  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.
