论文标题
基于不明智的先验知识，对连续机器人的有效加强学习控制
Efficient reinforcement learning control for continuum robots based on Inexplicit Prior Knowledge
论文作者
论文摘要
与通常在增强学习中研究的刚性机器人相比，某些复杂机器人（例如软机器人或连续机器人）的物理特征更为复杂。此外，最近的强化学习方法具有数据范围，如果没有仿真，就无法直接部署到机器人。在本文中，我们提出了一种有效的加强学习方法，基于对此类问题的不明智知识。我们首先通过模拟来证实该方法，并直接在现实世界中使用。通过使用我们的方法，我们可以实现肌腱驱动的机器人的主动视觉跟踪和距离维护，这在微创过程中至关重要。代码可在https://github.com/skylark0924/tendontrack上找到。
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.
