Leveraging Learning and Combinatorial Optimization for Advancing Multi-Robot Systems
MIE Department Seminar
March 18, 2025
11:00 AM - 12:00 PM America/Chicago
Presenter: Pratap Tokekar, PhD, University of Maryland
Location: ERF 1043
Abstract:In this talk I will discuss my group's recent work on how to make multi-robot systems more robust and scalable. Many higher level decision making and coordination tasks in multi-robot systems can be abstracted as combinatorial optimization problems. While these algorithms work in theory, they often fail in practice because the abstraction ignores the uncertainty that's inherent in the real world. I will discuss our recent work on risk-aware combinatorial optimization that allows a user to trade-off risk and reward. Recently, learning has emerged as a practical tool for robot planning. However, these methods are hard to scale to large teams of robots, especially when they are heterogeneous. I'll present some of our recent work on scalable learning for multi-robot teams. Finally, I'll complete the loop by showing how learning can be combined with combinatorial optimization. I'll present some ongoing work on differentiable optimization that gives us the best of both worlds.
Speaker Bio: Pratap Tokekar is an associate professor in the Department of Computer Science at the University of Maryland and an Amazon Scholar. Between 2015 and 2019, he was an assistant professor at the Department of Electrical and Computer Engineering at Virginia Tech. Previously, he was a postdoctoral researcher at the GRASP lab of University of Pennsylvania. He obtained his Ph.D. in computer science from the University of Minnesota in 2014 and bachelor of technology degree in electronics and telecommunication from College of Engineering Pune, India in 2008. He is a recipient of the Amazon Research Award (2022), NSF CAREER award (2020), and CISE Research Initiation Initiative award (2016). He serves as an associate editor for the IEEE Transactions of Robotics, and the ICRA and IROS Conference Editorial Board.
Date posted
Mar 3, 2025
Date updated
Mar 3, 2025