Robotic Assembly Control Reconfiguration Based on Transfer Reinforcement Learning for Objects with Different Geometric Features

TL;DR

This paper presents a method for reconfiguring robotic assembly controllers using transfer reinforcement learning and compliance law theory to adapt to objects with varying geometric features, reducing learning time and improving performance.

Contribution

It introduces a novel combination of compliance law reconfiguration and transfer reinforcement learning for efficient adaptation to different object geometries in robotic assembly.

Findings

Reconfigured controllers require less learning time.

The method achieves better control accuracy.

Experimental results validate the approach's effectiveness.

Abstract

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.