Assembly robots with optimized control stiffness through reinforcement learning

Autor:	Oikawa, Masahide, Kutsuzawa, Kyo, Sakaino, Sho, Tsuji, Toshiaki
Rok vydání:	2020
Předmět:	Computer Science - Robotics Computer Science - Machine Learning Electrical Engineering and Systems Science - Systems and Control
Druh dokumentu:	Working Paper
Popis:	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) Comment: Under review at the IEEE Robotics Automation Letter (RAL) and the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2002.12207 Zobrazit plný text záznamu View this record from Arxiv