
Wilson Cheng developed advanced robotic manipulation and simulation capabilities for the WATonomous/humanoid repository, focusing on both feature delivery and code maintainability. He built a robot arm and shadow hand simulation framework using Python, enabling joint-space and task-space control testing for multiple manipulators. His work included major enhancements to reinforcement learning environments, introducing consolidated configurations and reward structures to improve humanoid arm manipulation. Wilson also implemented a multi-task differential inverse kinematics solver and a real-time hand pose retargeting framework leveraging MediaPipe and computer vision. Throughout, he maintained code quality through targeted refactoring and addressed stability by managing rollbacks and bug fixes.
2026-03 Monthly Summary for WATonomous/humanoid: Delivered robust humanoid arm manipulation capabilities and stronger IK tooling, with a new hand pose retargeting test framework. Focused on business value and technical depth across reinforcement learning, inverse kinematics, and task-specific improvements to cabinet interaction.
2026-03 Monthly Summary for WATonomous/humanoid: Delivered robust humanoid arm manipulation capabilities and stronger IK tooling, with a new hand pose retargeting test framework. Focused on business value and technical depth across reinforcement learning, inverse kinematics, and task-specific improvements to cabinet interaction.
Concise monthly summary for July 2025 focusing on key accomplishments, risk management, and business impact for WATonomous/humanoid.
Concise monthly summary for July 2025 focusing on key accomplishments, risk management, and business impact for WATonomous/humanoid.

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