Negar Mehr
Seminar Information
To transform our lives, robots need to interact with other agents in complex shared environments. For example, autonomous cars need to interact with pedestrians, human-driven cars, and other autonomous cars. Autonomous delivery drones need to navigate in the aerial space shared by other drones, or mobile robots in a warehouse must navigate in the factory space shared by robots and humans. The interactive nature of such application domains requires us to develop a systematic methodology for enabling efficient interactions of robotic systems across various applications. The goal of my research is to develop algorithms and mathematical models that enable safe and intelligent interactions in such multi-agent domains.
In this talk, I will first focus on interactive planning and control for robots. To reach intelligent robotic interactions, robots must account for the dependence of agents' decisions upon one another. I will discuss how game-theoretic planning and control enables robots to be cognizant of their influence on other agents. I will present our recent results on leveraging the structure that is inherent in interactions to develop efficient motion planning algorithms which are suitable for real-time operation on robot hardware. In the second part of the talk, I will focus on how robots can learn and infer the intentions of their surrounding agents to account for agents' preferences and objectives. Currently, robots can infer the objectives of isolated agents within the formalism of inverse reinforcement learning; however, in multi-agent domains, agents are not isolated, and the decisions of all agents are mutually coupled. I will discuss a mathematical theory and numerical algorithms for inferring these interrelated preferences from observations of agents’ interactions.
Negar Mehr is an assistant professor of Aerospace Engineering at the University of Illinois Urbana-Champaign. She is also affiliated with the Coordinated Science Laboratory and the Electrical and Computer Engineering department at UIUC. Previously, she was a postdoctoral scholar at Stanford Aeronautics and Astronautics department from 2019 to 2020. She received her PhD in Mechanical Engineering from UC Berkeley in 2019 and her B.Sc. in Mechanical Engineering from Sharif University of Technology, Tehran, Iran, in 2013. Her research interests lie at the intersection of control theory, game theory, and machine learning. Specifically, she is interested in developing control algorithms that enable safe and intelligent multi-agent interactions. Negar recently won the NSF CAREER award. She was awarded the IEEE Intelligent Transportation Systems best Ph.D. dissertation award in 2020. Negar was recognized as a rising star in EECS, Aeronautics & Astronautics, and Civil and Environmental Engineering. She is also a recipient of the best student paper award at ITSC 2016.