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139 Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
 Steven Palma
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50293bb17b |
refactor(processors): several additions (#1926)
* chore(processor): remove merge_transitions functions (#1925) * refactor(processors): move processors out of configs (#1927) * chore(processor): streamline combine_features_dict (#1928) * chore(policies): use new constants (#1929) * fix(deps): right version transformers (#1930) * fix(tests): add none + disable async tests for now (#1931) |
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Steven Palma
|
846677f9cc | Merge branch 'main' into user/azouitine/2025-7-4-convert-codebase-with-pipeline | ||
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Gaëlle Lannuzel
|
6a3d57031a |
2 add reachy 2 to updated lerobot (#1767)
* Start adding Reachy 2 (no camera) * Fix joint shape * Remove print * Modify observation_features * Fix observation state * Try adding a fake Reachy teleoperator * Saving test scripts * Add reachy2camera to cameras * Add teleop_left camera to observation * Create test_reachy2_camera.py * Update utils.py * Add all rgb cameras * Future depth work * Try adding mobile_base velocity * Update tests * Update data_acquisition_server.py * Update with use_external_commands * Replay * Usable with or without mobile base * No need for new isntance * Use same ip for cameras * Remove useless imports * Add resume * Divide joints in multiple dicts * Divide joinits into several dicts in teleoperator * Fix forgotten method call * Create test_robot_client.py * Open gripper on start * Add arguments for cameras * Modify get_frame() requested size * Call generate_joints_dict on _init_ * black + isort * Add reachy2 in imports * Add reachy2 dependencies * Add documentation * Update reachy2.mdx * Update reachy2.mdx * Clean files and add types * Fix type in send_action * Remove print * Delete test files * Clean code * Update cameras * Disconnect from camera * Run pre-commit hooks * Update pyproject.toml * Create test_reachy2.py * Fix generate_joints * Update test_reachy2.py * Update send_action test * Update reachy2_cameras depth + CameraManager * Update reachy2_camera tests * Remove useless import and args * Rename reachy2_teleoperator * Create test_reachy2_teleoperator.py * Fix remainging fake_teleoperator * Remove useless elements * Mock cameras in test_reachy2 * Delete commented lines * Add use_present_position to teleoperator * Add cameras tests * Add check no part + test * Use disable_torque_on_disconnect * Use odometry for vel with present_position * Update documentation * Fix vel value type * Use ensure_safe_goal_position * Import joints dict from classes * Update reachy2.mdx * Update reachy2.mdx * Update minimal version * Update minimal version * fix(tests) fixes for reachy2 tests; removing reachy2 references from the script * Add reachy2_sdk fake as plugins --------- Co-authored-by: Michel Aractingi <michel.aractingi@huggingface.co> |
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Adil Zouitine
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99963b6968 |
refactor(dependencies): remove scipy dependency and introduce custom rotation utilities (#1863)
- 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. |
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Adil Zouitine
|
5f759b1637 |
feat(dependencies): Add scipy as a required dependency
- Included `scipy>=1.15.2` in the project dependencies to enhance functionality and support for scientific computing tasks. |
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Pepijn
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e5ade5565d |
Integrate pipeline and add phone teleop (#1681)
* Add normalization processor and related components - Introduced `NormalizationProcessor` to handle both observation normalization and action unnormalization. - Added `ObservationNormalizer` and `ActionUnnormalizer` classes for specific normalization tasks. - Updated `__init__.py` to include the new `NormalizationProcessor` in the module exports. - Enhanced `ObservationProcessor` with registration in the `ProcessorStepRegistry` for better modularity. - Created `RenameProcessor` for renaming keys in observations, improving flexibility in data processing. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Enhance processing architecture with new components - Added `RenameProcessor` to facilitate key renaming in observations, improving data handling flexibility. - Updated `__init__.py` to include `RenameProcessor` in module exports. - Refactored `NormalizationProcessor` and `ObservationNormalizer` to use `rsplit` for better key handling. - Introduced comprehensive tests for `NormalizationProcessor` and `RenameProcessor` to ensure functionality and robustness. * chore (docs): add docstring for processor * fix (test): test factory * fix(test): policies * Update tests/processor/test_observation_processor.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> Signed-off-by: Adil Zouitine <adilzouitinegm@gmail.com> * chore(test): add suggestion made by copilot regarding numpy test * fix(test): import issue * Refactor normalization components and update tests - Renamed `ObservationNormalizer` to `NormalizerProcessor` and `ActionUnnormalizer` to `UnnormalizerProcessor` for clarity. - Consolidated normalization logic for both observations and actions into `NormalizerProcessor` and `UnnormalizerProcessor`. - Updated tests to reflect the new class names and ensure proper functionality of normalization and unnormalization processes. - Enhanced handling of missing statistics in normalization processes. * chore (docstrin):Improve docstring for NormalizerProcessor * feat (device processor): Implement device processor * chore (batch handling): Enhance processing components with batch conversion utilities * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix(test): linting issue * chore (output format): improves output format * chore (type): add typing for multiprocess envs * feat (overrides): Implement support for loading processors with parameter overrides - Added the ability to provide non-serializable objects when loading processors from saved configurations using the `overrides` parameter. - Enhanced error handling for invalid override keys and instantiation errors. - Updated documentation and examples to illustrate the usage of overrides for both registered and unregistered steps. - Added comprehensive tests to validate the new functionality and ensure backward compatibility. * chore(normalization): addressing comments from copilot * chore(learner): nit comment from copilot * feat(pipeline): Enhance step_through method to support both tuple and dict inputs * refactor(pipeline): Simplify observation and padding data handling in batch transitions * Apply suggestions from code review Co-authored-by: Simon Alibert <75076266+aliberts@users.noreply.github.com> Signed-off-by: Adil Zouitine <adilzouitinegm@gmail.com> * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * refactor(pipeline): Introduce ComplementaryDataProcessor for handling complementary data in transitions * fix(ci): temporary fix on dataset deps version * feat(processors): Introduce processors for various policy types - Added `make_processor` function to create processor instances for different policy types, including `tdmpc`, `diffusion`, `act`, `vqbet`, `pi0`, `pi0fast`, `sac`, and `reward_classifier`. - Implemented corresponding processor files for each policy type, encapsulating