- Modified tests to assert that the processor raises appropriate exceptions when the task key is missing or has an invalid value in the complementary data.
- Ensured that the tests cover cases for None, integer, and mixed list task values, improving robustness against invalid inputs.
* feat(processor): enhance normalization handling and state management
- Added support for additional normalization modes including IDENTITY.
- Introduced a new function `clean_state_dict` to remove specific substrings from state dict keys.
- Implemented preservation of explicitly provided normalization statistics during state loading.
- Updated training script to conditionally provide dataset statistics based on resume state.
- Expanded tests to verify the correct behavior of stats override preservation and loading.
* fix(train): remove redundant comment regarding state loading
- Removed a comment that noted the preprocessor and postprocessor state is already loaded when resuming training, as it was deemed unnecessary for clarity.
* add libero
* backup
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---------
Signed-off-by: Jade Choghari <chogharijade@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Jade Choghari (jchoghar) <chogharijade@gmai.com>
Co-authored-by: Adil Zouitine <adilzouitinegm@gmail.com>
- Streamlined the introduction to clarify the challenges of debugging complex processor pipelines.
- Expanded the section on hooks, detailing their purpose and implementation for runtime monitoring.
- Introduced step-by-step debugging techniques, emphasizing the use of the `step_through()` method for inspecting intermediate states.
- Added examples of feature validation to ensure data structure contracts are met.
- Consolidated best practices for debugging, highlighting the synergy between hooks, step-through debugging, and feature validation.
- Updated the tutorial to use `NormalizerProcessorStep` as the primary example, clarifying its role in normalizing observations and actions.
- Improved explanations of the need for custom processors, emphasizing data compatibility and processing requirements.
- Added code snippets demonstrating the normalization process and the configuration of processor pipelines.
- Enhanced the introduction to processors, detailing their function as translators between raw robot data and model inputs.
- Included examples of real-world processor configurations for both training and inference scenarios.
* chore(processor): update input output of main 3 processors for better semantics
* refactor(processor): replace Any with RobotObservation for improved type safety in processors
* fix(processors): no PolicyObservation
* chore(processor): update with RobotObservation
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---------
Co-authored-by: AdilZouitine <adilzouitinegm@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
- Removed the log_shapes and measure_performance hooks, simplifying the monitoring process to focus on NaN checks.
- Updated performance reporting to include maximum processing times alongside average times for better insights.
- Clarified documentation regarding the processing pipeline and feature transformations.
- Updated DataProcessorPipeline to use HubMixin instead of ModelHubMixin for improved functionality.
- Refactored save_pretrained method to handle saving
- Introduced a new documentation entry for debugging processor pipelines, enhancing the existing guide on processors.
- This addition aims to provide users with insights and best practices for troubleshooting and optimizing their processor workflows.
* chore(docs): initialize doc
* Added script for the second part of the processor doc
* precommit style nit
* improved part 2 of processor guide
* Add comprehensive documentation for processors in robotics
- Introduced a detailed guide on processors, covering their role in transforming raw robot data into model-ready inputs and vice versa.
- Explained core concepts such as EnvTransition, ProcessorStep, and RobotProcessor, along with their functionalities.
- Included examples of common processor steps like normalization, device management, batch processing, and text tokenization.
- Provided insights on building complete pipelines, integrating processors into training loops, and saving/loading configurations.
- Emphasized best practices and advanced features for effective usage of processors in robotics applications.
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* feat(docs): Enhance introduction to processors with additional converter functions
- Updated the introduction to processors documentation to include default batch-to-transition and transition-to-batch converters.
- Added detailed descriptions and examples for new specialized converter functions: `to_transition_teleop_action`, `to_transition_robot_observation`, `to_output_robot_action`, and `to_dataset_frame`.
- Improved clarity on how these converters facilitate integration with existing robotics applications.
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* Improved doc implement_your_own_pipeline
- Use normalization processor as default example
- Add section on transform features
- Add section on overrides.
* Add phone docs and use pipeline for robots/teleop docs
* Fix typo in documentation for adapters in robots/teleop section
* Enhance documentation for processors with detailed explanations and examples
- Updated the introduction to processors, clarifying the role of `EnvTransition` and `ProcessorStep`.
- Introduced `DataProcessorPipeline` as a generic orchestrator for chaining processor steps.
- Added comprehensive descriptions of new converter functions and their applications.
- Improved clarity on type safety and the differences between `RobotProcessorPipeline` and `PolicyProcessorPipeline`.
- Included examples for various processing scenarios, emphasizing best practices for data handling in robotics.
* Enhance documentation for processor migration and debugging
- Added detailed sections on the migration of models to the new `PolicyProcessorPipeline` system, including breaking changes and migration scripts.
- Introduced a comprehensive guide for debugging processor pipelines, covering common issues, step-by-step inspection, and runtime monitoring techniques.
- Updated examples to reflect new usage patterns and best practices for processor implementation and error handling.
- Clarified the role of various processor steps and their configurations in the context of robotics applications.
---------
Co-authored-by: Michel Aractingi <michel.aractingi@huggingface.co>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Pepijn <pepijn@huggingface.co>
- Modified the migration script to include a branch argument for pushing to the hub, enhancing flexibility in version control.
- Improved error handling by ensuring the policy type is extracted from the configuration, promoting robustness.
- Streamlined the process of saving and pushing model components to the hub, allowing for a single commit with optional PR creation.
- Updated the commit message and description for better clarity on the migration changes and benefits, ensuring users are informed of the new architecture and usage.
- Updated the migration script to utilize `make_pre_post_processors` and `make_policy_config` from `lerobot.policies.factory`, enhancing consistency with the current codebase.
- Improved normalization statistics extraction and processor pipeline creation, ensuring compatibility with the new `PolicyProcessorPipeline` architecture.
- Cleaned up configuration handling by removing unnecessary fields and adding normalization mapping directly to the config.
- Enhanced type safety and readability by refining feature type and normalization mode handling.
- Updated imports in various files to include RobotAction and PolicyAction directly from the processor module, improving clarity and consistency.
- Removed redundant imports from core, streamlining the codebase and enhancing maintainability.
- Adjusted type annotations and references in the RobotProcessorPipeline and related components to align with the new import structure, ensuring better type safety and readability.
Jade Choghari