| Summary: | Shared autonomy systems allow robots to either operate
autonomously or request assistance from a human operator. In
such settings, the human operator may exhibit sub-optimal behaviours, influenced by latent variables such as attention level or
task proficiency. In this paper, we consider shared autonomy systems composed of multiple robots and one human. In this setting,
we aim to synthesise a controller that selects, at each decision step,
the actions to be taken by each robot and which (if any) robot the
human operator should assist. To efficiently allocate the human
operator to a robot at any given time, we propose a controller that
reasons about the uncertainty over the latent variables impacting
the human operator’s performance. To ensure scalability, we use
an online bidding system, where each robot plans while considering its belief over the human’s performance, and bids according to
the direct benefit of human assistance and how much information
will be gained by the system about the human. We experiment on
two domains, where we outperform approaches for allocation of
human assistance that do not consider the human’s latent variables,
and show that the performance of the overall system increases
when robots consider the information gained by requesting human
assistance when bidding.
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