題  目：Robot Control, Learning and Teleoperation

内容簡介：Nowadays robots are expected to be of increasing intelligence to deal with a large range of tasks. Especially, robots are supposed to be able to learn skills from our humans and perform dexterous manipulation in a human-like manner. A number of learning algorithms and techniques have been developed and successfully implemented for various robotic tasks. Among these methods, learning from demonstrations (LfD) enables robots to effectively and efficiently acquire skills by learning from human demonstrators, such that a robot can be quickly programmed to perform new tasks. This talk will present my recent research progress on human-in-the-loop learning for robots to acquire and generalize manipulation skills. It will also introduce my studies on human-like control design inspired by human motor behaviors from more than a decade ago. The control and learning technologies I developed have been particularly applied to robot teleoperation, for which I have also extensively investigated human robot interaction interface. This talk will also cover my recent work on teleoperation for minimum invasive surgery and ultrasound scanning.

報告人：楊辰光

報告人簡介：華南理工大學教授、博導，廣東省智能系統控制工程技術研究中心主任，

美國電氣與電子工程師學會（IEEE）Fellow。他是IEEE協作自動化柔性制造（CAFΜ）技術委員的聯合主席、Robot Learning期刊的主編、Frontiers on Robotics and AI計算智能領域主編、IEEE Transactions on Systems, Man and Cybernetics: Systems的高級編委。作為第一作者，他獲得過機器人學頂刊IEEE Transactions on Robotics最佳論文獎（2012）和計算智能領域頂刊IEEE Transactions on Neural Networks and Learning Systems傑出論文獎（2022）。以第一完成人獲得過自動化學會自然科學一等獎，廣東省自然科學二等獎以及教育部自然科學二等獎，并多次入選科睿唯安全球高被引學者。

時  間：2024年7月29日（周一）下午15：30開始

地  點：南海樓407室

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2024年7月28日