Refactor processing architecture to use RobotProcessor

- Replaced instances of RobotPipeline with RobotProcessor across the codebase for improved modularity and clarity.
- Introduced ProcessorStepRegistry for better management of processing steps.
- Updated relevant documentation and tests to reflect the new processing structure.
- Enhanced the save/load functionality to support the new processor design.
- Added a model card template for RobotProcessor to facilitate sharing and documentation.

tests/policies/test_policies.py

@@ -40,7 +40,7 @@ from lerobot.policies.factory import (
 from lerobot.policies.normalize import Normalize, Unnormalize
 from lerobot.policies.pretrained import PreTrainedPolicy
 from lerobot.processor.observation_processor import ObservationProcessor
-from lerobot.processor.pipeline import RobotPipeline, TransitionIndex
+from lerobot.processor.pipeline import RobotProcessor, TransitionIndex
 from lerobot.utils.random_utils import seeded_context
 from tests.artifacts.policies.save_policy_to_safetensors import get_policy_stats
 from tests.utils import DEVICE, require_cpu, require_env, require_x86_64_kernel
@@ -186,9 +186,9 @@ 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
-    obs_pipeline = RobotPipeline([ObservationProcessor()])
+    obs_processor = RobotProcessor([ObservationProcessor()])
     transition = (observation, None, None, None, None, None, None)
-    processed_transition = obs_pipeline(transition)
+    processed_transition = obs_processor(transition)
     observation = processed_transition[TransitionIndex.OBSERVATION]
 
     # send observation to device/gpu