Institute for Aerospace Studies, University of Toronto
Talk Title: Machine learning for safe, high-performance control of mobile robots
Abstract: Traditionally, planning, control and decision making algorithms have been designed based on a-priori knowledge about the system and its environment, including models of the system dynamics and maps of the environment. This approach has enabled successful system operation in predictable situations, where the models are a good approximation of the real system behavior. However, if detailed models are not available, control systems are typically designed to be conservative against the unknown, which may cause drastic performance losses. To achieve safe and efficient system behavior in the presence of uncertainties and unknown disturbances, we aim to enable systems to learn during operation and adapt their behavior accordingly. In this talk, I will present our approaches towards online, data-efficient, safety-guaranteed learning for robot control. Our algorithms leverage and combine: (i) insights and approaches from control theory, (ii) state-of-the-art and probabilistic learning methods such as neural nets and Gaussian Processes, and (iii) any prior knowledge we may have about the system dynamics. We demonstrate our algorithms on self-flying and -driving vehicles, as well as on mobile manipulators. More information and videos at: www.dynsyslab.org.Bio: Angela Schoellig is an assistant professor at the University of Toronto Institute for Aerospace Studies, an associate director of the Centre for Aerial Robotics Research and Education at U of T, and an instructor of Udacity’s flying-car nanodegree program. She conducts research at the interface of robotics, controls, and machine learning. Her goal is to enhance the performance, safety, and autonomy of robots by enabling them to learn from past experiments and from each other. She is a recipient of a Sloan Research Fellowship (US/Canada-wide award, one of two in robotics); a Canadian Ministry of Research, Innovation & Science Early Researcher Award; and a Connaught New Researcher Award. With a team of undergraduate and graduate students, she won the 2018 GM/SAE AutoDrive Challenge, a North-America-wide self-driving competition. She is one of MIT Technology Review’s Innovators Under 35 (2017), one of Robohub’s “25 women in robotics you need to know about (2013),” winner of MIT’s 2015 Enabling Society Tech Competition, a 2015 finalist in Dubai’s $1 million “Drones for Good” competition, and the youngest member of the 2014 Science Leadership Program, which promotes outstanding scientists in Canada. Her PhD was awarded the ETH Medal and the 2013 Dimitris N. Chorafas Foundation Award (one of 35 worldwide). More information at: www.dynsyslab.org.