From autonomous cars in cities to mobile manipulators at home, robots must interact with people. What makes this hard is that human behavior—especially when interacting with other agents—is vastly complex, varying between individuals, environments, and over time. Thus, robots rely on data and machine learning throughout the design process and during deployment to build and refine models of humans. However, by blindly trusting their data-driven human models, today’s robots confidently plan unsafe behaviors around people, resulting in anything from miscoordination to dangerous collisions.

My research aims to ensure safety in human-robot interaction, particularly when robots learn from and about people. In this talk, I will discuss how treating robot learning algorithms as dynamical systems driven by human data enables safe human-robot interaction. I will first introduce a Bayesian monitor which infers online if the robot’s learned human model can evolve to well-explain observed human data. I will then discuss how a novel, control-theoretic problem formulation enables us to formally quantify what the robot could learn online from human data and how quickly it could learn it. Coupling these ideas with robot motion planning algorithms, I will demonstrate how robots can safely and automatically adapt their behavior based on how trustworthy their learned human models are. I will end this talk by taking a step back and raising the question: “What is the ‘right’ notion of safety when robots interact with people?” and discussing opportunities for how rethinking our notions of safety can capture more subtle aspects of human-robot interaction.