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GRASP On Robotics: “Perspectives on Machine Learning for Adaptive Robotic Systems”
April 9 at 11:00 AM - 12:30 PM
Abstract: Recent advances in machine learning are leading to new tools for designing intelligent robots: functions relied on to govern a robot’s behavior can be learned from a robot’s interaction with its environment rather than hand-designed by an engineer. Many machine learning methods assume little prior knowledge and are extremely flexible, they can model almost anything! But, this flexibility comes at a cost. The same algorithms are often notoriously data hungry and computationally expensive, two problems that can be debilitating for robotics. In this talk I’ll discuss how machine learning can be combined with prior knowledge and structure to build effective solutions to robotics problems. I’ll introduce an online learning perspective on robot adaptation that unifies well-known algorithms and suggests new approaches. I’ll discuss how structure and simulation can augment learning and how imperfect models, simple policies, and hierarchical abstractions can help to build adaptive, resilient systems. I will also show how we have applied some of these ideas to several robotics tasks that require impressive sensing, speed, and agility to complete.
Associate Professor, University of Washington
Byron Boots is an Associate Professor in the Paul G. Allen School of Computer Science and Engineering at the University of Washington. His group performs fundamental and applied research in machine learning, artificial intelligence, and robotics with a focus on developing theory and systems that tightly integrate perception, learning, and control. His work has been applied to a range of problems including localization and mapping, motion planning, robotic manipulation, quadrupedal locomotion, and high-speed navigation. Byron has received several awards including “Best Paper” Awards from ICML, AISTATS, RSS, and IJRR. He is also the recipient of the RSS Early Career Award, the NSF CAREER Award, and the Outstanding Junior Faculty Research Award from the College of Computing at Georgia Tech. Byron received his PhD from the Machine Learning Department at Carnegie Mellon University.