  We consider robot learning in the context of shared autonomy, where control
of the system can switch between a human teleoperator and autonomous control.
In this setting we address reinforcement learning, and learning from
demonstration, where there is a cost associated with human time. This cost
represents the human time required to teleoperate the robot, or recover the
robot from failures. For each episode, the agent must choose between requesting
human teleoperation, or using one of its autonomous controllers. In our
approach, we learn to predict the success probability for each controller,
given the initial state of an episode. This is used in a contextual multi-armed
bandit algorithm to choose the controller for the episode. A controller is
learnt online from demonstrations and reinforcement learning so that autonomous
performance improves, and the system becomes less reliant on the teleoperator
with more experience. We show that our approach to controller selection reduces
the human cost to perform two simulated tasks and a single real-world task.
