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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Bibliographic Details
Published in:Computational intelligence and neuroscience 2022-09, Vol.2022, p.1-13
Main Authors: Cai, Zeyuan, Feng, Zhiquan, Zhou, Liran, Ai, Changsheng, Shao, Haiyan, Yang, Xiaohui
Format: Article
Language:English
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
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Summary: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.
ISSN:1687-5265
1687-5273
DOI:10.1155/2022/2341898