Event

While much work in human-robot interaction has focused on leader-assistant 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 propose data-driven models that capture subtler forms of adaptation, where the human teammate updates their expectations of the robot’s capabilities through interaction. Integrating these models into robot decision making allows for human-robot mutual adaptation, where coordination strategies, informative actions and trustworthy behavior are not explicitly modeled, but naturally emerge out of optimization processes. Human subjects 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.

Biography
Stefanos Nikolaidis is a Research Associate at the University of Washington in Department of Computer Science Engineering, working with Prof. Siddhartha Srinivasa.

He graduated in December 2017 with a PhD from Personal Robotics Lab, CMU Robotics Institute, advised by Prof. Siddhartha Srinivasa. Before coming to CMU, he received an MS and worked as a Research Specialist in the Interactive Robotics Group, MIT, advised by Prof. Julie Shah. 

His research lies at the intersection of human-robot interaction, game-theory and robot planning under uncertainty.  He draws upon insights from studies in economics, cognitive behavioral psychology and human team coordination, to develop mathematical models of human behavior and integrate them into robot decision making in a principled way. Ultimately,  his research is motivated by real world problems, thus he believes strongly in the importance of models that are scalable and robust, supporting deployed systems in real world applications.