Learning for Reliable Control in Dynamical Systems

[CFAR Rising Star Lecture Series]
Learning for Reliable Control in Dynamical Systems by Professor Yue Yisong

Related to the theme of Scientific Machine Learning, this session describes an on-going research at Caltech that integrates learning into the design of reliable controllers for dynamical systems. To achieve certifiable control-theoretic guarantees while using powerful function classes such as deep neural networks, careful integration of conventional control & planning principles with learning into unified frameworks are required. 

Prof Yue Yisong from Argo AI will illustrate the methods that admit relevant behavioural guarantees and are practical to deploy. These methods are demonstrated in a variety of applications, including smooth broadcasting of sports games, agile aerial flight while dealing with perturbations and boundary conditions, and fast planning in resource-limited safety-critical settings such as Mars rover navigation.

Prof Yue Yisong is a Professor of Computing and Mathematical Sciences at the California Institute of Technology, and a Principal Scientist at Argo AI. Prior to his role as a research scientist at Disney Research, he was a postdoctoral researcher in the Machine Learning Department and the iLab at Carnegie Mellon University. He received a Ph.D. from Cornell University and a B.S. from the University of Illinois at Urbana-Champaign. Prof Yue’s research interests are centered around machine learning, and in particular, getting theory to work in practice. To that end, his research agenda spans both fundamental and applied pursuits. His work has been recognised with multiple paper awards and nominations, in fields such as robotics, computer vision, sports analytics, machine learning for health, and information retrieval. At Argo AI, he is developing machine learning approaches to motion planning for autonomous urban driving.