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A Framework and Algorithm for Human-Robot Collaboration Based on Multimodal Reinforcement Learning
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 hu...
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Published in: | Computational intelligence and neuroscience 2022-09, Vol.2022, p.1-13 |
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description | 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. |
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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.</description><identifier>ISSN: 1687-5265</identifier><identifier>EISSN: 1687-5273</identifier><identifier>DOI: 10.1155/2022/2341898</identifier><language>eng</language><publisher>New York: Hindawi</publisher><subject>Algorithms ; Behavior ; Collaboration ; Computational linguistics ; Deep learning ; Habits ; Language processing ; Learning ; Machine learning ; Natural language interfaces ; Natural language processing ; Neural networks ; Reinforcement ; Robotics ; Robots</subject><ispartof>Computational intelligence and neuroscience, 2022-09, Vol.2022, p.1-13</ispartof><rights>Copyright © 2022 Zeyuan Cai et al.</rights><rights>COPYRIGHT 2022 John Wiley & Sons, Inc.</rights><rights>Copyright © 2022 Zeyuan Cai et al. 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subjects | Algorithms Behavior Collaboration Computational linguistics Deep learning Habits Language processing Learning Machine learning Natural language interfaces Natural language processing Neural networks Reinforcement Robotics Robots |
title | A Framework and Algorithm for Human-Robot Collaboration Based on Multimodal Reinforcement Learning |
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