Beyond Topology: A Morphological Symmetry Graph Representation for Locomotion Policy Learning
Overview
This paper presents a morphological symmetry graph representation for locomotion policy learning, instantiated as MS-PPO on Unitree Go2 and MS-SAC on Unitree G1. The method incorporates permutations and sign changes induced by morphological symmetry into the graph actor and critic, achieving lower tracking errors in both simulation and hardware, and the highest mean simulation returns across three joint-failure types on Go2 among evaluated baselines. It also demonstrates zero-shot sim-to-real deployment on the 29-DoF Unitree G1 whole-body locomotion task.
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- arXiv Robotics — research abstractsSignalBeyond Topology: A Morphological Symmetry Graph Representation for Locomotion Policy Learning
This paper presents a morphological symmetry graph representation for locomotion policy learning, instantiated as MS-PPO on Unitree Go2 and MS-SAC on Unitree G1. The method incorporates permutations and sign changes induced by morphological symmetry into the graph actor and critic, achieving lower tracking errors in both simulation and hardware, and the highest mean simulation returns across three joint-failure types on Go2 among evaluated baselines. It also demonstrates zero-shot sim-to-real deployment on the 29-DoF Unitree G1 whole-body locomotion task.
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