论文标题
机器人装配控制重新配置基于具有不同几何特征的对象的转移加固学习
Robotic Assembly Control Reconfiguration Based on Transfer Reinforcement Learning for Objects with Different Geometric Features
论文作者
论文摘要
基于机器人力的合规性控制是实现高精度组装任务的首选方法。当组装对象的几何特征是不对称的或不规则的，加固学习（RL）剂逐渐将其纳入合规控制器中，以适应复杂的力置映射，这很难在分析上进行建模。由于力置映射强烈取决于几何特征，因此合规控制器仅对当前的几何特征最佳。为了降低具有不同几何特征的组装对象的学习成本，本文致力于回答如何重新配置具有不同几何特征的新组装对象的现有控制器。在本文中，基于模型的参数首先是根据提议的等效合规法（ETCL）重新配置的。然后，基于提出的加权维策略蒸馏（WDPD）方法将RL代理转移。实验结果表明，控制重新配置方法的成本较小，并实现了更好的控制绩效，这证实了提出的方法的有效性。
Robotic force-based compliance control is a preferred approach to achieve high-precision assembly tasks. When the geometric features of assembly objects are asymmetric or irregular, reinforcement learning (RL) agents are gradually incorporated into the compliance controller to adapt to complex force-pose mapping which is hard to model analytically. Since force-pose mapping is strongly dependent on geometric features, a compliance controller is only optimal for current geometric features. To reduce the learning cost of assembly objects with different geometric features, this paper is devoted to answering how to reconfigure existing controllers for new assembly objects with different geometric features. In this paper, model-based parameters are first reconfigured based on the proposed Equivalent Theory of Compliance Law (ETCL). Then the RL agent is transferred based on the proposed Weighted Dimensional Policy Distillation (WDPD) method. The experiment results demonstrate that the control reconfiguration method costs less time and achieves better control performance, which confirms the validity of proposed methods.
