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Compared to rigid robots that are generally studied in reinforcementlearning, the physical characteristics of some sophisticated robots such assoft or continuum robots are higher complicated. Moreover, recent reinforcementlearning methods are data-inefficient and can not be directly deployed to therobot without simulation. In this paper, we propose an efficient reinforcementlearning method based on inexplicit prior knowledge in response to suchproblems. We first corroborate the method by simulation and employed directlyin the real world. By using our method, we can achieve active visual trackingand distance maintenance of a tendon-driven robot which will be critical inminimally invasive procedures. Codes are available athttps://github.com/Skylark0924/TendonTrack.

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