normalization and unnormalization steps. - Updated existing policies to remove direct normalization dependencies, enhancing modularity and clarity. - Enhanced test coverage to validate the integration of new processors with existing policy configurations. * refactor(learner): Remove normalization from cached image features retrieval - Simplified the retrieval of observation features by removing the normalization step from the `get_cached_image_features` method calls. - This change enhances clarity and aligns with the recent updates to policy processors. * refactor(policies): Remove unnormalization step from action predictions - Eliminated the unnormalization of actions in both `TDMPCPolicy` and `VQBeTPolicy` classes to streamline action prediction. - This change improves code clarity and aligns with recent updates to policy processors. * feat(train): Integrate preprocessor into training pipeline * refactor(train): Update preprocessor initialization to include dataset statistics * refactor(policies): Enhance processor creation and add NaN detection hook * refactor(train): Update memory pinning logic for mps compatibility * feat: initial commit phone teleop * ugly delta control * use quaternion * Refactor observation preprocessing to use a modular pipeline system - Introduced `RobotPipeline` and `ObservationProcessor` for handling observation transformations. - Updated `preprocess_observation` to maintain backward compatibility while leveraging the new pipeline. - Added tests for the new processing components and ensured they match the original functionality. - Removed hardcoded logic in favor of a more flexible, composable architecture. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Refactor observation processing and improve modularity - Updated `ObservationProcessor` to enhance the modular design for processing observations. - Cleaned up imports and improved code readability by removing unnecessary lines and comments. - Ensured backward compatibility while integrating new processing components. - Added tests to validate the functionality of the updated processing architecture. * Remove redundant tests for None observation and serialization methods in `test_observation_processor.py` to streamline the test suite and improve maintainability. * Refactor processing architecture to use RobotProcessor - Replaced instances of RobotPipeline with RobotProcessor across the codebase for improved modularity and clarity. - Introduced ProcessorStepRegistry for better management of processing steps. - Updated relevant documentation and tests to reflect the new processing structure. - Enhanced the save/load functionality to support the new processor design. - Added a model card template for RobotProcessor to facilitate sharing and documentation. * Add RobotProcessor tutorial to documentation - Introduced a new tutorial on using RobotProcessor for preprocessing robot data. - Added a section in the table of contents for easy navigation to the new tutorial. - The tutorial covers key concepts, real-world scenarios, and practical examples for effective use of the RobotProcessor pipeline. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Add normalization processor and related components - Introduced `NormalizationProcessor` to handle both observation normalization and action unnormalization. - Added `ObservationNormalizer` and `ActionUnnormalizer` classes for specific normalization tasks. - Updated `__init__.py` to include the new `NormalizationProcessor` in the module exports. - Enhanced `ObservationProcessor` with registration in the `ProcessorStepRegistry` for better modularity. - Created `RenameProcessor` for renaming keys in observations, improving flexibility in data processing. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Enhance processing architecture with new components - Added `RenameProcessor` to facilitate key renaming in observations, improving data handling flexibility. - Updated `__init__.py` to include `RenameProcessor` in module exports. - Refactored `NormalizationProcessor` and `ObservationNormalizer` to use `rsplit` for better key handling. - Introduced comprehensive tests for `NormalizationProcessor` and `RenameProcessor` to ensure functionality and robustness. * chore (docs): add docstring for processor * fix (test): test factory * fix(test): policies * Update tests/processor/test_observation_processor.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> Signed-off-by: Adil Zouitine <adilzouitinegm@gmail.com> * chore(test): add suggestion made by copilot regarding numpy test * fix(test): import issue * Refactor normalization components and update tests - Renamed `ObservationNormalizer` to `NormalizerProcessor` and `ActionUnnormalizer` to `UnnormalizerProcessor` for clarity. - Consolidated normalization logic for both observations and actions into `NormalizerProcessor` and `UnnormalizerProcessor`. - Updated tests to reflect the new class names and ensure proper functionality of normalization and unnormalization processes. - Enhanced handling of missing statistics in normalization processes. * chore (docstrin):Improve docstring for NormalizerProcessor * feat (device processor): Implement device processor * chore (batch handling): Enhance processing components with batch conversion utilities * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix(test): linting issue * chore (output format): improves output format * chore (type): add typing for multiprocess envs * feat (overrides): Implement support for loading processors with parameter overrides - Added the ability to provide non-serializable objects when loading processors from saved configurations using the `overrides` parameter. - Enhanced error handling for invalid override keys and instantiation errors. - Updated documentation and examples to illustrate the usage of overrides for both registered and unregistered steps. - Added comprehensive tests to validate the new functionality and ensure backward compatibility. * chore(normalization): addressing comments from copilot * chore(learner): nit comment from copilot * feat(pipeline): Enhance step_through method to support both tuple and dict inputs * refactor(pipeline): Simplify observation and padding data handling in batch transitions * Apply suggestions from code review Co-authored-by: Simon Alibert <75076266+aliberts@users.noreply.github.com> Signed-off-by: Adil Zouitine <adilzouitinegm@gmail.com> * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * refactor(pipeline): Introduce ComplementaryDataProcessor for handling complementary data in transitions * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * refactor(pipeline): Transition from tuple to dictionary format for EnvTransition - Updated the EnvTransition structure to use a dictionary format instead of a tuple, enhancing readability and maintainability. - Replaced instances of TransitionIndex with TransitionKey for accessing transition components. - Adjusted related processing functions and tests to accommodate the new dictionary format, ensuring consistent handling of transitions across the codebase. * refactor(observation_processor): Improve observation processing by using constants and simplifying pixel handling - Introduced constants for observation keys to enhance readability. - Streamlined the handling of the "pixels" key by copying observations first and processing images more clearly. - Updated the environment state and agent position assignments to use the new constants, improving maintainability. * feat(pipeline): Add