1 research outputs found
Everybody Needs Somebody Sometimes: Validation of Adaptive Recovery in Robotic Space Operations
This work assesses an adaptive approach to fault
recovery in autonomous robotic space operations, which uses indicators of opportunity, such as physiological state measurements
and observations of past human assistant performance, to inform
future selections. We validated our reinforcement learning approach using data we collected from humans executing simulated
mission scenarios. We present a method of structuring humanfactors experiments that permits collection of relevant indicator
of opportunity and assigned assistance task performance data, as
well as evaluation of our adaptive approach, without requiring
large numbers of test subjects. Application of our reinforcement
learning algorithm to our experimental data shows that our adaptive assistant selection approach can achieve lower cumulative
regret compared to existing non-adaptive baseline approaches
when using real human data. Our work has applications beyond
space robotics to any application where autonomy failures may
occur that require external intervention