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GRASP On Robotics: “Learning and Games in Self-Driving”
April 16, 2021 at 11:00 AM - 12:30 PM
Abstract: In this talk, I’ll explore the power of a game-theoretic viewpoint in self-driving and in machine learning. We begin by considering the application of machine learning to Aurora’s advanced self-driving system in both perception and decision making. We discuss complexities that arise from multi-actor interaction.
We then explore the, perhaps surprising, role a game-theoretic view can take in developing algorithms for learning to make decisions. In particular, we review a “no-regret” game-theoretic perspective on model-based RL, Approximate Policy Iteration, and Inverse Optimal Control.
Chief Scientist and co-founder, Aurora
J. Andrew (Drew) Bagnell is Chief Scientist and co-founder of Aurora (aurora.tech), where he works with an amazing team to develop and deliver self-driving safely, quickly, and broadly. Dr. Bagnell has worked for 19 years at the intersection of machine learning and robotics with expertise in self-driving cars, imitation and reinforcement learning, planning, and computer vision. Aurora was founded in 2017 to enable autonomous driving solutions that will make roads safer, improve lives, revitalize cities, and expand transportation access.Dr. Bagnell is also a Consulting Professor at Carnegie Mellon University’s Robotics Institute and Machine
Learning Department. His interests in artificial intelligence range from algorithmic and basic theoretical development to delivering fielded learning-based systems. Bagnell and his research group have received over a dozen research awards for publications in both the robotics and machine learning communities including best paper awards at ICML, RSS, and ICRA.
He received the 2016 Ryan Award, Carnegie Mellon University’s award for Meritorious Teaching, and served as the founding director of the Robotics Institute Summer Scholars program, a summer research experience that has enabled hundreds of undergraduates throughout the world to leap into robotics research.