McCormick Robotics Speaker Series: Ram Vasudevan
When:
Thursday, October 11, 2018
2:00 PM - 3:00 PM CT
Where: Mudd Hall ( formerly Seeley G. Mudd Library), 2210, 2233 Tech Drive, Evanston, IL 60208 map it
Audience: Faculty/Staff - Student - Post Docs/Docs - Graduate Students
Contact:
Jarvis Schultz
(847) 491-5474
Group: Master of Science in Robotics (MSR)
Category: Academic
Description:
Title:
Infinite Dimensional Optimization for Modeling, Estimation, and Control of Robotic Systems
Abstract:
Autonomous systems offer the promise of providing greater safety and access for people throughout the world. Unfortunately the broad deployment of such systems has been hindered due to lack of certified algorithms for system identification, estimation, and control. This is due in large part to the nonlinearity of the dynamics of these systems.
In this talk, we describe three techniques to address this challenge. First, we present a modeling method that allows one to represent a nonlinear system as a linear system in the infinite-dimensional space of real-valued functions. This approach, which is applied to identify models of soft robotic systems, enables one to construct models of nonlinear systems via linear regression on observed data. Second, we describe a measure-theoretic technique that allows one to represent polynomial optimization problems as linear programs in infinite-dimensions. This approach, which is applied to solve Pose-Graph and Landmark simultaneous localization and mapping problems, enables one to globally optimize non-convex polynomial optimization problems without initialization. Finally, we present a measure-theoretic method to compute the reachable set of high-dimensional nonlinear systems using an infinite dimensional linear program. This approach, which is applied to real-time, control of autonomous vehicles, enables one to perform provably safe control.
Bio:
Ram Vasudevan is an assistant professor in Mechanical Engineering at the University of Michigan. He received a BS in Electrical Engineering and Computer Sciences in 2006, an MS degree in Electrical Engineering in 2009, and a PhD in Electrical Engineering in 2012 all from the University of California, Berkeley. He is a recipient of the NSF Career Award in 2018 and the ONR Young Investigator Award in 2018. His research interests include dynamical systems, optimization, and robotics especially with applications involving human interaction.