  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.
