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0053defa2e
* 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 for more information, see https://pre-commit.ci * 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>
82 lines
2.6 KiB
Python
82 lines
2.6 KiB
Python
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from enum import Enum
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from .config import TeleoperatorConfig
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from .teleoperator import Teleoperator
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class TeleopEvents(Enum):
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"""Shared constants for teleoperator events across teleoperators."""
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SUCCESS = "success"
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FAILURE = "failure"
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RERECORD_EPISODE = "rerecord_episode"
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IS_INTERVENTION = "is_intervention"
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TERMINATE_EPISODE = "terminate_episode"
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def make_teleoperator_from_config(config: TeleoperatorConfig) -> Teleoperator:
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if config.type == "keyboard":
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from .keyboard import KeyboardTeleop
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return KeyboardTeleop(config)
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elif config.type == "koch_leader":
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from .koch_leader import KochLeader
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return KochLeader(config)
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elif config.type == "so100_leader":
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from .so100_leader import SO100Leader
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return SO100Leader(config)
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elif config.type == "so101_leader":
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from .so101_leader import SO101Leader
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return SO101Leader(config)
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elif config.type == "stretch3":
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from .stretch3_gamepad import Stretch3GamePad
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return Stretch3GamePad(config)
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elif config.type == "widowx":
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from .widowx import WidowX
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return WidowX(config)
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elif config.type == "mock_teleop":
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from tests.mocks.mock_teleop import MockTeleop
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return MockTeleop(config)
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elif config.type == "gamepad":
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from .gamepad.teleop_gamepad import GamepadTeleop
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return GamepadTeleop(config)
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elif config.type == "keyboard_ee":
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from .keyboard.teleop_keyboard import KeyboardEndEffectorTeleop
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return KeyboardEndEffectorTeleop(config)
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elif config.type == "homunculus_glove":
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from .homunculus import HomunculusGlove
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return HomunculusGlove(config)
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elif config.type == "homunculus_arm":
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from .homunculus import HomunculusArm
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return HomunculusArm(config)
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elif config.type == "bi_so100_leader":
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from .bi_so100_leader import BiSO100Leader
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return BiSO100Leader(config)
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else:
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raise ValueError(config.type)
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