  In robotics, it is essential to be able to plan efficiently in
high-dimensional continuous state-action spaces for long horizons. For such
complex planning problems, unguided uniform sampling of actions until a path to
a goal is found is hopelessly inefficient, and gradient-based approaches often
fall short when the optimization manifold of a given problem is not smooth. In
this paper we present an approach that guides the search of a state-space
planner, such as A*, by learning an action-sampling distribution that can
generalize across different instances of a planning problem. The motivation is
that, unlike typical learning approaches for planning for continuous action
space that estimate a policy, an estimated action sampler is more robust to
error since it has a planner to fall back on. We use a Generative Adversarial
Network (GAN), and address an important issue: search experience consists of a
relatively large number of actions that are not on a solution path and a
relatively small number of actions that actually are on a solution path. We
introduce a new technique, based on an importance-ratio estimation method, for
using samples from a non-target distribution to make GAN learning more
data-efficient. We provide theoretical guarantees and empirical evaluation in
three challenging continuous robot planning problems to illustrate the
effectiveness of our algorithm.
