Efficient reinforcement learning control for continuum robots based on Inexplicit Prior Knowledge

• URL: http://arxiv.org/abs/2002.11573v2
• Date: Fri, 2 Oct 2020 17:02:25 GMT
• Title: Efficient reinforcement learning control for continuum robots based on Inexplicit Prior Knowledge
• Authors: Junjia Liu, Jiaying Shou, Zhuang Fu, Hangfei Zhou, Rongli Xie, Jun Zhang, Jian Fei and Yanna Zhao
• Abstract summary: We propose an efficient reinforcement learning method based on inexplicit prior knowledge. By using our method, we can achieve active visual tracking and distance maintenance of a tendon-driven robot.
• Score: 3.3645162441357437
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Compared to rigid robots that are generally studied in reinforcement learning, the physical characteristics of some sophisticated robots such as soft or continuum robots are higher complicated. Moreover, recent reinforcement learning methods are data-inefficient and can not be directly deployed to the robot without simulation. In this paper, we propose an efficient reinforcement learning method based on inexplicit prior knowledge in response to such problems. We first corroborate the method by simulation and employed directly in the real world. By using our method, we can achieve active visual tracking and distance maintenance of a tendon-driven robot which will be critical in minimally invasive procedures. Codes are available at https://github.com/Skylark0924/TendonTrack.

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