Ming Cao: Title, abstract and bio

Ming Cao

Title: What can large language models (AI) do for autonomous robots?

Abstract: Large language models are particularly useful for autonomous robots to gain understanding of their surroundings from information given in human daily languages. These models also become handy for engineers to enable robots to carry out commonsense reasoning, and thus significantly enhance robots' ability to comprehend subtle contexts, and even to handle implicit information privacy constraints. In this talk, I will use robotic navigation as a case study to show how to apply large language models for autonomous robots. It offers discussion on state-of-the-art technologies of 3D mapping and target identification; more importantly, it provides food for thought on what the benefits and challenges are when applying the latest developments in AI for autonomous robots in broad areas of warehousing, logistics, manufacturing and healthcare.

Bio: Ming Cao has since 2016 been a professor of networks and robotics with the Engineering and Technology Institute (ENTEG) at the University of Groningen, the Netherlands, where he started as an assistant professor in 2008. Since 2022 he is the director of the Jantina Tammes School of Digital Society, Technology and AI at the same university. He received the Bachelor degree in 1999 and the Master degree in 2002 from Tsinghua University, China, and the Ph.D. degree in 2007 from Yale University, USA. From 2007 to 2008, he was a Research Associate at Princeton University, USA. He worked as a research intern in 2006 at the IBM T. J. Watson Research Center, USA. 

He is an IEEE Fellow. He is the 2017 and inaugural recipient of the Manfred Thoma medal from the International Federation of Automatic Control (IFAC) and the 2016 recipient of the European Control Award sponsored by the European Control Association (EUCA). He is a Senior Editor for Systems and Control Letters, and has been an Associate Editor for IEEE Transactions on Automatic Control, IEEE Transaction on Control of Network Systems, IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Circuits and Systems, IEEE Robotics & Automation Magazine, and IEEE Circuits and Systems Magazine. He is a member of the IFAC Council and a vice chair of the IFAC Technical Committee on Large-Scale Complex Systems. His research interests include autonomous robots and multi-agent systems, complex networks and decision-making processes.