期刊
PATTERN RECOGNITION LETTERS
卷 99, 期 -, 页码 67-76出版社
ELSEVIER
DOI: 10.1016/j.patrec.2017.04.007
关键词
Exoskeleton robot; Human-robot physical interaction; Human-in-the-loop; Reinforcement learning
资金
- Research Program for Brain Sciences from the Japan Agency for Medical Research and Development, AMED
- JSPS KAKENHI [JP16H06565]
- NEDO
- MIC-SCOPE
- Development of Medical Devices and Systems for Advanced Medical Services from AMED
- ImPACT Program of Council for Science, Technology and Innovation (Cabinet Office, Government of Japan)
- Research and development of technology for enhancing functional recovery of elderly and disabled people based on non-invasive brain imaging and robotic assistive devices
Social demand for exoskeleton robots that physically assist humans has been increasing in various situations due to the demographic trends of aging populations. With exoskeleton robots, an assistive strategy is a key ingredient. Since interactions between users and exoskeleton robots are bidirectional, the assistive strategy design problem is complex and challenging. In this paper, we explore a data-driven learning approach for designing assistive strategies for exoskeletons from user-robot physical interaction. We formulate the learning problem of assistive strategies as a policy search problem and exploit a data-efficient model-based reinforcement learning framework. Instead of explicitly providing the desired trajectories in the cost function, our cost function only considers the user's muscular effort measured by electromyography signals (EMGs) to learn the assistive strategies. The key underlying assumption is that the user is instructed to perform the task by his/her own intended movements. Since the EMGs are observed when the intended movements are achieved by the user's own muscle efforts rather than the robot's assistance, EMGs can be interpreted as the cost of the current assistance. We applied our method to a 1-DoF exoskeleton robot and conducted a series of experiments with human subjects. Our experimental results demonstrated that our method learned proper assistive strategies that explicitly considered the bidirectional interactions between a user and a robot with only 60 seconds of interaction. We also showed that our proposed method can cope with changes in both the robot dynamics and movement trajectories. (C) 2017 The Authors. Published by Elsevier B.V.
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