Efficient reinforcement learning control for continuum robots based on Inexplicit Prior Knowledge
This work addresses data inefficiency and simulation reliance in reinforcement learning for continuum robots, which is important for applications like minimally invasive surgery.
The paper tackles the challenge of applying reinforcement learning to complex continuum robots by proposing a method based on inexplicit prior knowledge, achieving active visual tracking and distance maintenance for a tendon-driven robot in real-world deployment.
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.