* 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(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.
* [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>
* feat(tokenizer): Introduce TokenizerProcessor for text tokenization
- Added TokenizerProcessor class to handle tokenization of task strings using Hugging Face's AutoTokenizer.
- Supports both string and list inputs, with customizable parameters for task key, output key, and tokenization settings.
- Implemented comprehensive unit tests to validate functionality, including handling of various input scenarios and integration with RobotProcessor.
- Updated types.py to include LANGUAGE feature type and modified __init__.py to register the new processor.
* feat(language): Enhance language processing in TokenizerProcessor
- Added OBS_LANGUAGE constant to define the observation language key.
- Updated TokenizerProcessor to store tokenized task data in the observation dictionary, ensuring compatibility with the new language feature.
- Introduced Pi0NewLineProcessor to append newlines to tasks for proper tokenization.
- Modified tests to validate the integration of language tokens and attention masks in the observation structure.
* feat(tokenizer): Add padding configuration to TokenizerProcessor
- Introduced `padding_side` parameter to the TokenizerProcessor for customizable padding direction.
- Updated the `make_pi0_processor` function to include the new padding configuration.
- Enhanced unit tests to validate the functionality of the `padding_side` parameter in various scenarios.
* feat(processor): Add state management methods to Pi0NewLineProcessor
* feat(normalization): Track normalization and unnormalization info in complementary data
- Updated NormalizerProcessor and UnnormalizerProcessor to accept additional parameters for tracking normalization modes.
- Enhanced the __call__ methods to store normalization and unnormalization information in the complementary data of transitions.
- Added unit tests to verify the correct tracking of normalization info, including scenarios with missing stats and selective normalization keys.
* feat(factory): Add preprocessor and postprocessor overrides to ProcessorConfigKwargs
- Updated ProcessorConfigKwargs to include optional overrides for preprocessor and postprocessor configurations.
- Enhanced the make_processor function to utilize the new overrides, allowing for more flexible processor initialization.
* feat(processors): Integrate RenameProcessor into various processor configurations
- Added RenameProcessor to the input steps of multiple processor functions, including make_act_processor, make_diffusion_processor, make_pi0_processor, make_sac_processor, make_tdmpc_processor, make_vqbet_processor, and make_smolvla_processor.
- Consolidated normalization features from input and output into a single NormalizerProcessor for improved efficiency.
- Updated the input steps to ensure compatibility with the new RenameProcessor integration.
* feat(smolvla): Refactor language processing and introduce new line processor (#1658)
- Removed the prepare_language method and directly accessed language tokens and masks from the batch using the OBS_LANGUAGE constant.
- Added SmolVLANewLineProcessor to ensure tasks end with a newline, enhancing tokenization compatibility.
- Updated the make_smolvla_processor function to include the new line processor and tokenizer processor for improved input handling.
* feture(policies): add device processor (#1659)
* feat(processors): Integrate DeviceProcessor into multiple processor configurations
- Added DeviceProcessor to the input and output steps of various processor functions, including make_act_processor, make_diffusion_processor, make_pi0_processor, make_pi0fast_processor, make_sac_processor, make_tdmpc_processor, make_vqbet_processor, and make_smolvla_processor.
- Enhanced the DeviceProcessor class with state management methods and ensured compatibility with existing processor pipelines.
- Introduced unit tests for DeviceProcessor to validate functionality across different scenarios, including CPU and CUDA operations.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* refactor(pipeline): Remove to() method for device management
- Eliminated the to() method from RobotProcessor, which was responsible for moving tensor states to specified devices.
- Removed associated unit tests that validated the functionality of the to() method across various scenarios.
- Streamlined the pipeline code by focusing on other device management strategies.
* feat(processor): Enhance DeviceProcessor with float dtype conversion
- Added support for optional float dtype conversion in DeviceProcessor, allowing tensors to be converted to specified floating-point types while preserving non-float types.
- Implemented validation for float dtype input and updated the processor's configuration methods to include float dtype.
- Refactored tensor processing logic to streamline device movement and dtype conversion.
- Introduced comprehensive unit tests to validate the new float dtype functionality across various scenarios.
* feat(policies): Add new line processors and update module exports
* feat(processor): Enhance batch and device processors to handle index and task_index fields
- Added logic to ToBatchProcessor for unsqueezing 0D tensors for index and task_index fields, ensuring they are processed as 1D tensors.
- Updated DeviceProcessor to process index and task_index fields in complementary data, preserving their tensor types and ensuring non-tensor fields remain unchanged.
- Enhanced unit tests to validate the correct handling of index and task_index fields across various scenarios, including device compatibility and dtype preservation.
- Enhanced ToBatchProcessor to wrap string tasks in a list, adding batch dimensions for compatibility with model inference.
- Implemented a new method for processing complementary data, ensuring that task values are correctly handled as either strings or lists of strings.
- Added comprehensive unit tests to validate task processing, including edge cases and in-place mutation of complementary data.
- 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.
- Added ToBatchProcessor to ensure observations have proper batch dimensions for model processing.
- Implemented functionality to add batch dimensions to state and image observations as needed.
- Created comprehensive unit tests to validate the processor's behavior with various tensor dimensions and types.
- Ensured compatibility with existing transition keys and maintained the integrity of non-observation data.
Adil Zouitine