hook unregistration functionality and enhance documentation - Implemented methods to unregister before, after, and reset hooks in the RobotProcessor class, allowing for more flexible hook management. - Enhanced documentation to clarify hook execution semantics and the implications of modifying transitions within hooks. - Added comprehensive tests to verify the correct behavior of hook registration and unregistration, including error handling for non-existent hooks. * refactor(pipeline): Clarify hook behavior and improve documentation - Updated the RobotProcessor class to ensure hooks are strictly for observation and do not modify transitions, enhancing clarity and maintainability. - Refactored hook registration methods to reflect the new behavior, ensuring they accept only functions that do not return modified transitions. - Enhanced documentation to clearly outline the purpose of hooks and their execution semantics. - Added tests to verify that hooks are not executed during the step_through method while ensuring they function correctly during the __call__ method. * feat(pipeline): Add __repr__ method to RobotProcessor for improved readability - Implemented a __repr__ method in the RobotProcessor class to provide a clear string representation of the processor, including step names and optional parameters like name and seed. - Added comprehensive tests to validate the __repr__ output for various scenarios, including empty processors, single and multiple steps, custom names, and seed values. - Ensured that the representation handles long lists of steps with truncation for better readability. * chore(pipeline): Move _CFG_NAME along other class member * refactor(pipeline): Utilize get_safe_torch_device for device assignment - Replaced direct torch.device instantiation with get_safe_torch_device to ensure safe device handling. - This change enhances code readability and maintains consistency in device management across the RobotProcessor class. * refactor(pipeline): Enhance state filename generation and profiling method - Updated state filename generation to use the registry name when available, improving clarity in saved files. - Modified the profile_steps method to include a warmup_runs parameter, allowing for more controlled performance profiling. - Ensured consistent conditions during profiling by deep copying transitions for each run, enhancing accuracy in timing results. * chore(doc): address pip install commant lerobot that not exist yet * feat(pipeline): Enhance configuration filename handling and state file naming - Introduced support for custom configuration filenames in the `save_pretrained` method, allowing users to specify a filename instead of the default. - Improved state file naming to include step indices, preventing conflicts when multiple processors of the same type are saved. - Added automatic detection for configuration files when loading from a directory, with error handling for multiple files. - Updated tests to validate new features, including custom filenames and automatic config detection. * refactor(pipeline): Improve state file naming conventions for clarity and uniqueness - Enhanced state file naming to include the processor's sanitized name, ensuring uniqueness when multiple processors are saved in the same directory. - Updated tests to reflect changes in state file naming, verifying that filenames now include the processor name and step indices to prevent conflicts. - Added a new test to validate state file naming when using multiple processors, ensuring distinct filenames for each processor's state files. * docs(pipeline): Add clarification for repo name sanitization process * feat(processors): Introduce processors for various policy types - Added `make_processor` function to create processor instances for different policy types, including `tdmpc`, `diffusion`, `act`, `vqbet`, `pi0`, `pi0fast`, `sac`, and `reward_classifier`. - Implemented corresponding processor files for each policy type, encapsulating normalization and unnormalization steps. - Updated existing policies to remove direct normalization dependencies, enhancing modularity and clarity. - Enhanced test coverage to validate the integration of new processors with existing policy configurations. * refactor(learner): Remove normalization from cached image features retrieval - Simplified the retrieval of observation features by removing the normalization step from the `get_cached_image_features` method calls. - This change enhances clarity and aligns with the recent updates to policy processors. * refactor(policies): Remove unnormalization step from action predictions - Eliminated the unnormalization of actions in both `TDMPCPolicy` and `VQBeTPolicy` classes to streamline action prediction. - This change improves code clarity and aligns with recent updates to policy processors. * feat(train): Integrate preprocessor into training pipeline * refactor(train): Update preprocessor initialization to include dataset statistics * refactor(policies): Enhance processor creation and add NaN detection hook * feat(record): Integrate RobotProcessor into recording loop and update policy handling - Added support for RobotProcessor in the record_loop function to enhance data processing capabilities. - Updated the logic to reset both policy and processor when provided, ensuring proper state management. - Modified action prediction to utilize the processor, improving the overall functionality of the recording process. - Adjusted the save_checkpoint function to include preprocessor state saving, enhancing checkpointing capabilities. * feat(migration): Add script for migrating policy models with normalization layers * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * feat(migrate): Enhance migration script to create preprocessor and postprocessor for policy models - 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. * feat(migrate): Add model card generation and saving to migration script - 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. * feat(processor): Introduce ToBatchProcessor for handling observation batching - 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. * feat(processors): Add ToBatchProcessor to multiple policy processors - Integrated ToBatchProcessor into various policy processors to handle observation batching. - Updated make functions for act, diffusion, pi0, pi0fast, sac, smolvla, tdmpc, and vqbet processors to include the new batching functionality. - Ensured consistency across all processor implementations for improved data handling. * refactor(factory): Remove unused imports and NaN detection hook from processor creation * feat(batch_processor): Enhance ToBatchProcessor to handle action batching - 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. