Human-Robot Mutual Adaptation in Shared Autonomy
This work addresses the challenge of balancing guidance and trust in shared autonomy for human-robot collaboration, representing an incremental advance with a novel method for a known bottleneck.
The paper tackled the problem of improving human-robot team performance in shared autonomy by developing a mutual adaptation formalism that allows the robot to guide adaptable humans toward effective strategies while complying with stubborn humans to retain trust. The result showed improved team performance and high user trust compared to robots strictly following human preferences, as demonstrated in a human subject experiment.
Shared autonomy integrates user input with robot autonomy in order to control a robot and help the user to complete a task. Our work aims to improve the performance of such a human-robot team: the robot tries to guide the human towards an effective strategy, sometimes against the human's own preference, while still retaining his trust. We achieve this through a principled human-robot mutual adaptation formalism. We integrate a bounded-memory adaptation model of the human into a partially observable stochastic decision model, which enables the robot to adapt to an adaptable human. When the human is adaptable, the robot guides the human towards a good strategy, maybe unknown to the human in advance. When the human is stubborn and not adaptable, the robot complies with the human's preference in order to retain their trust. In the shared autonomy setting, unlike many other common human-robot collaboration settings, only the robot actions can change the physical state of the world, and the human and robot goals are not fully observable. We address these challenges and show in a human subject experiment that the proposed mutual adaptation formalism improves human-robot team performance, while retaining a high level of user trust in the robot, compared to the common approach of having the robot strictly following participants' preference.