Refactor observation preprocessing to use a modular pipeline system

- Introduced `RobotPipeline` and `ObservationProcessor` for handling observation transformations.
- Updated `preprocess_observation` to maintain backward compatibility while leveraging the new pipeline.
- Added tests for the new processing components and ensured they match the original functionality.
- Removed hardcoded logic in favor of a more flexible, composable architecture.

tests/policies/test_policies.py

@@ -30,7 +30,8 @@ from lerobot.configs.types import FeatureType, NormalizationMode, PolicyFeature
 from lerobot.datasets.factory import make_dataset
 from lerobot.datasets.utils import cycle, dataset_to_policy_features
 from lerobot.envs.factory import make_env, make_env_config
-from lerobot.envs.utils import preprocess_observation
+from lerobot.processor.pipeline import RobotPipeline, TransitionIndex
+from lerobot.processor.observation_processor import ObservationProcessor
 from lerobot.optim.factory import make_optimizer_and_scheduler
 from lerobot.policies.act.modeling_act import ACTTemporalEnsembler
 from lerobot.policies.factory import (
@@ -185,7 +186,10 @@ def test_policy(ds_repo_id, env_name, env_kwargs, policy_name, policy_kwargs):
     observation, _ = env.reset(seed=train_cfg.seed)
 
     # apply transform to normalize the observations
-    observation = preprocess_observation(observation)
+    obs_pipeline = RobotPipeline([ObservationProcessor()])
+    transition = (observation, None, None, None, None, None, None)
+    processed_transition = obs_pipeline(transition)
+    observation = processed_transition[TransitionIndex.OBSERVATION]
 
     # send observation to device/gpu
     observation = {key: observation[key].to(DEVICE, non_blocking=True) for key in observation}