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * feat(factory): Enhance make_processor to support preprocessor and postprocessor configuration - Introduced ProcessorConfigKwargs TypedDict for better type safety in processor configuration. - Updated make_processor to accept preprocessor and postprocessor configuration filenames, improving flexibility in processor instantiation. - Refactored the loading of pretrained processors to utilize the new configuration options. * refactor(factory): Clean up imports in factory.py - Removed unused import of IdentityProcessor to streamline the code. * feat(migrate): Extend load_model_from_hub to include train configuration - 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. * refactor(record): Rename processor parameters and update processing logic - Renamed `processor` to `preprocessor` and added `postprocessor` parameter for clarity. - Updated the `record_loop` and `predict_action` functions to utilize the new preprocessor and postprocessor, enhancing the processing flow. - Ensured compatibility with existing functionality while improving code readability. * feat(batch_processor): Add task field processing to ToBatchProcessor - 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. * feat(normalization): Implement IDENTITY mode for normalization and unnormalization - Enhanced NormalizerProcessor and UnnormalizerProcessor to support IDENTITY mode, allowing features to bypass normalization when specified. - Updated processing logic to check normalization modes and handle missing statistics gracefully. - Added comprehensive unit tests to validate IDENTITY mode functionality for both observations and actions, ensuring correct behavior across various scenarios. - Improved error handling for unsupported normalization modes. * fix(rebase): remove residual normalization layer: * refactor(diffusion): remove normalization layer from input processing * Add debug + calib * cleanup * Add pipeline * fix int * Add record example * nit * Add feature contract to pipelinestep and pipeline * Add tests * Add processor tests * PR feedback * encorperate pr feedback * type in doc * oops * cleaned up steps and integrated pipeline with feature_contract * refactor steps and robot to pipeline * cleanup pipeline * cleanup code further * make it run * feat(processors): Introduce processors for various policy types - Added `make_processor` function to create processor instances for different policy types, including `tdmpc`, `diffusion`, `act`, `vqbet`, `pi0`, `pi0fast`, `sac`, and `reward_classifier`. - Implemented corresponding processor files for each policy type, encapsulating normalization and unnormalization steps. - Updated existing policies to remove direct normalization dependencies, enhancing modularity and clarity. - Enhanced test coverage to validate the integration of new processors with existing policy configurations. * refactor(learner): Remove normalization from cached image features retrieval - Simplified the retrieval of observation features by removing the normalization step from the `get_cached_image_features` method calls. - This change enhances clarity and aligns with the recent updates to policy processors. * refactor(policies): Remove unnormalization step from action predictions - Eliminated the unnormalization of actions in both `TDMPCPolicy` and `VQBeTPolicy` classes to streamline action prediction. - This change improves code clarity and aligns with recent updates to policy processors. * feat(train): Integrate preprocessor into training pipeline * refactor(train): Update preprocessor initialization to include dataset statistics * refactor(policies): Enhance processor creation and add NaN detection hook * feat(record): Integrate RobotProcessor into recording loop and update policy handling - Added support for RobotProcessor in the record_loop function to enhance data processing capabilities. - Updated the logic to reset both policy and processor when provided, ensuring proper state management. - Modified action prediction to utilize the processor, improving the overall functionality of the recording process. - Adjusted the save_checkpoint function to include preprocessor state saving, enhancing checkpointing capabilities. * feat(migration): Add script for migrating policy models with normalization layers * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * feat(migrate): Enhance migration script to create preprocessor and postprocessor for policy models - 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. * feat(migrate): Add model card generation and saving to migration script - 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. * feat(processor): Introduce ToBatchProcessor for handling observation batching - 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. * feat(processors): Add ToBatchProcessor to multiple policy processors - Integrated ToBatchProcessor into various policy processors to handle observation batching. - Updated make functions for act, diffusion, pi0, pi0fast, sac, smolvla, tdmpc, and vqbet processors to include the new batching functionality. - Ensured consistency across all processor implementations for improved data handling. * refactor(factory): Remove unused imports and NaN detection hook from processor creation * feat(batch_processor): Enhance ToBatchProcessor to handle action batching - 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. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * feat(factory): Enhance make_processor to support preprocessor and postprocessor configuration - Introduced ProcessorConfigKwargs TypedDict for better type safety in processor configuration. - Updated make_processor to accept preprocessor and postprocessor configuration filenames, improving flexibility in processor instantiation. - Refactored the loading of pretrained processors to utilize the new configuration options. * refactor(factory): Clean up imports in factory.py - Removed unused import of IdentityProcessor to streamline the code. * feat(migrate): Extend load_model_from_hub to include train configuration - 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. * refactor(record): Rename processor parameters and update processing logic - Renamed `processor` to `preprocessor` and added `postprocessor` parameter for clarity. - Updated the `record_loop` and `predict_action` functions to utilize the new preprocessor and postprocessor, enhancing the processing flow. - Ensured compatibility with existing functionality while improving code readability. * feat(batch_processor): Add task field processing to ToBatchProcessor - 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. * feat(normalization): Implement IDENTITY mode for normalization and unnormalization - Enhanced NormalizerProcessor and UnnormalizerProcessor to support IDENTITY mode, allowing features to bypass normalization when specified. - Updated processing logic to check normalization modes and handle missing statistics gracefully. - Added comprehensive unit tests to validate IDENTITY mode functionality for both observations and actions, ensuring correct behavior across various scenarios. - Improved error handling for unsupported normalization modes. * fix(rebase): remove residual normalization layer: * refactor(diffusion): remove normalization layer from input processing * refactor(normalization): Remove unused state dict transformation methods and streamline imports - Eliminated the _transform_state_dict_keys and _load_as_safetensor methods from PI0Policy, simplifying the model loading process. - Cleaned up imports in modeling_pi0.py by removing log_model_loading_keys and init_logging. - Updated TDMPCPolicy and VQBeTPolicy to handle action removal from batches during offline evaluation. - Introduced hotswap_stats function in normalize_processor.py to update normalization statistics dynamically, with corresponding tests to ensure functionality. * refactor(normalization): Clean up imports in normalize_processor.py * feat(batch_processor): Add feature_contract method to ToBatchProcessor - Introduced feature_contract method that returns features without modification, maintaining the no-op behavior of the processor. - This addition enhances the flexibility of the ToBatchProcessor for future feature processing needs. * fix(dependencies): Update