论文标题
局部连接增强学习方法，用于有效控制机器人钉孔组装
Local Connection Reinforcement Learning Method for Efficient Control of Robotic Peg-in-Hole Assembly
论文作者
论文摘要
机器人孔组装的传统控制方法取决于复杂的接触状态分析。加固学习（RL）逐渐成为控制机器人钉孔组装任务的首选方法。但是，RL的训练过程非常耗时，因为RL方法始终是全球连接的，这意味着所有状态组件都被认为是所有动作组件的策略输入，因此增加了要探索的动作空间和状态空间。在本文中，我们首先定义连续空间序列化的沙普利值（CS3），并构建一个连接图，以阐明状态组件上的作用组件的相关性。然后，我们提出了基于连接图的局部连接增强学习（LCRL）方法，从而消除了无关的状态组件对选择动作组件选择的影响。模拟和实验结果表明，通过LCRL方法获得的控制策略可提高控制过程的稳定性和速度。 LCRL方法将提高数据效率并增加培训过程的最终奖励。
Traditional control methods of robotic peg-in-hole assembly rely on complex contact state analysis. Reinforcement learning (RL) is gradually becoming a preferred method of controlling robotic peg-in-hole assembly tasks. However, the training process of RL is quite time-consuming because RL methods are always globally connected, which means all state components are assumed to be the input of policies for all action components, thus increasing action space and state space to be explored. In this paper, we first define continuous space serialized Shapley value (CS3) and construct a connection graph to clarify the correlativity of action components on state components. Then we propose a local connection reinforcement learning (LCRL) method based on the connection graph, which eliminates the influence of irrelevant state components on the selection of action components. The simulation and experiment results demonstrate that the control strategy obtained through LCRL method improves the stability and rapidity of the control process. LCRL method will enhance the data-efficiency and increase the final reward of the training process.
