  Anytime motion planners are widely used in robotics. However, the
relationship between their solution quality and computation time is not well
understood, and thus, determining when to quit planning and start execution is
unclear. In this paper, we address the problem of deciding when to stop
deliberation under bounded computational capacity, so called meta-reasoning,
for anytime motion planning. We propose data-driven learning methods,
model-based and model-free meta-reasoning, that are applicable to different
environment distributions and agnostic to the choice of anytime motion
planners. As a part of the framework, we design a convolutional neural
network-based optimal solution predictor that predicts the optimal path length
from a given 2D workspace image. We empirically evaluate the performance of the
proposed methods in simulation in comparison with baselines.
