- Changed assertions in multiple processor test files to verify the updated names from "robot_preprocessor" and "robot_postprocessor" to "policy_preprocessor" and "policy_postprocessor" for consistency with recent refactoring.
- Updated constant names from PREPROCESSOR_DEFAULT_NAME and POSTPROCESSOR_DEFAULT_NAME to POLICY_PREPROCESSOR_DEFAULT_NAME and POLICY_POSTPROCESSOR_DEFAULT_NAME for better context.
- Adjusted references across multiple files to use the new constant names, ensuring consistency in the codebase.
- Added the HasTeleopEvents protocol to define a standard for teleoperators that provide control events.
- Implemented a runtime check to ensure teleoperators implement the get_teleop_events() method.
- Updated AddTeleopEventsAsInfoStep to utilize the new protocol, enhancing compatibility with custom teleoperators.
- Improved documentation for clarity on teleoperation event extraction and compatibility with built-in teleoperators.
- Removed the scipy dependency from the project to streamline requirements.
- Added a new `rotation.py` module containing a custom `Rotation` class that replicates essential functionalities of `scipy.spatial.transform.Rotation`, allowing for rotation vector, matrix, and quaternion conversions without external dependencies.
- Updated the `robot_kinematic_processor.py` to utilize the new custom rotation utilities.
- Updated the DataProcessorPipeline to require that all steps inherit from ProcessorStep, enhancing type safety and clarity.
- Adjusted tests to utilize a MockTokenizerProcessorStep that adheres to the ProcessorStep interface, ensuring consistent behavior across tests.
- Refactored various mock step classes in tests to inherit from ProcessorStep for improved consistency and maintainability.
- Introduced new transition features (`next.reward`, `next.done`, `next.truncated`) in the dataset during recording.
- Updated the `transition_to_dataset_frame` function to handle scalar values correctly, ensuring compatibility with expected array formats for reward, done, and truncated features.
- Updated the DataProcessorPipeline to require a specific config_filename when loading from Hugging Face Hub, enhancing clarity and preventing errors.
- Simplified local path checks and improved error handling for invalid paths.
- Adjusted tests to reflect the new requirement and ensure proper error handling for various loading scenarios.
* feat(processor): introduce PolicyProcessorPipeline and RobotProcessorPipeline as type aliases for DataProcessorPipeline
- Added PolicyProcessorPipeline and RobotProcessorPipeline type aliases to enhance clarity and maintainability in the processor module.
- Updated the __all__ list to include the new pipelines for better module export consistency.
* refactor(processor): replace DataProcessorPipeline with PolicyProcessorPipeline across multiple modules
- Updated all instances of DataProcessorPipeline to PolicyProcessorPipeline in various processor files for consistency and clarity.
- Adjusted function signatures to reflect the new pipeline type, enhancing maintainability and readability.
* refactor(processor): update hotswap_stats function to use PolicyProcessorPipeline
- Changed the parameter name from robot_processor to policy_processor for clarity.
- Ensured consistency with recent updates to the processor module by reflecting the new pipeline type in the function signature.
* refactor(processor): replace DataProcessorPipeline with PolicyProcessorPipeline in migrate_policy_normalization.py
- Updated the preprocessor and postprocessor to use PolicyProcessorPipeline for consistency with recent changes in the processor module.
- Enhanced clarity and maintainability by aligning with the new pipeline structure.
* refactor(processor): update hotswap_stats to use PolicyProcessorPipeline
- Changed the parameter type in hotswap_stats from DataProcessorPipeline to PolicyProcessorPipeline for consistency with recent updates.
- Enhanced clarity by updating the function documentation to reflect the new pipeline type.
* refactor(processor): replace DataProcessorPipeline with RobotProcessorPipeline across multiple files
- Updated instances of DataProcessorPipeline to RobotProcessorPipeline in evaluate.py, record.py, replay.py, teleoperate.py, and other relevant files for consistency and clarity.
- Adjusted function signatures and variable types to reflect the new pipeline structure, enhancing maintainability and readability.
- Changed the internal device variable from `_device` to `tensor_device` for improved readability and consistency.
- Updated references throughout the class to reflect the new variable name.
- Changed the internal tokenizer variable name from `_tokenizer` to `input_tokenizer` for improved readability and consistency.
- Updated references throughout the class to reflect the new variable name.