transformers dependency constraint to allow only versions up to 4.52.0 * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * 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. * Do some todos and cleanup * change feature_contract to dataset_features * use one method for conversion pipeline output to add_frame dict and use base processors where possible * Add back in and use record_loop * update todo * rename to_dataset_frame * 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. * 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 * fix * fix reference frame * 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. * update data visualization * update teleop example * fix record bugs * Add replay * Not code * feature(pipeline): port tokenizer pipeline for VLA (#1645) * 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. * Add eval script * fix `q_curr` in InverseKinematicsEEToJoints to the IK solution * feat(processors): Introduce processors for various policy types - Added `make_processor` function to create processor instances for different policy types, including `tdmpc`, `diffusion`, `act`, `vqbet`, `pi0`, `pi0fast`, `sac`, and `reward_classifier`. - Implemented corresponding processor files for each policy type, encapsulating normalization and unnormalization steps. - Updated existing policies to remove direct normalization dependencies, enhancing modularity and clarity. - Enhanced test coverage to validate the integration of new processors with existing policy configurations. * refactor(learner): Remove normalization from cached image features retrieval - Simplified the retrieval of observation features by removing the normalization step from the `get_cached_image_features` method calls. - This change enhances clarity and aligns with the recent updates to policy processors. * refactor(policies): Remove unnormalization step from action predictions - Eliminated the unnormalization of actions in both `TDMPCPolicy` and `VQBeTPolicy` classes to streamline action prediction. - This change improves code clarity and aligns with recent updates to policy processors. * feat(train): Integrate preprocessor into training pipeline * refactor(train): Update preprocessor initialization to include dataset statistics * refactor(policies): Enhance processor creation and add NaN detection hook * feat(record): Integrate RobotProcessor into recording loop and update policy handling - Added support for RobotProcessor in the record_loop function to enhance data processing capabilities. - Updated the logic to reset both policy and processor when provided, ensuring proper state management. - Modified action prediction to utilize the processor, improving the overall functionality of the recording process. - Adjusted the save_checkpoint function to include preprocessor state saving, enhancing checkpointing capabilities. * feat(migration): Add script for migrating policy models with normalization layers * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * feat(migrate): Enhance migration script to create preprocessor and postprocessor for policy models - 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. * feat(migrate): Add model card generation and saving to migration script - 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. * feat(processor): Introduce ToBatchProcessor for handling observation batching - 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. * feat(processors): Add ToBatchProcessor to multiple policy processors - Integrated ToBatchProcessor into various policy processors to handle observation batching. - Updated make functions for act, diffusion, pi0, pi0fast, sac, smolvla, tdmpc, and vqbet processors to include the new batching functionality. - Ensured consistency across all processor implementations for improved data handling. * refactor(factory): Remove unused imports and NaN detection hook from processor creation * feat(batch_processor): Enhance ToBatchProcessor to handle action batching - 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. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * feat(factory): Enhance make_processor to support preprocessor and postprocessor configuration - Introduced ProcessorConfigKwargs TypedDict for better type safety in processor configuration. - Updated make_processor to accept preprocessor and postprocessor configuration filenames, improving flexibility in processor instantiation. - Refactored the loading of pretrained processors to utilize the new configuration options. * refactor(factory): Clean up imports in factory.py - Removed unused import of IdentityProcessor to streamline the code. * feat(migrate): Extend load_model_from_hub to include train configuration - 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. * refactor(record): Rename processor parameters and update processing logic - Renamed `processor` to `preprocessor` and added `postprocessor` parameter for clarity. - Updated the `record_loop` and `predict_action` functions to utilize the new preprocessor and postprocessor, enhancing the processing flow. - Ensured compatibility with existing functionality while improving code readability. * feat(batch_processor): Add task field processing to ToBatchProcessor - 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. * feat(normalization): Implement IDENTITY mode for normalization and unnormalization - Enhanced NormalizerProcessor and UnnormalizerProcessor to support IDENTITY mode, allowing features to bypass normalization when specified. - Updated processing logic to check normalization modes and handle missing statistics gracefully. - Added comprehensive unit tests to validate IDENTITY mode functionality for both observations and actions, ensuring correct behavior across various scenarios. - Improved error handling for unsupported normalization modes. * fix(rebase): remove residual normalization layer: * refactor(diffusion): remove normalization layer from input processing * refactor(normalization): Remove unused state dict transformation methods and streamline imports - Eliminated the _transform_state_dict_keys and _load_as_safetensor methods from PI0Policy, simplifying the model loading process. - Cleaned up imports in modeling_pi0.py by removing log_model_loading_keys and init_logging. - Updated TDMPCPolicy and VQBeTPolicy to handle action removal from batches during offline evaluation. - Introduced hotswap_stats function in normalize_processor.py to update normalization statistics dynamically, with corresponding tests to ensure functionality. * refactor(normalization): Clean up imports in normalize_processor.py * feat(batch_processor): Add feature_contract method to ToBatchProcessor - Introduced feature_contract method that returns features without modification, maintaining the no-op behavior of the processor. - This addition enhances the flexibility of the ToBatchProcessor for future feature processing needs. * fix(dependencies): Update transformers dependency constraint to allow only versions up to 4.52.0 * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * feature(pipeline): port tokenizer pipeline for VLA (#1645) * 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. * refactor(processors): Standardize processor naming conventions - Updated processor names across various files to use a consistent "robot_preprocessor" and "robot_postprocessor" format. - Modified the make_processor functions in factory, act, diffusion, pi0, pi0fast, sac, smolvla, tdmpc, and vqbet to reflect the new naming scheme. - Enhanced the pipeline