Game-Theoretic Modeling of Human Adaptation in Human-Robot Collaboration
This addresses the challenge of optimizing human-robot collaboration by modeling human learning and adaptation, though it is incremental as it builds on existing game-theoretic frameworks.
The paper tackles the problem of inaccurate human models of robot capabilities in human-robot teams by proposing a game-theoretic model of human partial adaptation, where the human's reward function changes stochastically over time. It demonstrates through a human subject experiment that this model significantly improves team performance compared to policies assuming complete human adaptation.
In human-robot teams, humans often start with an inaccurate model of the robot capabilities. As they interact with the robot, they infer the robot's capabilities and partially adapt to the robot, i.e., they might change their actions based on the observed outcomes and the robot's actions, without replicating the robot's policy. We present a game-theoretic model of human partial adaptation to the robot, where the human responds to the robot's actions by maximizing a reward function that changes stochastically over time, capturing the evolution of their expectations of the robot's capabilities. The robot can then use this model to decide optimally between taking actions that reveal its capabilities to the human and taking the best action given the information that the human currently has. We prove that under certain observability assumptions, the optimal policy can be computed efficiently. We demonstrate through a human subject experiment that the proposed model significantly improves human-robot team performance, compared to policies that assume complete adaptation of the human to the robot.