A Framework and Algorithm for Human-Robot Collaboration Based on Multimodal Reinforcement Learning. (28th September 2022)

• Record Type: Journal Article
• Authors: Cai, Zeyuan; Feng, Zhiquan; Zhou, Liran; Ai, Changsheng; Shao, Haiyan; Yang, Xiaohui
• Other Names: Hu Zhongxu Academic Editor.
• Is Part Of: Computational intelligence and neuroscience. Volume 2022(2022)
• Publication Date: 2022-09-28
• Subjects: Neurosciences -- Data processing -- Periodicals; Computational intelligence -- Periodicals; Computational neuroscience -- Periodicals; 612.80285
• DOI: 10.1155/2022/2341898
• Languages: English
• ISSNs: 1687-5265

Abstract

Abstract : Despite the emergence of various human-robot collaboration frameworks, most are not sufficiently flexible to adapt to users with different habits. In this article, a Multimodal Reinforcement Learning Human-Robot Collaboration (MRLC) framework is proposed. It integrates reinforcement learning into human-robot collaboration and continuously adapts to the user's habits in the process of collaboration with the user to achieve the effect of human-robot cointegration. With the user's multimodal features as states, the MRLC framework collects the user's speech through natural language processing and employs it to determine the reward of the actions made by the robot. Our experiments demonstrate that the MRLC framework can adapt to the user's habits after repeated learning and better understand the user's intention compared to traditional solutions.