  A mobility map, which provides maximum achievable speed on a given terrain,
is essential for path planning of autonomous ground vehicles in off-road
settings. While physics-based simulations play a central role in creating
next-generation, high-fidelity mobility maps, they are cumbersome and
expensive. For instance, a typical simulation can take weeks to run on a
supercomputer and each map requires thousands of such simulations. Recent work
at the U.S. Army CCDC Ground Vehicle Systems Center has shown that trained
machine learning classifiers can greatly improve the efficiency of this
process. However, deciding which simulations to run in order to train the
classifier efficiently is still an open problem. According to PAC learning
theory, data that can be separated by a classifier is expected to require
$\mathcal{O}(1/\epsilon)$ randomly selected points (simulations) to train the
classifier with error less than $\epsilon$. In this paper, building on existing
algorithms, we introduce an active learning paradigm that substantially reduces
the number of simulations needed to train a machine learning classifier without
sacrificing accuracy. Experimental results suggest that our sampling algorithm
can train a neural network, with higher accuracy, using less than half the
number of simulations when compared to random sampling.
