Control of a Ball Catch Robot Using Machine Learning

• Main Author: Shinichi Imai
• Format: Article
• Language: English
• Published: Atlantis Press 2020-12-01
• Series: Journal of Robotics, Networking and Artificial Life (JRNAL)
• Subjects: Machine learning; control; experiment; evaluation
• Online Access: https://www.atlantis-press.com/article/125950176/view

Robots, such as industrial robots, have been used in the world of industry since the 1970s. There has been particularly rapid development in the field of robots in recent years, and there has been progress in robot research in industries such as communications and automobiles. For this reason, in the near future, robots with a diverse range of applications will be required around us. In this paper, as part of foundational research on robots and artificial intelligence, we propose a method for learning ball trajectories, using machine learning, to estimate target values for the distance in which robots move. In the proposed method, we use a linear regression model for supervised learning, and validate its effectiveness through experimentation.