Formalizing the Structure of Multiagent Domains for Autonomous Robot Navigation in Human Spaces

Christoforos Mavrogiannis, University of Washington


Location: 122 Gates Hall

Time: 2:40p.m.

Abstract: Pedestrian scenes pose great challenges for robots due to the lack of formal rules regulating traffic, the lack of explicit coordination among agents, and the high dimensionality of the underlying space of outcomes. However, humans navigate with ease and comfort through a variety of complex multiagent environments, such as busy train stations, crowded malls or academic buildings. Human effectiveness in such domains can be largely attributed to cooperation, which introduces structure to multiagent behavior. In this talk, I will discuss how we can formalize this structure through the use of representations from low-dimensional topology. I will describe how these representations can be used to build prediction and planning algorithms for socially compliant robot navigation in pedestrian domains and show how their machinery may transfer to additional challenging environments such as uncontrolled street intersections.

Bio: Christoforos (Chris) Mavrogiannis is a postdoctoral research associate in the Paul G. Allen School of Computer Science & Engineering at the University of Washington, working with Prof. Siddhartha Srinivasa. His interests lie at the intersection of motion planning, multiagent systems, and human-robot interaction. He is particularly interested in the design and evaluation of algorithms for multiagent domains in human environments. To this end, he employs tools from motion planning and machine learning, and often seeks insights from (algebraic) topology and social sciences. Chris has been a best-paper award finalist at the ACM/IEEE International Conference on Human-Robot Interaction (HRI), and selected as a Pioneer at the HRI and RSS conferences. He has also led open-source initiatives (Openbionics, MuSHR), for which he has been a finalist for the Hackaday Prize and a winner of the Robotdalen International Innovation Award. Chris holds M.S. and Ph.D. degrees from Cornell University, and a Diploma in mechanical engineering from the National Technical University of Athens.