Assembly robots with optimized control stiffness through reinforcement learning

TL;DR

This paper introduces a reinforcement learning-based method for assembly robots that optimizes control stiffness in contact-rich tasks, enabling precise and damage-free operations with rapid response capabilities.

Contribution

It presents a novel RL approach that online generates stiffness matrices to enhance local trajectory optimization in contact-rich assembly tasks.

Findings

Effective in two contact-rich tasks

Rapid response due to short sampling time

Demonstrated successful implementation in manipulations

Abstract

There is an increased demand for task automation in robots. Contact-rich tasks, wherein multiple contact transitions occur in a series of operations, are extensively being studied to realize high accuracy. In this study, we propose a methodology that uses reinforcement learning (RL) to achieve high performance in robots for the execution of assembly tasks that require precise contact with objects without causing damage. The proposed method ensures the online generation of stiffness matrices that help improve the performance of local trajectory optimization. The method has an advantage of rapid response owing to short sampling time of the trajectory planning. The effectiveness of the method was verified via experiments involving two contact-rich tasks. The results indicate that the proposed method can be implemented in various contact-rich manipulations. A demonstration video shows the performance. (https://youtu.be/gxSCl7Tp4-0)