* 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
- Introduced RenameProcessor in the preprocessor to handle renaming features.
- Combined input and output features in a single NormalizerProcessor for improved efficiency.
- Updated RobotProcessor initialization to clarify step naming for preprocessor and postprocessor.
- Added DeviceProcessor to both preprocessor and postprocessor for better device management.
- Updated load_model_from_hub to return the train configuration alongside the model state_dict and config.
- Modified main function to handle the additional train configuration when loading models from both the hub and local paths.
- Adjusted dataset_repo_id extraction to utilize the train configuration for improved accuracy.
- Updated ToBatchProcessor to add batch dimensions to actions in addition to observations.
- Implemented separate methods for processing observations and actions, improving code readability.
- Added comprehensive unit tests to validate action batching functionality across various tensor dimensions and types.
- Implemented functionality to generate and save a model card for the migrated model, including metadata such as dataset repository ID, license, and tags.
- Enhanced the script to push the model card to the hub if requested, improving model documentation and accessibility.
- Refactored the saving process to ensure the model card is saved locally and uploaded correctly when pushing to the hub.
- Updated the migration script to generate both a preprocessor and a postprocessor, improving the handling of normalization for training and inference.
- Added functionality to convert features to PolicyFeature objects, ensuring compatibility with the new processor architecture.
- Refined the extraction and removal of normalization statistics and layers, streamlining the migration process.
- Improved error handling for missing mandatory configuration fields during model instantiation.
