Zhiguang Liu1, Shilin Wang1, Jian Zhao1,*, Jianhong Hao2, Fei Yu3
CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 473-487, 2024, DOI:10.32604/cmes.2023.029729
Abstract A real-time adaptive roles allocation method based on reinforcement learning is proposed to improve human-robot cooperation performance for a curtain wall installation task. This method breaks the traditional idea that the robot is regarded as the follower or only adjusts the leader and the follower in cooperation. In this paper, a self-learning method is proposed which can dynamically adapt and continuously adjust the initiative weight of the robot according to the change of the task. Firstly, the physical human-robot cooperation model, including the role factor is built. Then, a reinforcement learning model that can adjust the role factor in real… More >
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