Diff-Transfer: Model-based Robotic Manipulation Skill Transfer via Differentiable Physics Simulation

Yuqi Xiang, Feitong Chen, Qinsi Wang, Gang Yang, Xiang Zhang, Xinghao Zhu, Xingyu Liu, Lin Shao

Primary Area: applications to robotics, autonomy, planning

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Keywords: Differentiable simulation for manipulation; Transfer Learning in Robotics

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Abstract: The capability to transfer mastered skills to accomplish a range of similar yet novel tasks is crucial for intelligent robots. In this work, we introduce Diff-Transfer, a novel framework leveraging differentiable physics simulation to efficiently transfer robotic skills. Specifically, Diff-Transfer discovers a feasible path within the task space that brings the source task to the target task. At each pair of adjacent points along this task path, which is two sub-tasks, Diff-Transfer adapts known actions from one sub-task to tackle the other sub-task successfully. The adaptation is guided by the gradient information from differentiable physics simulations. We propose a novel path-planning method to generate sub-tasks, leveraging $Q$-learning with a task-level state and reward. We implement our framework in simulation experiments and execute four challenging transfer tasks on robotic manipulation, demonstrating the efficacy of Diff-Transfer through comprehensive experiments. Supplementary and Videos are on the website https://sites.google.com/view/difftransfer.

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Submission Number: 4650