Qinchao Xu — accepted AI-conference papers

2026

M2oE: Modular Mixture of Experts for Multi-Morphology Reinforcement Learning of Modular Robots

Chang Liu, Qinchao Xu, Satoshi Yagi, Satoshi Yamamori, Yaonan Zhu, Yusuke Iwasawa +2

ICRA 2026poster

Modular robots offer a promising solution for building versatile and adaptable robotic systems. For instance, space exploration robots can be designed to reconfigure to meet diverse task demands across varying environments. However, training such systems by Reinforcement Learning (RL) remains challe…

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2026

When the Adversary Knows You Better: Adversarial Training for Learning-Based Legged Robots

Qinchao Xu, Satoshi Yagi, Satoshi Yamamori, Jun Morimoto

ICRA 2026poster

Deep reinforcement learning has emerged as the dominant paradigm for training legged robots to locomote, however, when deployed in unstructured, dynamically varying real-world environments, the safety of neural network based controllers remains insufficiently guaranteed. Prior studies have demonstra…

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