Speaker: Igor Gilitschenski
Location: 122 Gates Hall and Zoom
Time: 2:40 p.m.-3:30 p.m.
In recent years, we have seen an exploding interest in real-world deployment of autonomous systems, such as autonomous drones or vehicles. This interest was sparked by major advances in robot perception, planning, and control. However, robust operation in the “wild” remains a challenging goal. Correct consideration of the broad variety of real-world conditions requires both, better understanding of the learning process and robustifying the deployment of autonomous robots. In this talk, I will discuss several of our recent works in that space. This involves, first, discussing the challenges associated with severe weather conditions. Second, approaches for reducing real-world data requirements for safe navigation. Finally, enabling safe learning for control in interactive settings.
Bio: Igor Gilitschenski is an Assistant Professor of Computer Science at the University of Toronto where he leads the Toronto Intelligent Systems Lab. He is also a (part-time) Research Scientist at the Toyota Research Institute. Prior to that, Dr. Gilitschenski was a Research Scientist at MIT’s Computer Science and Artificial Intelligence Lab and the Distributed Robotics Lab (DRL) where he was the technical lead of DRL’s autonomous driving research team. He joined MIT from the Autonomous Systems Lab of ETH Zurich where he worked on robotic perception, particularly localization and mapping. Dr. Gilitschenski obtained his doctorate in Computer Science from the Karlsruhe Institute of Technology and a Diploma in Mathematics from the University of Stuttgart. His research interests involve developing novel robotic perception and decision-making methods for challenging dynamic environments. He is the recipient of several best paper awards including at the American Control Conference, the International Conference of Information Fusion, and the Robotics and Automation Letters.