NAVER LABS Europe seminars are open to the public. This seminar is virtual and requires registration
Date: 9th September 2021, 16:00pm CEST (GMT +02.00)
Interactive learning for robust autonomy
Abstract: 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 some of our recent work in this space. This involves, first, discussing the challenges associated with severe weather conditions. Second, approaches for reducing real-world data requirements for safe navigation and, finally, enabling safe learning for control in interactive settings.
About the Speaker: Igor Gilitschenski is an Assistant Professor of Computer Science at the University of Toronto. He is also a (part-time) Research Scientist at TRI. Prior to that, Dr. Gilitschenski was a Research Scientist at MIT’s Computer Science and Artificial Intelligence Lab. 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. His work received several awards including Best Paper Awards at the American Control Conference and the Robotics and Automation Letters.