Efficient reinforcement learning control for continuum robots based on Inexplicit Prior Knowledge
arXiv:2002.11573
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.
arXiv comment11 pages, 12 figures
References in corpus (6)
- Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
- Robots that can adapt like animals
- QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation
- Model-Ensemble Trust-Region Policy Optimization
- Model-Based Value Estimation for Efficient Model-Free Reinforcement Learning
- Value Prediction Network