configuration to align with the updated processor names, improving clarity and maintainability. * refactor(factory): Update processor configuration and type hints - Changed return type of get_policy_class to type[PreTrainedPolicy] for improved type safety. - Enhanced make_processor function to utilize dataset_stats in processor creation for better flexibility. - Updated ProcessorConfigKwargs to include dataset_stats, allowing for more comprehensive processor configurations. - Streamlined processor initialization by removing unnecessary kwargs and ensuring clarity in processor type handling. * Fix eval and android gripper * add some tests * refactor(factory, pi0fast): Update processor function names and parameters - Renamed make_pi0_processor to make_pi0fast_processor for clarity and consistency. - Updated parameter names in the factory's make_processor function to use pretrained_model_name_or_path instead of source, enhancing readability and alignment with naming conventions. * fix(train.py) push postprocessor with preprocessor - Add preprocesser policy overrides for device and rename_map - Add rename_map to DatasetRecordConfig (record.py) * Cleanup pr * fix more git diff pr issues * add path as type in save_pretrained * small nit * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * rename test file * fix: make dataset_features/feature_contract is optional * fix tests * Encorperate pr feedback * clean up record.py * add ascii art, fix normal record * remove merge issues * fix merge * remove features * Add feedback PR * fix last 4 tests * remove features check * rename to transform_features * add transform_features * fix lekiwi eval and update eval api example --------- Signed-off-by: Adil Zouitine <adilzouitinegm@gmail.com> Signed-off-by: Pepijn <138571049+pkooij@users.noreply.github.com> Co-authored-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: Copilot <175728472+Copilot@users.noreply.github.com> Co-authored-by: Simon Alibert <75076266+aliberts@users.noreply.github.com> Co-authored-by: Michel Aractingi <michel.aractingi@huggingface.co> |
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Steven Palma
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862bc7ef85 | Merge branch 'main' into user/azouitine/2025-7-4-convert-codebase-with-pipeline | ||
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Steven Palma
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c66cd40176 | chore: Bump to 0.3.4 (#1691) | ||
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Steven Palma
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b883328e6c | chore: Bump to 0.3.3 (#1690) | ||
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Steven Palma
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49ecbeb33f |
fix(deps): ceil torch pkg versions (#1689)
* fix(deps): ceil torch pkg versions * chore(Docs): add todo comment |
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pre-commit-ci[bot]
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a1734cf575 |
[pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci |
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Adil Zouitine
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82f300e880 | fix(dependencies): Update transformers dependency constraint to allow only versions up to 4.52.0 | ||
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Steven Palma
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8c577525c1 | chore: Bump to 4.0.0 (#1653) | ||
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Steven Palma
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f771e3eaf1 | fix(ci): create venv for release testing (#1652) | ||
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Steven Palma
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240a3892ae | fix(ci): remove uv run + bump minor (#1651) | ||
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Steven Palma
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3e24ecaf54 | chore(ci): Bump to v0.3.0 (#1649) | ||
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Steven Palma
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1baaa77a86 |
feat(ci): release workflow publish to pypi test + lock files (#1643)
* chore(ci): add some release stuff * chore(ci): add requirements-macos * chore(ci): added lockfiles for future reference * feat(ci): add draft & prerelease option to release workflow tag |
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Caroline Pascal
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c14ab9e97b | fix(dependencies): removing versions ceilings on tokenizers and huggingface_hub dependencies (#1618) | ||
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Caroline Pascal
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b267cd40f7 | fix(tokenizers dependency): adding ceiling version on tokenizers (#1612) | ||
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Michel Aractingi
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98746c7cf9 | bump wandb version to be compatible with ne grpcio-deps (#1604) | ||
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Caroline Pascal
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f089ab3628 | fix(hf hub dependency): adding ceiling version on huggingface_hub (#1608) | ||
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Steven Palma
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d4f962fb34 | feat(ci): add entrypoints + add version checks + add minimal release testing + uncomment publishing to pypi (#1589) | ||
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Eugene Mironov
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989f3d05ba |
[Async Inference] Merge Protos & refactoring (#1480)
* Merge together proto files and refactor Async inference * Fixup for Async inference * Drop not reuqired changes * Fix tests * Drop old async files * Drop chunk_size param * Fix versions * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix wrong fix Co-authored-by: Ben Zhang <ben.zhang@uwaterloo.ca> * Fixup --------- Co-authored-by: Michel Aractingi <michel.aractingi@huggingface.co> Co-authored-by: Ben Zhang <ben.zhang@uwaterloo.ca> Co-authored-by: Francesco Capuano <74058581+fracapuano@users.noreply.github.com> |
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Daniel Ritchie
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f59baeab45 | bump version for breaking changes in 1417 (#1515) | ||
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Steven Palma
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38d3737f09 | feat(ci): add new & clean dockerfiles (#1525) | ||
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Steven Palma
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378e1f0338 |
Update pre-commit-config.yaml + pyproject.toml + ceil rerun & transformer dependencies version (#1520)
* chore: update .gitignore * chore: update pre-commit * chore(deps): update pyproject * fix(ci): multiple fixes * chore: pre-commit apply * chore: address review comments * Update pyproject.toml Co-authored-by: Ben Zhang <5977478+ben-z@users.noreply.github.com> Signed-off-by: Steven Palma <imstevenpmwork@ieee.org> * chore(deps): add todo --------- Signed-off-by: Steven Palma <imstevenpmwork@ieee.org> Co-authored-by: Ben Zhang <5977478+ben-z@users.noreply.github.com> |
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pre-commit-ci[bot]
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c4c0105a47 |
[pre-commit.ci] pre-commit autoupdate (#1327)
