  We present a novel learning-based trajectory generation algorithm for outdoor
robot navigation. Our goal is to compute collision-free paths that also satisfy
the environment-specific traversability constraints. Our approach is designed
for global planning using limited onboard robot perception in mapless
environments while ensuring comprehensive coverage of all traversable
directions. Our formulation uses a Conditional Variational Autoencoder (CVAE)
generative model that is enhanced with traversability constraints and an
optimization formulation used for the coverage. We highlight the benefits of
our approach over state-of-the-art trajectory generation approaches and
demonstrate its performance in challenging and large outdoor environments,
including around buildings, across intersections, along trails, and off-road
terrain, using a Clearpath Husky and a Boston Dynamics Spot robot. In practice,
our approach results in a 6% improvement in coverage of traversable areas and
an 89% reduction in trajectory portions residing in non-traversable regions.
Our video is here: https://youtu.be/3eJ2soAzXnU
