  A robot can invoke heterogeneous computation resources such as CPUs, cloud
GPU servers, or even human computation for achieving a high-level goal. The
problem of invoking an appropriate computation model so that it will
successfully complete a task while keeping its compute and energy costs within
a budget is called a model selection problem. In this paper, we present an
optimal solution to the model selection problem with two compute models, the
first being fast but less accurate, and the second being slow but more
accurate. The main insight behind our solution is that a robot should invoke
the slower compute model only when the benefits from the gain in accuracy
outweigh the computational costs. We show that such cost-benefit analysis can
be performed by leveraging the statistical correlation between the accuracy of
fast and slow compute models. We demonstrate the broad applicability of our
approach to diverse problems such as perception using neural networks and safe
navigation of a simulated Mars rover.
