  Shared autonomy methods, where a human operator and a robot arm work
together, have enabled robots to complete a range of complex and highly
variable tasks. Existing work primarily focuses on one human sharing autonomy
with a single robot. By contrast, in this paper we present an approach for
multi-robot shared autonomy that enables one operator to provide real-time
corrections across two coordinated robots completing the same task in parallel.
Sharing autonomy with multiple robots presents fundamental challenges. The
human can only correct one robot at a time, and without coordination, the human
may be left idle for long periods of time. Accordingly, we develop an approach
that aligns the robot's learned motions to best utilize the human's expertise.
Our key idea is to leverage Learning from Demonstration (LfD) and time warping
to schedule the motions of the robots based on when they may require
assistance. Our method uses variability in operator demonstrations to identify
the types of corrections an operator might apply during shared autonomy,
leverages flexibility in how quickly the task was performed in demonstrations
to aid in scheduling, and iteratively estimates the likelihood of when
corrections may be needed to ensure that only one robot is completing an action
requiring assistance. Through a preliminary study, we show that our method can
decrease the scheduled time spent sanding by iteratively estimating the times
when each robot could need assistance and generating an optimized schedule that
allows the operator to provide corrections to each robot during these times.
