Robot Learning via Cooperative and Adversarial Games

Friday, May 31, 2019, 11:00 am - 12:00 pm PDTiCal
10th floor conference room (1016)
This event is open to the public.
AI Seminar
Stefanos Nikolaidis, USC
Video Recording:
The goal of my research is to improve robot performance by integrating 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 actions, enabling autonomous systems to act optimally in a variety of real-world settings.
In this talk, I will first propose an equal partners model in collaborative manipulation tasks, where human and robot engage in a dance of inference and action, and I will 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, leading to superior performance compared to leader-follower settings. I will then generalize these models to domains where people may act in an adversarial manner. By leveraging human adversarial actions in an interactive learning framework,  we enable improved grasping success rate and robustness to disturbances. Human subjects experiments show how robust grasping strategies and coordination behaviors naturally emerge out of optimization processes, without being explicitly modeled. 
Stefanos Nikolaidis is an Assistant Professor of Computer Science at the University of Southern California. His research focuses on the mathematical foundations of human-robot interaction, drawing upon expertise on machine learning, algorithmic game theory and decision making under uncertainty. His work leads to end-to-end solutions that enable deployed robotic systems to act optimally when interacting with people in practical, real-world applications. Previously, Stefanos completed his PhD at Carnegie Mellon's Robotics Institute and received his MS from MIT. He has also a MEng from the University of Tokyo and a BS from the National Technical University of Athens. Stefanos has worked as a research associate at the University of Washington, as a research specialist at MIT and as a researcher at Square Enix in Tokyo. He has received a Best Enabling Technologies Paper Award from the IEEE/ACM International Conference on Human-Robot Interaction in 2015, a best paper nomination from the same conference in 2018 and was a best paper award finalist in the International Symposium on Robotics 2013.
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