強化學習於機械手臂插銷控制應用

Reinforcement learning control for robotic peg insertion

邱創杰(Chuang-Jie Chiou)

指導教授 ： 顏家鈺

國立臺灣大學/工學院/機械工程學研究所/碩士(2020年)

https://doi.org/10.6342/NTU202004037

摘要

隨著機器人的應用日益廣泛，生產線上動態插件的應用也爆炸性的成長，透過機器人位置控制、力回饋、以及引入強化學習，可以大幅降低傳統機械手臂所機要的輔助人力，並且可以降低插件損壞的可能性，本論文主要研究目的為機械手臂插銷控制應用，結合機器人學以及強化學習的機器人系統，模擬機器人系統能夠如人類手腕調整歪斜螺絲。

本文設計一簡易整合介面，將量測到的接觸力和力矩即時顯示，並引入強化學習，結合遠端運動中心控制，教導機器人類比人類手腕轉動調整螺絲歪斜狀況。

關鍵字

力回饋 ； 強化學習 ； 遠端運動中心

並列摘要

Given the fact that the applications of robot arm have become increasingly extensive, dynamic peg insertion on the production line has also been applied massively. Through robot position control, force measurements feedback, and reinforcement learning, the requirements of operation personnel and the chance of peg damage during insertion can be greatly reduced. The purpose of this thesis is the robot arm peg insertion control application. Combining robotics and reinforcement learning into robot systems, simulation of skew screws adjustment of human wrist can be achieved.
In this paper, we design a simple integration interface to visualize the contact forces and torques. Furthermore, using reinforcement learning technique, combined with remote center of motion control, it is possible to train the robot to learn how to adjust the skew of screw like human wrist do.

並列關鍵字

Peg insertion ； Reinforcement learning ； Remote center of motion control

參考文獻

[1] S. Wagner. "Reinforcement Learning and Supervised Learning: A brief comparison." (accessed.

[2] D. Silver et al., "Mastering the game of Go with deep neural networks and tree search," nature, vol. 529, no. 7587, p. 484, 2016.

[3] J. Kober, J. A. Bagnell, and J. Peters, "Reinforcement learning in robotics: A survey," The International Journal of Robotics Research, vol. 32, no. 11, pp. 1238-1274, 2013.

[4] S. Nagendra, N. Podila, R. Ugarakhod, and K. George, "Comparison of reinforcement learning algorithms applied to the cart-pole problem," in 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2017: IEEE, pp. 26-32.

[5] Y. P. Pane, S. P. Nageshrao, J. Kober, and R. Babuška, "Reinforcement learning based compensation methods for robot manipulators," Engineering Applications of Artificial Intelligence, vol. 78, pp. 236-247, 2019.