Cornell University

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LMSS @ Cornell Tech: Byron Boots (Georgia Tech)

Friday, March 9, 2018 at 12:00pm to 1:30pm

Bloomberg Center, Room 165, Cornell Tech 2 West Loop Road, New York, NY 10044

Learning Machines Seminar Series

"Learning Perception and Control for Agile Off-Road Autonomous Driving"

The main goal of this talk is to illustrate how machine learning can start to address some of the fundamental perceptual and control challenges involved in building intelligent robots. I’ll start by introducing a new high speed autonomous “rally car” platform built at Georgia Tech, and discuss an off-road racing task that requires impressive sensing, speed, and agility to complete. I will discuss two approaches to this problem, one based on model predictive control and one based on learning deep policies that directly map images to actions. Along the way I’ll introduce new tools from reinforcement learning, imitation learning, and online learning and show how theoretical insights help us to overcome some of the practical challenges involved in learning on a real-world platform. I will conclude by discussing ongoing work in my lab related to machine learning for robotics. 


Byron Boots is an Assistant Professor of Interactive Computing at Georgia Tech. He directs the Georgia Tech Robot Learning Lab, affiliated with the Center for Machine Learning and the Institute for Robotics and Intelligent Machines. Byron’s research focuses on theory and systems that tightly integrate perception, learning, and control. Before starting at Georgia Tech, Byron received his Ph.D. in Machine Learning from Carnegie Mellon University and held a postdoctoral research position in Computer Science and Engineering at the University of Washington. His research has won several awards including Best Paper at ICML in 2010 and finalist for Best Paper at ICRA in 2017.


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Cornell Tech - NYC


CornellTech, CornellTechResearch, CornellTechCommunity, CornellTechSeminar, CornellTechLMSS





Byron Boots

Speaker Affiliation

Georgia Tech

Disability Access Information

Available Upon Request