论文标题
通过深度加固学习学习球平衡机器人
Learning Ball-balancing Robot Through Deep Reinforcement Learning
论文作者
论文摘要
平衡机器人（Ballbot）是测试平衡控制器有效性的好平台。考虑到平衡控制，常规的基于模型的反馈控制方法已被广泛使用。但是，接触和碰撞很难建模，并且通常会导致平衡控制的失败，尤其是当球机器人倾斜的角度时。为了探索球机器人的最大初始倾斜角，平衡控制被解释为使用增强学习（RL）的恢复任务。 RL是难以建模的系统的强大技术，因为它允许代理通过与环境进行交互来学习策略。在本文中，通过将常规反馈控制器与RL方法相结合，提出了化合物控制器。我们通过训练代理成功执行涉及联系和碰撞的恢复任务来显示化合物控制器的有效性。仿真结果表明，与传统的基于模型的控制器相比，使用化合物控制器可以在更大的初始倾斜角度下保持平衡。
The ball-balancing robot (ballbot) is a good platform to test the effectiveness of a balancing controller. Considering balancing control, conventional model-based feedback control methods have been widely used. However, contacts and collisions are difficult to model, and often lead to failure in balancing control, especially when the ballbot tilts a large angle. To explore the maximum initial tilting angle of the ballbot, the balancing control is interpreted as a recovery task using Reinforcement Learning (RL). RL is a powerful technique for systems that are difficult to model, because it allows an agent to learn policy by interacting with the environment. In this paper, by combining the conventional feedback controller with the RL method, a compound controller is proposed. We show the effectiveness of the compound controller by training an agent to successfully perform a recovery task involving contacts and collisions. Simulation results demonstrate that using the compound controller, the ballbot can keep balance under a larger set of initial tilting angles, compared to the conventional model-based controller.
