Mathematical Models of Adaptation in Human-Robot Collaboration
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-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.