Prof. Derong Liu, IEEE Fellow, Guangdong University of Technology, China
Speech Title: Reinforcement Learning for Optimal Control of Complex Nonlinear Systems
Abstract: Reinforcement learning (RL) is one of the most important branches of artificial intelligence. Researchers have been using RL techniques in modern control theory. Self-learning control methodologies are a good representative of such efforts. RL recently has become a major force in the machine learning fields. On the other hand, adaptive dynamic programming (ADP) has now become popular in control communities. Both RL and ADP have roots in dynamic programming and in many ways they are equivalent. Major breakthroughs of ADPRL for optimal control were achieved around 2006, when iterative ADP approaches were introduced.
Biography: Derong Liu received the Ph.D. degree in electrical engineering from the University of Notre Dame in 1994. He was a Staff Fellow with General Motors Research and Development Center, from 1993 to 1995. He was an Assistant Professor with the Department of Electrical and Computer Engineering, Stevens Institute of Technology, from 1995 to 1999. He joined the University of Illinois at Chicago in 1999, and became a Full Professor of Electrical and Computer Engineering and of Computer Science in 2006. He served as the Associate Director of The State Key Laboratory of Management and Control for Complex Systems at the Institute of Automation, from 2010 to 2015. He is now a Full Professor with the School of Automation, Guangdong University of Technology. He has published 19 books. He is the Editor-in-Chief of Artificial Intelligence Review (Springer). He was the Editor-in-Chief of the IEEE Transactions on Neural Networks and Learning Systems from 2010 to 2015. He received the Faculty Early Career Development Award from the National Science Foundation in 1999, the University Scholar Award from University of Illinois from 2006 to 2009, the Overseas Outstanding Young Scholar Award from the National Natural Science Foundation of China in 2008, and the Outstanding Achievement Award from Asia Pacific Neural Network Assembly in 2014. He is a Fellow of the IEEE, a Fellow of the International Neural Network Society, and a Fellow of the International Association of Pattern Recognition.
Prof. Robert Mahony, IEEE Fellow, Australian National University, Australia
Speech Title: An Equivariant Perspective on Visual Spatial Awareness
Abstract: Spatial awareness algorithms for robotic systems include some of the most celebrated and researched problems in the field; Simultaneous Localisation and Mapping (SLAM), Visual Inertial Odomoetry (VIO), object pose estimation, etc. These algorithms are core enabling technology for 21st century automation. Recent advances in equivariant systems theory are providing a new set of tools to study classical problems in spatial awareness and leading to new algorithms with impressive performance. In this talk, I will provide a high level overview of some of the recent advances and explain how and why this new approach is so exciting.
Biography: Rob Mahony is a Professor in the Research School of Engineering at the Australian National University and has been a Chief Investigator with the Centre since its inauguration in 2014. His research interests are in non-linear systems theory with applications in robotics and computer vision. He wrote the seminal paper providing a clear exposition of non-linear complementary filters on the special orthogonal group for attitude estimation; an enabling technology in the early development of quadrotor aerial robotic vehicles. He was the first to provide a principled analysis for using optical flow of control of aerial robotic vehicles and was a coauthor on the first experimental paper that demonstrated landing of a quadrotor vehicle on a textured but featureless moving surface. In 2016, Rob was named a Fellow of the IEEE, recognising his contribution to the control aspects of aerial robotics. He is the leader of the Centre’s Fast Visual Motion Control research project.