Given a stream of multimodal sensory data, an autonomous robot must continuously refine its understanding of itself and its environment as it makes decisions on how to act to achieve a goal. These are difficult problems that roboticists have attacked using classical tools from mechanics and controls and, more recently, machine learning. However, classical methods and machine learning algorithms are often seen to be at odds, and researchers continue to debate the merits of engineering vs. learning. 

A recurring theme in this talk will be that prior knowledge and domain insights can make learning and inference easier. I will discuss several fundamental robotics problems including continuous-time motion planning, localization, and mapping from a unified probabilistic inference perspective. I will show how models from statistical machine learning like Gaussian Processes can be tightly integrated with insights from engineering expressed as differential equations to solve these problems efficiently. Finally, I will demonstrate the effectiveness of these algorithms on several existent robotics platforms.

Bio

Byron Boots is an assistant professor in the School of Interactive Computing and the Institute for Robotics and Intelligent Machines at the Georgia Institute of Technology. Prior to joining Georgia Tech, Boots was a postdoctoral researcher working with Dieter Fox in the Robotics and State Estimation Lab at the University of Washington. He received his Ph.D. in Machine Learning from Carnegie Mellon in 2012, where he was advised by Geoff Gordon. Boot’s work on learning models of dynamical systems received the 2010 Best Paper award at ICML. His current research focuses on developing theory and systems that integrate perception, learning, and decision-making.