* [pre-commit.ci] pre-commit autoupdate updates: - [github.com/adhtruong/mirrors-typos: v1.33.1 → v1.34.0](https://github.com/adhtruong/mirrors-typos/compare/v1.33.1...v1.34.0) - [github.com/astral-sh/ruff-pre-commit: v0.11.13 → v0.12.3](https://github.com/astral-sh/ruff-pre-commit/compare/v0.11.13...v0.12.3) - [github.com/woodruffw/zizmor-pre-commit: v1.9.0 → v1.11.0](https://github.com/woodruffw/zizmor-pre-commit/compare/v1.9.0...v1.11.0) - [github.com/PyCQA/bandit: 1.8.3 → 1.8.6](https://github.com/PyCQA/bandit/compare/1.8.3...1.8.6) * Ignore B615 --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Simon Alibert <simon.alibert@huggingface.co> Co-authored-by: Simon Alibert <75076266+aliberts@users.noreply.github.com> |
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Francesco Capuano
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30c161006d |
Add Async Inference (#1196)
Co-authored-by: Steven Palma <imstevenpmwork@ieee.org> Co-authored-by: Michel Aractingi <michel.aractingi@huggingface.co> |
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Adil Zouitine
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ce2b9724bf |
fix(hil-serl): discrete critic send through network (#1468)
Co-authored-by: Khalil Meftah <kmeftah.khalil@gmail.com> Co-authored-by: jpizarrom <jpizarrom@gmail.com> |
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Simon Alibert
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039de254ea |
Add Hope Jr (#935)
* Fix imports * Add feetech write tests * Nit * Add autoclosing fixture * Assert ping stub called * Add CalibrationMode * Add Motor in dxl robots * Simplify split_int_bytes * Rename read/write -> sync_read/write, refactor, add write * Rename tests * Refactor dxl tests by functionality * Add dxl write test * Refactor _is_comm_success * Refactor feetech tests by functionality * Add feetech write test * Simplify _is_comm_success & _is_error * Move mock_serial patch to dedicated file * Remove test skips & fix docstrings * Nit * Add dxl operating modes * Add is_connected in robots and teleops * Update Koch * Add feetech operating modes * Caps dxl OperatingMode * Update ensure_safe_goal_position * Update so100 * Privatize methods & renames * Fix dict * Add _configure_motors & move ping methods * Return models (str) with pings * Implement feetech broadcast ping * Add raw_values option * Rename idx -> id_ * Improve errors * Fix feetech ping tests * Ensure motors exist at connection time * Update tests * Add test_motors_bus * Move DriveMode & TorqueMode * Update Koch imports * Update so100 imports * Fix visualize_motors_bus * Fix imports * Add calibration * Rename idx -> id_ * Rename idx -> id_ * (WIP) _async_read * Add new calibration method for robot refactor (#896) Co-authored-by: Simon Alibert <simon.alibert@huggingface.co> * Remove deprecated scripts * Rename CalibrationMode -> MotorNormMode * Fix calibration functions * Remove todo * Add scan_port utility * Add calibration utilities * Move encoding functions to encoding_utils * Add test_encoding_utils * Rename test * Add more calibration utilities * Format baudrate tables * Implement SO-100 leader calibration * Implement SO-100 follower calibration * Implement Koch calibration * Add test_scan_port (TODO) * Fix calibration * Hack feetech firmware bug * Update tests * Update Koch & SO-100 * Improve format * Rename SO-100 classes * Rename Koch classes * Add calibration tests * Remove old calibration tests * Revert feetech hack and monkeypatch instead * Simplify motors mocks * Add is_calibrated test * Update viperx & widowx * Rename viperx & widowx * Remove old calibration * feat(teleop): thread-safe keyboard teleop implementation (#869) Co-authored-by: Simon Alibert <75076266+aliberts@users.noreply.github.com> * Add support for feetech scs series + various fixes * Update dynamixel with motors bus & tables changes * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * (WIP) Add Hope Jr * Rename arm -> hand * (WIP) Add homonculus arm & glove * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Add Feetech protocol version * Implement read * Use constants from sdks * (nit) move write * Fix broadcast ping type hint * Add protocol 1 broadcast ping * Refactor & add _serialize_data * Add feetech sm8512bl * Make feetech broadcast ping faster in protocol 1 * Cleanup * Add support for feetech protocol 1 to _split_into_byte_chunks * Fix unormalize * Remove test_motors_bus fixtures * Add more segmented tests (base motor bus & feetech), add feetech protocol 1 support * Add more segmented tests (dynamixel) * Refactor tests * Add handshake, fix feetech _read_firmware_version * Fix tests * Motors config & disconnect fixes * Add torque_disabled context * Update branch & fix pre-commit errors * Fix hand & glove readings * Update feetech tables * Move read/write_calibration implementations * Add setup_motor * Fix calibration msg display * Fix setup_motor & add it to robots * Fix _find_single_motor * Remove deprecated configure_motor * Remove deprecated dynamixel_calibration * Remove names * Remove deprecated import * refactor/lekiwi robot (#863) Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Simon Alibert <simon.alibert@huggingface.co> Co-authored-by: Simon Alibert <75076266+aliberts@users.noreply.github.com> * fix(teleoperators): use property is_connected (#1075) * Remove deprecated manipulator * Update robot features & naming * Update teleop features & naming * Add make_teleoperator_from_config * Rename find_port * Fix config parsing * Remove app script * Add setup_motors * Add teleoperate * Add record * Add replay * Fix test_datasets * Add mock robot & teleop * Add new test_control_robot * Add test_record_and_resume * Remove deprecated scripts & tests * Add calibrate * Add docstrings * Fix tests (no-extras install) * Add SO101 * Remove pynput from optional deps * Rename example 7 * Remove unecessary id * Add MotorsBus docstrings * Rename arm -> bus * Remove Moss arm * Fix setup_motors & calibrate configs * Fix test_calibrate * Add copyrights * Update hand & arm * Update homonculus hand & arm * Fix dxl _find_single_motor * Update glove * Add setup_motors for lekiwi * Fix glove calibration * Complete docstring * Add check for same min and max during calibration * Move MockMotorsBus * Add so100_follower tests * (WIP) add calibration gui * Fix test * Add setup_motors * Update calibration gui * Remove old .cache folder * Replace deprecated abc.abstractproperty * Fix feetech protocol 1 configure * Cleanup gui & add copyrights * Anatomically precise joint names * (WIP) Add glove to hand joints translation * Move make_robot_config * Add drive_mode & norm_mode in glove calibration * Fix joints translation * Fix normalization drive_mode * nit * Fix glove to hand conversion * Adapt feetech calibration * Remove pygame prompt * Implement arm calibration (hacks) * Better MotorsBus error messages * Update feetech read_calibration * Fix feetech test_is_calibrated * Cleanup glove * (WIP) Update arm * Add changes from #1117 * refactor(cameras): cameras implementations + tests improvements (#1108) Co-authored-by: Simon Alibert <simon.alibert@huggingface.co> Co-authored-by: Simon Alibert <75076266+aliberts@users.noreply.github.