Princeton University

School of Engineering & Applied Science

Mutual Adaptation in Human-Robot Colalboration

Stefanos Nikolaidis, CMU Robotics Institute
E-Quad, B205
Tuesday, May 16, 2017 - 4:30pm

The goal of my research is to improve human-robot collaboration by integrating mathematical models of human behavior into robot decision making. I develop game-theoretic algorithms and probabilistic planning techniques that reason over the uncertainty in the human internal state and its dynamics, enabling autonomous systems to act optimally in a variety of real-world collaborative settings.

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. In this talk, I propose an equal-partners model, where human and robot engage in a dance of inference and action, and I focus on one particular instance of this dance: the robot adapts its own
actions via estimating the probability of the human adapting to the robot. I 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. I then examine more subtle forms of adaptation, where the human teammate adapts to the robot, without replicating the robot’s policy. I 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.

Stefanos Nikolaidis is a PhD Candidate at the Personal Robotics Lab in Carnegie Mellon's Robotics Institute, working with Prof. Siddhartha Srinivasa. His research lies at the intersection of human-robot interaction, algorithmic game-theory and planning under uncertainty. Stefanos develops decision-making algorithms that leverage mathematical models of human behavior to support deployed robotic systems in real-world collaborative settings. He has a MS from MIT, a MEng from the University of Tokyo and a BS from the National Technical University of Athens. He has additionally worked as a research specialist at MIT and as a researcher at Square-Enix in Tokyo. He has received a Best Enabling Technologies Award from the IEEE/ACM International Conference on Human-Robot Interaction and was a Best Paper Award Finalist in the International Symposium on Robotics.
This seminar is supported by the Korhammer Lecture Fund