huggingface/lerobot
Apr 2026 – Jun 2026
3 Months active
Languages Used
Python
Technical Skills
Data ProcessingDeep LearningMachine LearningPyTorchModel OptimizationModel Testing
Haoming Song
PROFILE
Haoming Song
Contributed to the huggingface/lerobot repository by developing and stabilizing advanced robot control features using Python, PyTorch, and deep learning techniques. Delivered a vision-language-action policy leveraging the Qwen2.5-VL backbone, enabling multimodal robot interaction and seamless integration with transformers v5.4.0. Addressed training stability by resolving torch.compile graph breaks and improving configuration management, including robust handling of pretrained processor overrides and validation logic. Enhanced experiment reproducibility and reduced maintenance risk through expanded test coverage and compatibility fixes across CUDA, MPS, and flash attention environments. The work emphasized backend development, model optimization, and reliable deployment pipelines for robotics and computer vision applications.
Overall Statistics
Feature vs Bugs
43%Features
Repository Contributions
7Total
Bugs
4
Commits
7
Features
3
Lines of code
4,704
Activity Months3
Your Network
118 peopleWork History
June 2026
1 Commits • 1 Features
Jun 1, 2026June 2026 – Enhanced training configurability and robustness for huggingface/lerobot. Delivered Flexible Action Overrides for Pretrained Processors, with safety validation to apply rename_map only when a pretrained checkpoint exists. Preserved pretrained processor pipelines when relative actions are enabled and aligned the registry key naming to relative_actions_processor. These changes reduce configuration errors, improve experiment reproducibility, and enable more flexible action processing in training pipelines.
1 Commits • 1 Features
Jun 1, 2026June 2026 – Enhanced training configurability and robustness for huggingface/lerobot. Delivered Flexible Action Overrides for Pretrained Processors, with safety validation to apply rename_map only when a pretrained checkpoint exists. Preserved pretrained processor pipelines when relative actions are enabled and aligned the registry key naming to relative_actions_processor. These changes reduce configuration errors, improve experiment reproducibility, and enable more flexible action processing in training pipelines.
June 2026
May 2026
5 Commits • 2 Features
May 1, 2026May 2026 highlights: Delivered EO-1 Vision-Language-Action policy for robot control using the Qwen2.5-VL backbone, enabling end-to-end configuration, training, and evaluation within LeRobot for richer multimodal robot interaction. Updated PI05 model to align with transformers v5.4.0 interface, improving compatibility and stability. Stabilized runtime by addressing torch.compile graph breaks and expanding test coverage for pi0/pi05. Hardened configuration robustness by fixing GR00TN15Config initialization and guarding the strict config decorator to maintain CI reliability. Resolved Eagle25VL model/processor crash across relevant transformer versions and improved processor initialization for compatibility with flash attention. Overall, these changes deliver more capable, reliable robot control and smoother upgrades with newer transformer ecosystems, reducing maintenance risk and accelerating feature delivery.
May 2026
5 Commits • 2 Features
May 1, 2026May 2026 highlights: Delivered EO-1 Vision-Language-Action policy for robot control using the Qwen2.5-VL backbone, enabling end-to-end configuration, training, and evaluation within LeRobot for richer multimodal robot interaction. Updated PI05 model to align with transformers v5.4.0 interface, improving compatibility and stability. Stabilized runtime by addressing torch.compile graph breaks and expanding test coverage for pi0/pi05. Hardened configuration robustness by fixing GR00TN15Config initialization and guarding the strict config decorator to maintain CI reliability. Resolved Eagle25VL model/processor crash across relevant transformer versions and improved processor initialization for compatibility with flash attention. Overall, these changes deliver more capable, reliable robot control and smoother upgrades with newer transformer ecosystems, reducing maintenance risk and accelerating feature delivery.
April 2026
1 Commits
Apr 1, 2026April 2026 monthly summary focused on stabilizing Beta sampling during training in PyTorch's compiled mode and CUDA. Delivered a fix by relocating noise and time sampling into policy wrappers, preserving the standard training workflow while avoiding compilation errors in torch.compile on MPS and maintaining the CUDA training path. Validation and risk mitigation were performed via targeted pre-commit checks and pytest suites for the policies pi0 and pi05, ensuring continued CI readiness and minimal regression risk.
1 Commits
Apr 1, 2026April 2026 monthly summary focused on stabilizing Beta sampling during training in PyTorch's compiled mode and CUDA. Delivered a fix by relocating noise and time sampling into policy wrappers, preserving the standard training workflow while avoiding compilation errors in torch.compile on MPS and maintaining the CUDA training path. Validation and risk mitigation were performed via targeted pre-commit checks and pytest suites for the policies pi0 and pi05, ensuring continued CI readiness and minimal regression risk.
April 2026
Activity
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Quality Metrics
Correctness82.8%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage48.6%
Skills & Technologies
Programming Languages
Python
Technical Skills
Data ProcessingDeep LearningMachine LearningModel OptimizationModel TestingNLPPyTorchPythonPython ProgrammingSoftware Developmentbackend developmentcomputer visionconfiguration managementdata modelingdeep learning
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