Learning Perception and Control for Agile Off-Road Autonomous Driving

Department of Mechanical Engineering & Materials Science Seminar

Byron Boots
Assistant Professor
Interactive Computing
Georgia Tech

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

Abstract: 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.

Bio: 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. He is the recipient of several awards including an NSF CAREER award, the Best Overall Paper award at ICML, and finalist for Best Overall Paper at ICRA.

Wednesday, April 4, 2018
2:30 – 3:30 pm
Location – Mason 107
Host: Professor Aaron Dollar
Refreshments served at 2:15 pm