Cornell University –
Crowded human environments such as pedestrian scenes constitute challenging domains for mobile robots, for a variety of reasons including the heterogeneity of pedestrians’ decision making mechanisms, the lack of universal formal rules regulating traffic, the lack of channels of explicit communication with them, robot hardware limitations etc. State-of-the-art approaches for planning socially compliant robot motion tend to hard-code social norms or directly imitate observed human behaviors, thus exhibiting poor generalization to different environments and contexts, and often lack a thorough, in-depth validation, raising questions about reproducibility. To address these gaps, we have proposed a family of planning algorithms that employ mathematical abstractions from topology and physics to model multi-agent dynamics and follow design principles extracted from studies on human behavior. Our algorithms leverage the collaborative nature of human navigation as well as the human mechanisms of nonverbal communication and teleological inference to generate consistently intent-expressive and socially compliant robot motion in the context of a dynamic, multi-agent environment.
Evidence extracted from an online user study suggests that humans perceive the motion generated by our framework as more legible compared to the motion generated by two widely employed baselines. This is in parallel to our findings from extensive lab experiments suggesting that human motion is smoother when navigating around a robot running our algorithm. In this talk, I will present the mathematical and computational foundations of our approach,summarize our key findings and discuss potential directions for future work
Christoforos (Chris) Mavrogiannis is a final-year PhD candidate in Mechanical Engineering at Cornell University where he works with Prof. Ross A. Knepper at the Robotic Personal Assistants Lab. His research is focused on the design of planning algorithms for the generation of intent-expressive and socially compliant robot motion in crowded human environments. His work draws inspiration from psychology and sociology studies on human behavior and tools from topology, machine learning, path planning and human-robot interaction. In the past, he worked on the development of grasp planning algorithms for multi-fingered robot hands and on the mechanical design of adaptive robotic and prosthetic hands as a member of the open-source
initiative OpenBionics. Christoforos holds an MS in Mechanical Engineering from Cornell University (2016) and a Diploma in Mechanical Engineering from the National Technical
University of Athens (2013). He was a best paper finalist at the IEEE/ACM International Conference on Human-Robot Interaction and selected as a participant in the HRI and RSS Pioneers doctoral consortia.
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