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An outstanding challenge with safety methods for human-robot interaction is reducing their conservatism while maintaining robustness to variations in human behavior. In this talk, I discuss how robots like autonomous cars can leverage online data of human-robot interaction to modulate the conservatism of their safety monitors, automatically shifting between robust zero-sum models to general-sum game-theoretic models of human interaction.
Andrea Bajcsy is a postdoctoral scholar at UC Berkeley in the Electrical Engineering and Computer Science Department and an incoming Assistant Professor at the Robotics Institute at CMU (starting Fall 2023). She studies safe human-robot interaction, particularly when robots learn from and learn about people. Her research unites traditionally disparate methods from control theory and machine learning to develop theoretical frameworks and practical algorithms for human-robot interaction in domains like assistive robotic arms, quadrotors, and autonomous cars. Andrea received her Ph.D. in Electrical Engineering & Computer Science from UC Berkeley and B.S. in Computer Science at the University of Maryland, College Park. Her research has been featured in NBC news, WIRED magazine, and the Robohub podcast. She is the recipient of an Honorable Mention for the T-RO Best Paper Award, the NSF Graduate Research Fellowship, UC Berkeley Chancellor’s Fellowship, and has worked at NVIDIA Research and Max Planck Institute for Intelligent Systems.