* refactor(processor): rename MapDeltaActionToRobotAction and MapTensorToDeltaActionDict for consistency
* refactor(processor): rename DeviceProcessor to DeviceProcessorStep for consistency across modules
* refactor(processor): rename Torch2NumpyActionProcessor to Torch2NumpyActionProcessorStep for consistency
* refactor(processor): rename Numpy2TorchActionProcessor to Numpy2TorchActionProcessorStep for consistency
* refactor(processor): rename AddTeleopActionAsComplimentaryData to AddTeleopActionAsComplimentaryDataStep for consistency
* refactor(processor): rename ImageCropResizeProcessor and AddTeleopEventsAsInfo for consistency
* refactor(processor): rename TimeLimitProcessor to TimeLimitProcessorStep for consistency
* refactor(processor): rename GripperPenaltyProcessor to GripperPenaltyProcessorStep for consistency
* refactor(processor): rename InterventionActionProcessor to InterventionActionProcessorStep for consistency
* refactor(processor): rename RewardClassifierProcessor to RewardClassifierProcessorStep for consistency
* refactor(processor): rename JointVelocityProcessor to JointVelocityProcessorStep for consistency
* refactor(processor): rename MotorCurrentProcessor to MotorCurrentProcessorStep for consistency
* refactor(processor): rename NormalizerProcessor and UnnormalizerProcessor to NormalizerProcessorStep and UnnormalizerProcessorStep for consistency
* refactor(processor): rename VanillaObservationProcessor to VanillaObservationProcessorStep for consistency
* refactor(processor): rename RenameProcessor to RenameProcessorStep for consistency
* refactor(processor): rename TokenizerProcessor to TokenizerProcessorStep for consistency
* refactor(processor): rename ToBatchProcessor to AddBatchDimensionProcessorStep for consistency
* refactor(processor): update config file name in test for RenameProcessorStep consistency
* refactor(processors): enhance transform_features method across multiple processors
- Updated the transform_features method in various processors to utilize a copy of the features dictionary, ensuring immutability of the original features.
- Added handling for new feature keys and removed obsolete ones in the MapTensorToDeltaActionDict, JointVelocityProcessor, and others.
- Improved readability and maintainability by following consistent patterns in feature transformation.
* refactor(processors): standardize action and observation keys in delta_action_processor and joint_observations_processor
- Updated action and observation keys to use constants for improved readability and maintainability.
- Refactored the transform_features method in multiple processors to ensure consistent handling of feature keys.
- Enhanced error handling by raising exceptions for missing required components in action and observation processing.
- Removed obsolete code and improved overall structure for better clarity.
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* refactor(processors): remove unused import in joint_observations_processor
* refactor(processors): simplify transform_features method in delta_action_processor
* refactor(processors): streamline transform_features method in ImageCropResizeProcessor
* refactor(processors): improve error handling and streamline transform_features method in phone_processor
- Raised a ValueError for missing position and rotation in action to enhance error handling.
* refactor(processors): enhance error handling in JointVelocityProcessor
- Added a ValueError raise for missing current joint positions in the observation method to improve error handling and ensure the integrity of the transform_features method.
* refactor(processors): simplify transform_features method in robot kinematic processors
* refactor(processors): standardize action keys in phone_processor
* fix(processor): RKP feature obs -> act
---------
Signed-off-by: Adil Zouitine <adilzouitinegm@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Steven Palma <steven.palma@huggingface.co>
* refactor(converters): move batch transition functions to converters module
- Moved `_default_batch_to_transition` and `_default_transition_to_batch` functions from `pipeline.py` to `converters.py` for better organization and separation of concerns.
- Updated references in `RobotProcessor` to use the new location of these functions.
- Added tests to ensure correct functionality of the transition functions, including handling of index and task_index fields.
- Removed redundant tests from `pipeline.py` to streamline the test suite.
* refactor(processor): reorganize EnvTransition and TransitionKey definitions
- Moved `EnvTransition` and `TransitionKey` classes from `pipeline.py` to a new `core.py` module for better structure and maintainability.
- Updated import statements across relevant modules to reflect the new location of these definitions, ensuring consistent access throughout the codebase.
* refactor(converters): rename and update dataset frame conversion functions
- Replaced `to_dataset_frame` with `transition_to_dataset_frame` for clarity and consistency in naming.
- Updated references in `record.py`, `pipeline.py`, and tests to use the new function name.
- Introduced `merge_transitions` to streamline the merging of transitions, enhancing readability and maintainability.
- Adjusted related tests to ensure correct functionality with the new naming conventions.
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* fix(processor): solve conflict artefacts
* refactor(converters): remove unused identity function and update type hints for merge_transitions
* refactor(processor): remove unused identity import and clean up gym_manipulator.py
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Steven Palma <steven.palma@huggingface.co>
- Introduced `to_tensor` function using `singledispatch` to handle various input types, including scalars, arrays, and dictionaries, converting them to PyTorch tensors.
- Replaced previous tensor conversion logic in `gym_action_processor`, `normalize_processor`, and `test_converters` with the new `to_tensor` function for improved readability and maintainability.
- Updated tests to cover new functionality and ensure correct tensor conversion behavior.
Co-authored-by: AdilZouitine <adilzouitinegm@gmail.com>
- Introduced `to_tensor` function using `singledispatch` to handle various input types, including scalars, arrays, and dictionaries, converting them to PyTorch tensors.
- Replaced previous tensor conversion logic in `gym_action_processor`, `normalize_processor`, and `test_converters` with the new `to_tensor` function for improved readability and maintainability.
- Updated tests to cover new functionality and ensure correct tensor conversion behavior.
* 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.
- Added new constants for truncated and done states in constants.py.
- Updated references to action and observation keys in pipeline_features.py, converters.py, hil_processor.py, tokenizer_processor.py, and robot_kinematic_processor.py to use the new constants for improved readability and maintainability.
