PolicyProcessorPipeline for consistency with recent changes in the processor module.
- Enhanced clarity and maintainability by aligning with the new pipeline structure.

* refactor(processor): update hotswap_stats to use PolicyProcessorPipeline

- Changed the parameter type in hotswap_stats from DataProcessorPipeline to PolicyProcessorPipeline for consistency with recent updates.
- Enhanced clarity by updating the function documentation to reflect the new pipeline type.

* refactor(processor): replace DataProcessorPipeline with RobotProcessorPipeline across multiple files

- Updated instances of DataProcessorPipeline to RobotProcessorPipeline in evaluate.py, record.py, replay.py, teleoperate.py, and other relevant files for consistency and clarity.
- Adjusted function signatures and variable types to reflect the new pipeline structure, enhancing maintainability and readability.

src/lerobot/policies/diffusion/processor_diffusion.py

@@ -20,9 +20,9 @@ from lerobot.constants import POSTPROCESSOR_DEFAULT_NAME, PREPROCESSOR_DEFAULT_N
 from lerobot.policies.diffusion.configuration_diffusion import DiffusionConfig
 from lerobot.processor import (
     AddBatchDimensionProcessorStep,
-    DataProcessorPipeline,
     DeviceProcessorStep,
     NormalizerProcessorStep,
+    PolicyProcessorPipeline,
     ProcessorKwargs,
     RenameProcessorStep,
     UnnormalizerProcessorStep,
@@ -34,7 +34,7 @@ def make_diffusion_pre_post_processors(
     dataset_stats: dict[str, dict[str, torch.Tensor]] | None = None,
     preprocessor_kwargs: ProcessorKwargs | None = None,
     postprocessor_kwargs: ProcessorKwargs | None = None,
-) -> tuple[DataProcessorPipeline, DataProcessorPipeline]:
+) -> tuple[PolicyProcessorPipeline, PolicyProcessorPipeline]:
     if preprocessor_kwargs is None:
         preprocessor_kwargs = {}
     if postprocessor_kwargs is None:
@@ -57,12 +57,12 @@ def make_diffusion_pre_post_processors(
         ),
     ]
     return (
-        DataProcessorPipeline(
+        PolicyProcessorPipeline(
             steps=input_steps,
             name=PREPROCESSOR_DEFAULT_NAME,
             **preprocessor_kwargs,
         ),
-        DataProcessorPipeline(
+        PolicyProcessorPipeline(
             steps=output_steps,
             name=POSTPROCESSOR_DEFAULT_NAME,
             **postprocessor_kwargs,