Mathematical Models of Adaptation in Human-Robot Collaboration
This work tackles the problem of improving human-robot collaboration by accounting for mutual adaptation, though it appears to be an incremental summary of existing methods rather than introducing new techniques.
This paper addresses the challenge of enabling robots to reason about human adaptation during collaboration by formulating it as a two-player game with incomplete information, demonstrating that probabilistic planning and game-theoretic algorithms lead to diverse and effective robot behaviors in real-time interactions with humans across manufacturing, personal robotics, and assistive care settings.
A robot operating in isolation needs to reason over the uncertainty in its model of the world and adapt its own actions to account for this uncertainty. Similarly, a robot interacting with people needs to reason over its uncertainty over the human internal state, as well as over how this state may change, as humans adapt to the robot. This paper summarizes our own work in this area, which depicts the different ways that probabilistic planning and game-theoretic algorithms can enable such reasoning in robotic systems that collaborate with people. We start with a general formulation of the problem as a two-player game with incomplete information. We then articulate the different assumptions within this general formulation, and we explain how these lead to exciting and diverse robot behaviors in real-time interactions with actual human subjects, in a variety of manufacturing, personal robotics and assistive care settings.