Abstract

Shared control is a key technology for various
robotic applications in which a robotic system and a human
operator are meant to collaborate efficiently. In order to achieve
efficient task execution in shared control, it is essential to
predict the desired behavior for a given situation or context
to simplify the control task for the human operator. To do this
prediction, we use Learning from Demonstration (LfD), which is
a popular approach for transferring human skills to robots. We
encode the demonstrated behavior as trajectory distributions
and generalize the learned distributions to new situations. The
goal of this paper is to present a shared control framework
that uses learned expert distributions to gain more autonomy.
Our approach controls the balance between the controller’s
autonomy and the human preference based on the distributions
of the demonstrated trajectories. Moreover, the learned
distributions are autonomously refined from collaborative task
executions, resulting in a master-slave system with increasing
autonomy that requires less user input with an increasing
number of task executions. We experimentally validated that
our shared control approach enables efficient task executions.
Moreover, the conducted experiments demonstrated that the
developed system improves its performances through interactive
task executions with our shared control.