* [Port codebase pipeline] General fixes for RL and scripts (#1748)
* Refactor dataset configuration in documentation and codebase
- Updated dataset configuration keys from `dataset_root` to `root` and `num_episodes` to `num_episodes_to_record` for consistency.
- Adjusted replay episode handling by renaming `episode` to `replay_episode`.
- Enhanced documentation
- added specific processor to transform from policy actions to delta actions
* Added Robot action to tensor processor
Added new processor script for dealing with gym specific action processing
* removed RobotAction2Tensor processor; imrpoved choosing observations in actor
* nit in delta action
* added missing reset functions to kinematics
* Adapt teleoperate and replay to pipeline similar to record
* refactor(processors): move to inheritance (#1750)
* fix(teleoperator): improvements phone implementation (#1752)
* fix(teleoperator): protect shared state in phone implementation
* refactor(teleop): separate classes in phone
* fix: solve breaking changes (#1753)
* refactor(policies): multiple improvements (#1754)
* refactor(processor): simpler logic in device processor (#1755)
* refactor(processor): euclidean distance in delta action processor (#1757)
* refactor(processor): improvements to joint observations processor migration (#1758)
* refactor(processor): improvements to tokenizer migration (#1759)
* refactor(processor): improvements to tokenizer migration
* fix(tests): tokenizer tests regression from #1750
* fix(processors): fix float comparison and config in hil processors (#1760)
* chore(teleop): remove unnecessary callbacks in KeyboardEndEffectorTeleop (#1761)
* refactor(processor): improvements normalize pipeline migration (#1756)
* refactor(processor): several improvements normalize processor step
* refactor(processor): more improvements normalize processor
* refactor(processor): more changes to normalizer
* refactor(processor): take a different approach to DRY
* refactor(processor): final design
* chore(record): revert comment and continue deleted (#1764)
* refactor(examples): pipeline phone examples (#1769)
* refactor(examples): phone teleop + teleop script
* refactor(examples): phone replay + replay
* chore(examples): rename phone example files & folders
* feat(processor): fix improvements to the pipeline porting (#1796)
* refactor(processor): enhance tensor device handling in normalization process (#1795)
* refactor(tests): remove unsupported device detection test for complementary data (#1797)
* chore(tests): update ToBatchProcessor test (#1798)
* refactor(tests): remove in-place mutation tests for actions and complementary data in batch processor
* test(tests): add tests for action and task processing in batch processor
* add names for android and ios phone (#1799)
* use _tensor_stats in normalize processor (#1800)
* fix(normalize_processor): correct device reference for tensor epsilon handling (#1801)
* add point 5 add missing feature contracts (#1806)
* Fix PR comments 1452 (#1807)
* use key to determine image
* Address rest of PR comments
* use PolicyFeatures in transform_features
---------
Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com>
---------
Co-authored-by: Michel Aractingi <michel.aractingi@huggingface.co>
Co-authored-by: Adil Zouitine <adilzouitinegm@gmail.com>
Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com>
The 'episode_data' parameter was previously ignored, causing an error if provided. This change ensures it is correctly used, which allows for asynchronous episode saving by passing a copy of the episode buffer, preventing conflicts with the main data collection loop.
- Introduced `PREPROCESSOR_DEFAULT_NAME` and `POSTPROCESSOR_DEFAULT_NAME` constants for consistent naming across various processor implementations.
- Updated processor creation in multiple policy files to utilize these constants, enhancing code readability and maintainability.
- Modified the training script to load and save the preprocessor and postprocessor using the new constants.
* 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
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* 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>
- Introduced new test files for ACT, Classifier, Diffusion, PI0, SAC, SmolVLA, TDMPC, and VQBeT policy processors.
- Each test file includes unit tests to validate functionality, including handling of batch sizes, device management, and data type conversions.
- Enhanced test coverage to ensure robustness and reliability of processor implementations across different scenarios.
- Improved the _process_tensor method to preserve GPU placement for tensors already on a GPU, facilitating multi-GPU training scenarios.
- Introduced a new _detect_device method in TokenizerProcessor to ensure tokenized tensors match the device of existing tensors in transitions.
- Added comprehensive unit tests to validate the functionality of device detection and float dtype conversion across various scenarios.
- Removed commented-out code related to loading pretrained processors in the make_processor function.
- This change enhances code clarity and maintains focus on the current implementation.
- Deleted the Normalize and Unnormalize classes from the normalization module to streamline the codebase.
- Updated tests to ensure compatibility with the removal of these classes, focusing on the new NormalizerProcessor and UnnormalizerProcessor implementations.
- Enhanced the handling of normalization statistics and improved overall code clarity.
- Updated TokenizerProcessor to conditionally import AutoTokenizer based on the availability of the transformers library, enhancing flexibility.
- Modified tokenizer attribute type to Any to accommodate scenarios where transformers may not be installed.
- Improved observation handling by using a more concise approach to manage the transition dictionary, ensuring compatibility with existing data structures.
- Added error handling for missing transformers library, providing clear guidance for users on installation requirements.
Adil Zouitine