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.

翻译：以机器人力量为基础的合规控制是完成高精度组装任务的首选方法。当组装物体的几何特征不对称或不定期时,强化学习(RL)剂将逐渐纳入合规控制器,以适应难以进行分析的复杂武力定位绘图。由于强制定位绘图在很大程度上取决于几何特征,合规控制器仅对当前几何特征而言是最佳的。为了降低具有不同几何特征的组装物体的学习成本,本文件专门回答如何重新配置具有不同几何特征的新组装物体的现有控制器。在本文件中,基于模型的参数将首先根据拟议的等效合规法理论(ETCL)进行重组。然后,根据拟议的轻度政策蒸馏法(WDPD)方法将RL代理器转移。实验结果表明,控制重新配置方法花费的时间较少,而且取得更好的控制性能,这证实了拟议方法的有效性。