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.

研究人机协作中的不完全适应现象，提出一种基于博弈论的人机部分适应模型，实验结果显示该模型显著提高了人机团队的性能。