Abstract:
While much work in human-robot interaction has focused on leader- follower teamwork models, the recent advancement of robotic systems that have access to vast amounts of information suggests the need for robots that take into account the quality of the human decision making and actively guide people towards better ways of doing their task. This thesis proposes an equal-partners model, where human and robot engage in a dance of inference and action, and focuses on one particular instance of this dance: the robot adapts its own actions via estimating the probability of the human adapting to the robot. We start with a bounded-memory model of human adaptation parameterized by the human adaptability – the probability of the human switching towards a strategy newly demonstrated by the robot. We then examine more subtle forms of adaptation, where the human teammate adapts to the robot, without replicating the robot’s policy. We model the interaction as a repeated game, and present an optimal policy computation algorithm that has complexity linear to the number of robot actions. Integrating these models into robot action selection allows for human-robot mutual-adaptation. Human subject experiments in a variety of collaboration and shared-autonomy settings show that mutual adaptation significantly improves human-robot team performance, compared to one-way robot adaptation to the human.

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Thesis Committee Members:
Siddhartha Srinivasa, Chair
Jodi Forlizzi
Emma Brunskill
Ariel Procaccia
David Hsu, National University of Singapore