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> * Fix arm joints order * Add timeout/event logic * Fix arm & glove * Fix predict_action from record * fix(cameras): update docstring + handle sn when starts with 0 + update timeouts to more reasonable value (#1154) * fix(scripts): parser instead of draccus in record + add __get_path_fields__() to RecordConfig (#1155) * Left/Right sides + other fixes * Arm fixes and add config * More hacks * Add control scripts * Fix merge errors * push changes to calibration, teleop and docs * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Move readme to docs * update readme Signed-off-by: Martino Russi <77496684+nepyope@users.noreply.github.com> * Add files via upload Signed-off-by: Martino Russi <77496684+nepyope@users.noreply.github.com> * Update image sources * Symlink doc * Compress image * Move image * Update docs link * fix docs * simplify teleop scripts * fix variable names * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Address code review * add EMA to glove * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * integrate teleoperation for hand * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * update docs * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * import hopejr/homunculus in teleoperate * update docs for teleoperate, record, replay, train and inference * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * chore(hopejr): address comments * chore(hopejr): address coments 2 * chore(docs): update teleoperation instructions for the hand/glove * fix(hopejr): calibration int + update docs --------- Signed-off-by: Martino Russi <77496684+nepyope@users.noreply.github.com> Signed-off-by: Simon Alibert <75076266+aliberts@users.noreply.github.com> Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com> Co-authored-by: Steven Palma <imstevenpmwork@ieee.org> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: nepyope <nopyeps@gmail.com> Co-authored-by: Martino Russi <77496684+nepyope@users.noreply.github.com> Co-authored-by: Steven Palma <steven.palma@huggingface.co> |
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Michel Aractingi
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596e9050bd |
Refactor kinematics and switch to using placo (#1322)
Co-authored-by: Caroline Pascal <caroline8.pascal@gmail.com> Co-authored-by: Adil Zouitine <adilzouitinegm@gmail.com> Co-authored-by: leo-berte <leonardo.bertelli96@gmail.com> |
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Gregor Lenz
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6047bbee10 |
Update pyproject.toml to make package installable with pip (#1430)
Signed-off-by: Gregor Lenz <gregor@paddington-robotics.com> |
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Simon Alibert
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d4ee470b00 |
Package folder structure (#1417)
* Move files * Replace imports & paths * Update relative paths * Update doc symlinks * Update instructions paths * Fix imports * Update grpc files * Update more instructions * Downgrade grpc-tools * Update manifest * Update more paths * Update config paths * Update CI paths * Update bandit exclusions * Remove walkthrough section |
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Francesco Capuano
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7c8be7fb9b |
bump pi0 and hil transformers version (#1298)
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Adil Zouitine
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8fc18be065 | chore(dependencies): add gamepad support with pygame and hidapi (#1287) | ||
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Adil Zouitine
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d8079587a2 |
Port HIL SERL (#644)
Co-authored-by: Michel Aractingi <michel.aractingi@huggingface.co> Co-authored-by: Eugene Mironov <helper2424@gmail.com> Co-authored-by: s1lent4gnt <kmeftah.khalil@gmail.com> Co-authored-by: Ke Wang <superwk1017@gmail.com> Co-authored-by: Yoel Chornton <yoel.chornton@gmail.com> Co-authored-by: imstevenpmwork <steven.palma@huggingface.co> Co-authored-by: Simon Alibert <simon.alibert@huggingface.co> |
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Dana Aubakirova
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d0521189b1 |
fix issues: checkpoints keys mismatch and 'task' tokenisation in smolvla (#1256)
Co-authored-by: danaaubakirova <d.aubakirova@alumni.edu.kz> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Simon Alibert <75076266+aliberts@users.noreply.github.com> Co-authored-by: Simon Alibert <simon.alibert@huggingface.co> |
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Caroline Pascal
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1ee2ca5c26 | fix(pyserial): adding pyserial dependency to global ones (#1219) | ||
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Simon Alibert
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4e4eec92dc | Fix smolVLA dependencies (#1218) | ||
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Steven Palma
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09343acce7 | fix(smolvla): update record.py, fix populate_queues and remove unused dependencies (#1208) | ||
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Simon Alibert
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e23b41e79a |
Hardware API redesign (#777)
Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com> Co-authored-by: Steven Palma <imstevenpmwork@ieee.org> 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> Co-authored-by: Adil Zouitine <adilzouitinegm@gmail.com> Co-authored-by: Pepijn <pepijn@huggingface.co> |
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mshukor
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bfd26eef5a |
Add SmolVLA (#1175)
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: fracapuano <francesco.capuano@huggingface.co> Co-authored-by: Steven Palma <imstevenpmwork@ieee.org> Co-authored-by: Dana Aubakirova <118912928+danaaubakirova@users.noreply.github.com> Co-authored-by: Remi <remi.cadene@huggingface.co> |
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Adil Zouitine
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2be7f3a3ff | (hotfix): nightly CI by clipping pymunk version below 7.0.0 (#1182) | ||
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Adil Zouitine
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0cf864870c | [Fix] Unpin torch beyond 2.6.0 & torchcodec beyond 0.2.1 (#1127) | ||
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Pepijn
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a1daeaf0c4 |
feat(docs): Add new docs build process (#1046)
Co-authored-by: Mishig Davaadorj <dmishig@gmail.com> Co-authored-by: Steven Palma <steven.palma@huggingface.co> |
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Caroline Pascal
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6d723c45a9 | feat(encoding): switching to PyAV for ffmpeg related tasks (#983) | ||
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Adil Zouitine
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a75d00970f |
fix(ci): Pin torchcodec (==0.2.1) to fix pipeline temporarly (#1030)
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Adil Zouitine
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4df18de636 |
fix(ci): Pin draccus (<0.10.0) and torch (<2.7) to fix pipeline (#1022)
Co-authored-by: imstevenpmwork <steven.palma@huggingface.co> Co-authored-by: Simon Alibert <75076266+aliberts@users.noreply.github.com> |
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Steven Palma
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4041f57943 | feat(visualization): replace cv2 GUI with Rerun (and solves ffmpeg versioning issues) (#903) | ||
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Steven Palma
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145fe4cd17 | fix(deps): avoid torchcodec in macos x86_64 (#925) |