Abstract: The talk will include a brief overview of methods for planning and perception developed at Zoox, and focus on some recent results for learning-based system identification and decision making.

Marin Kobilarov is principal engineer for planning and control at Zoox and assistant professor in Mechanical Engineering at the Johns Hopkins University where he leads the Laboratory for Autonomous Systems, Control, and Optimization. His research focuses on planning and control of robotic systems, on approximation methods for optimization and statistical learning, and applications to autonomous vehicles. Until 2012, he was a postdoctoral fellow in Control and Dynamical Systems at the California Institute of Technology. He obtained a Ph.D. from the University of Southern California in Computer Science (2008) and a B.S. in Computer Science and Applied Mathematics from Trinity College, Hartford, CT (2003).