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 algorithmic human-robot interaction, and he often draws tools from topology, motion planning, and machine learning. In parallel, he is leading MuSHR (mushr.io), the University of Washington’s open-source autonomous racecar project. In the past, Chris has been a best paper award finalist at the ACM/IEEE International Conference on Human-Robot Interaction (HRI), a finalist for the Hackaday Prize and a winner of the Robotdalen International Innovation Award. He has also been selected as Pioneer at the HRI and RSS conferences. Chris holds MS and PhD degrees from Cornell University, and a Diploma in mechanical engineering from the National Technical University of Athens.
Pedestrian scenes pose great challenges for robots due to the lack of formal rules regulating traffic and the lack of explicit coordination among multiple navigating agents. 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 navigation in pedestrian domains and show how their machinery may transfer to additional challenging environments such as uncontrolled street intersections.
Papers (not necessarily covered during the talk but more like a relevant reading list)
J. Roh, C. Mavrogiannis, R. Madan, D. Fox, S.S. Srinivasa, “Multimodal Trajectory Prediction via Topological Invariance for Navigation at Uncontrolled Intersections”, Conference on Robot Learning (CoRL). 2020.
C. Mavrogiannis, R. A. Knepper. “Multi-Agent Path Topology in Support of Socially Competent Navigation Planning”, The International Journal of Robotics Research. 2019.
C. Mavrogiannis, A. Hutchinson, J. Macdonald, P. Alves-Oliveira, R. A. Knepper. “Effects of Distinct Robot Navigation Strategies on Human Behavior in a Crowded Environment”. International Conference on Human-Robot Interaction (HRI). 2019.
C. Mavrogiannis, W. Thomason, R. A. Knepper, “Social Momentum: A Framework for Legible Navigation in Dynamic Multi-Agent Environments”. International Conference on Human-Robot Interaction (HRI). 2018.
C. Mavrogiannis and R. A. Knepper, “Multi-Agent Trajectory Prediction and Generation with Topological Invariants Enforced by Hamiltonian Dynamics”. Algorithmic Foundations of Robotics XIII (WAFR 2018). 2018.