Refactorgym_manipulator.py using the universal pipeline (#1650)

* Migrate gym_manipulator to use the pipeline
Added get_teleop_events function to capture relevant events from teleop devices unrelated to actions

* Added the capability to record a dataset

* Added the replay functionality with the pipeline

* Refactored `actor.py` to use the pipeline

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* RL works at this commit - fixed actor.py and bugs in gym_manipulator

* change folder structure to reduce the size of gym_manip

* Refactored hilserl config

* Remove dataset and mode from HilSerlEnvConfig to a GymManipulatorConfig to reduce verbose of configs during training

* format docs

* removed get_teleop_events from abc

* Refactor environment configuration and processing pipeline for GymHIL support. Removed device attribute from HILSerlRobotEnvConfig, added DummyTeleopDevice for simulation, and updated processor creation to accommodate GymHIL environments.

* Improved typing for HILRobotEnv config and GymManipulator config

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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* Migrated `gym_manipulator` to use a more modular structure similar to phone teleop

* Refactor gripper handling and transition processing in HIL and robot kinematic processors

- Updated gripper position handling to use a consistent key format across processors
- Improved the EEReferenceAndDelta class to handle reference joint positions.
- Added support for discrete gripper actions in the GripperVelocityToJoint processor.
- Refactored the gym manipulator to improve modularity and clarity in processing steps.

* Added delta_action_processor mapping wrapper

* Added missing file delta_action_processor and improved imports in `gym_manipulator`

* nit

* Added missing file joint_observation_processor

* Enhance processing architecture with new teleoperation processors

- Introduced `AddTeleopActionAsComplimentaryData` and `AddTeleopEventsAsInfo` for integrating teleoperator actions and events into transitions.
- Added `Torch2NumpyActionProcessor` and `Numpy2TorchActionProcessor` for seamless conversion between PyTorch tensors and NumPy arrays.
- Updated `__init__.py` to include new processors in module exports, improving modularity and clarity in the processing pipeline.
- GymHIL is now fully supported with HIL using the pipeline

* Refactor configuration structure for gym_hil integration

- Renamed sections for better readability, such as changing "Gym Wrappers Configuration" to "Processor Configuration."
- Enhanced documentation with clear examples for dataset collection and policy evaluation configurations.

* Enhance reset configuration and teleoperation event handling

- Added `terminate_on_success` parameter to `ResetConfig` and `InterventionActionProcessor` for controlling episode termination behavior upon success detection.
- Updated documentation to clarify the impact of `terminate_on_success` on data collection for reward classifier training.
- Refactored teleoperation event handling to use `TeleopEvents` constants for improved readability and maintainability across various modules.

* fix(keyboard teleop), delta action keys

* Added transform features and feature contract

* Added transform features for image crop

* Enum for TeleopEvents

* Update tranform_features delta action proc

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

src/lerobot/processor/__init__.py

@@ -15,7 +15,20 @@
 # limitations under the License.
 
 from .batch_processor import ToBatchProcessor
+from .delta_action_processor import MapDeltaActionToRobotAction
 from .device_processor import DeviceProcessor
+from .hil_processor import (
+    AddTeleopActionAsComplimentaryData,
+    AddTeleopEventsAsInfo,
+    GripperPenaltyProcessor,
+    ImageCropResizeProcessor,
+    InterventionActionProcessor,
+    Numpy2TorchActionProcessor,
+    RewardClassifierProcessor,
+    TimeLimitProcessor,
+    Torch2NumpyActionProcessor,
+)
+from .joint_observations_processor import JointVelocityProcessor, MotorCurrentProcessor
 from .normalize_processor import NormalizerProcessor, UnnormalizerProcessor, hotswap_stats
 from .observation_processor import VanillaObservationProcessor
 from .pipeline import (
@@ -37,11 +50,20 @@ from .tokenizer_processor import TokenizerProcessor
 
 __all__ = [
     "ActionProcessor",
+    "AddTeleopActionAsComplimentaryData",
+    "AddTeleopEventsAsInfo",
     "DeviceProcessor",
     "DoneProcessor",
+    "MapDeltaActionToRobotAction",
     "EnvTransition",
+    "GripperPenaltyProcessor",
     "IdentityProcessor",
+    "ImageCropResizeProcessor",
     "InfoProcessor",
+    "InterventionActionProcessor",
+    "JointVelocityProcessor",
+    "MapDeltaActionToRobotAction",
+    "MotorCurrentProcessor",
     "NormalizerProcessor",
     "UnnormalizerProcessor",
     "hotswap_stats",
@@ -49,10 +71,14 @@ __all__ = [
     "ProcessorStep",
     "ProcessorStepRegistry",
     "RenameProcessor",
+    "RewardClassifierProcessor",
     "RewardProcessor",
     "RobotProcessor",
     "ToBatchProcessor",
     "TokenizerProcessor",
+    "TimeLimitProcessor",
+    "Numpy2TorchActionProcessor",
+    "Torch2NumpyActionProcessor",
     "TransitionKey",
     "TruncatedProcessor",
     "VanillaObservationProcessor",