refactor(processor): improve processor pipeline typing with generic type (#1810)

* refactor(processor): introduce generic type for to_output

- Always return `TOutput`
- Remove `_prepare_transition`, so `__call__` now always returns `TOutput`
- Update tests accordingly
- This refactor paves the way for adding settings for `to_transition` and `to_output` in `make_processor` and the post-processor

* refactor(processor): consolidate ProcessorKwargs usage across policies

- Removed the ProcessorTypes module and integrated ProcessorKwargs directly into the processor pipeline.
- Updated multiple policy files to utilize the new ProcessorKwargs structure for preprocessor and postprocessor arguments.
- Simplified the handling of processor kwargs by initializing them to empty dictionaries when not provided.

src/lerobot/policies/diffusion/processor_diffusion.py

@@ -21,6 +21,7 @@ from lerobot.policies.diffusion.configuration_diffusion import DiffusionConfig
 from lerobot.processor import (
     DeviceProcessor,
     NormalizerProcessor,
+    ProcessorKwargs,
     RenameProcessor,
     RobotProcessor,
     ToBatchProcessor,
@@ -29,8 +30,16 @@ from lerobot.processor import (
 
 
 def make_diffusion_pre_post_processors(
-    config: DiffusionConfig, dataset_stats: dict[str, dict[str, torch.Tensor]] | None = None
+    config: DiffusionConfig,
+    dataset_stats: dict[str, dict[str, torch.Tensor]] | None = None,
+    preprocessor_kwargs: ProcessorKwargs | None = None,
+    postprocessor_kwargs: ProcessorKwargs | None = None,
 ) -> tuple[RobotProcessor, RobotProcessor]:
+    if preprocessor_kwargs is None:
+        preprocessor_kwargs = {}
+    if postprocessor_kwargs is None:
+        postprocessor_kwargs = {}
+
     input_steps = [
         RenameProcessor(rename_map={}),
         NormalizerProcessor(
@@ -47,6 +56,15 @@ def make_diffusion_pre_post_processors(
             features=config.output_features, norm_map=config.normalization_mapping, stats=dataset_stats
         ),
     ]
-    return RobotProcessor(steps=input_steps, name=PREPROCESSOR_DEFAULT_NAME), RobotProcessor(
-        steps=output_steps, name=POSTPROCESSOR_DEFAULT_NAME
+    return (
+        RobotProcessor(
+            steps=input_steps,
+            name=PREPROCESSOR_DEFAULT_NAME,
+            **preprocessor_kwargs,
+        ),
+        RobotProcessor(
+            steps=output_steps,
+            name=POSTPROCESSOR_DEFAULT_NAME,
+            **postprocessor_kwargs,
+        ),
     )