INVITED SPEAKER SEMINAR - Machine Learning for More Efficient Robots

Date: Tuesday, June 18th, 2024, from 3:30 p.m.    
Location: EV 11.119

Abstract

Systems engineering is integral across industries due to its focus on understanding the complex interactions and influences between various systems, which is crucial for effective design. Additionally, the integration of human factors into system design significantly affects the development of engineering products. Among the most challenging systems to design are those involving moving entities, which can pose safety risks to users. Recently, the application of machine learning to these safety-critical systems has emerged, although its impact on the design of compound systems remains underexplored.

This presentation seeks to examine how systems engineering concepts and perspectives can enhance applications involving moving systems, particularly robots that interact with humans. We will specifically address the development of solutions for unmanned aerial vehicles (UAVs), unmanned ground vehicles (UGVs), self-driving vehicles, and human-machine interaction with vehicles and robotic arms.

The presentation will discuss the role of artificial intelligence and machine learning in systems engineering, with a focus on deep learning and reinforcement learning techniques. Examples will be provided in key areas such as automated infrastructure maintenance, human-machine interaction, communications, and sensing and control, illustrating the practical applications and benefits of these advanced technologies.

Biography

Dr. Sidney Givigi is an Associate Professor in the School of Computing at Queen’s University, a position he has held since January 2019. Prior to this, he was a faculty member in the Department of Electrical and Computer Engineering at the Royal Military College of Canada from 2009 to 2019. Over the past decade, Dr. Givigi has supervised over 40 graduate students, many of whom now occupy key positions in universities, industry, and government organizations globally, particularly in fields related to Artificial Intelligence (AI) and Machine Learning (ML).

Dr. Givigi has co-authored more than 150 scientific papers in prestigious journals and conferences with low acceptance rates. Some of these papers have received best-paper awards at major conferences, such as the IEEE International Conference on Systems, Man, and Cybernetics (SMC). As a Senior Member of IEEE, Dr. Givigi has led over 25 research projects in areas including robotics, autonomous systems, control, and machine learning. His work extensively covers the modeling of uncertain systems using reinforcement learning and inference systems for the perception of robotic and self-driving systems. With nearly 20 years of experience, Dr. Givigi has developed numerous robotic solutions involving mobile robots and manipulators that operate in conjunction with humans.