- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
* fix(policies): remove action from batch for offline evaluation in diffusion, tdmpc, and vqbet policies
* style(diffusion): correct comment capitalization for clarity in modeling_diffusion.py
Prevent VAE inference when running in offline mode. In the lerobot dataset, the presence of the 'action' field incorrectly triggers the VAE inference block. This leads to a RuntimeError due to mismatched tensor dimensions (3 vs 2) when concatenating cls_embed, robot_state_embed, and action_embed—since action_embed lacks the chunk_size dimension. Additionally, this aligns with the original paper, where variational inference is skipped during inference.
* 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
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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>
* test(cameras): increase timeout in depth read for testing
* test(cameras): skip test_depth in realsense
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Co-authored-by: Michel Aractingi <michel.aractingi@huggingface.co>
* fix(ci): tag of image when pushing to main
* fix(docs): remove symlink in docs folder
* chore(docs): move .mdx files to docs/ folder
* chore(docs): create symlink to docs files
* chore(ci): de-couple fast and full test pipeline
* fix(ci): skip GPU Tests for community PRs
* Update so101.mdx
Signed-off-by: Jakob Frick <jakob.maria.frick@gmail.com>
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Update so101.mdx
Signed-off-by: Jakob Frick <jakob.maria.frick@gmail.com>
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Signed-off-by: Jakob Frick <jakob.maria.frick@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* LeRobotDataset video encoding: updated `save_episode` method and added `batch_encode_videos` method to handle video encoding based on `batch_encoding_size`, allowing for both immediate and batched encoding.
* LeRobotDataset video cleanup: Enabled individual episode cleanup and check for remaining PNG files before removing the `images` directory.
* LeRobotDataset - VideoEncodingManager: added proper handling of pending episodes (encoding, cleaning) on exit or recording failures.
* LeRobotDatasetMetadata: removed `update_video_info` to only update video info at episode index 0 encoding.
* Adjusted the `record` function to utilize the new encoding management logic.
* Removed `encode_videos` method from `LeRobotDataset` and `encode_episode_videos` outputs as they are nowhere used.
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Signed-off-by: Xingdong Zuo <zuoxingdong@users.noreply.github.com>
Co-authored-by: Xingdong Zuo <xingdong.zuo@navercorp.com>
Co-authored-by: Caroline Pascal <caroline8.pascal@gmail.com>
Adil Zouitine