  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.
