  We consider the problem of completing a set of $n$ tasks with a human-robot
team using minimum effort. In many domains, teaching a robot to be fully
autonomous can be counterproductive if there are finitely many tasks to be
done. Rather, the optimal strategy is to weigh the cost of teaching a robot and
its benefit -- how many new tasks it allows the robot to solve autonomously. We
formulate this as a planning problem where the goal is to decide what tasks the
robot should do autonomously (act), what tasks should be delegated to a human
(delegate) and what tasks the robot should be taught (learn) so as to complete
all the given tasks with minimum effort. This planning problem results in a
search tree that grows exponentially with $n$ -- making standard graph search
algorithms intractable. We address this by converting the problem into a mixed
integer program that can be solved efficiently using off-the-shelf solvers with
bounds on solution quality. To predict the benefit of learning, we propose a
precondition prediction classifier. Given two tasks, this classifier predicts
whether a skill trained on one will transfer to the other. Finally, we evaluate
our approach on peg insertion and Lego stacking tasks, both in simulation and
real-world, showing substantial savings in human effort.
