  Reasoning about large numbers of diverse plans to achieve high speed
navigation in cluttered environments remains a challenge for robotic systems
even in the case of perfect perceptual information. Often, this is tackled by
methods that iteratively optimize around a prior seeded trajectory and
consequently restrict to local optima. We present a novel planning method using
normalizing flows (NFs) to encode expert-styled motion primitives. We also
present an accelerated collision checking framework that enables rejecting
samples from the prior distribution before running them through the NF model
for rapid sampling of collision-free trajectories. The choice of an NF as the
generator permits a flexible way to encode diverse multi-modal behavior
distributions while maintaining a smooth relation to the input space which
allows approximating collision checks on NF inputs rather than outputs. We show
comparable performance to model predictive path integral control in random
cluttered environments and improved exit rates in a cul-de-sac environment. We
conclude by discussing our plans for future work to improve both safety and
performance of our controller.
