  In this paper, we present a method of multi-robot motion planning by biasing
centralized, sampling-based tree search with decentralized, data-driven steer
and distance heuristics. Over a range of robot and obstacle densities, we
evaluate the plain Rapidly-expanding Random Trees (RRT), and variants of our
method for double integrator dynamics. We show that whereas plain RRT fails in
every instance to plan for $4$ robots, our method can plan for up to 16 robots,
corresponding to searching through a very large 65-dimensional space, which
validates the effectiveness of data-driven heuristics at combating exponential
search space growth. We also find that the heuristic information is
complementary; using both heuristics produces search trees with lower failure
rates, nodes, and path costs when compared to using each in isolation. These
results illustrate the effective decomposition of high-dimensional joint-space
motion planning problems into local problems.
