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We present a novel learning-based trajectory generation algorithm for outdoorrobot navigation. Our goal is to compute collision-free paths that also satisfythe environment-specific traversability constraints. Our approach is designedfor global planning using limited onboard robot perception in maplessenvironments, while ensuring comprehensive coverage of all traversabledirections. Our formulation uses a Conditional Variational Autoencoder (CVAE)generative model that is enhanced with traversability constraints and anoptimization formulation used for the coverage. We highlight the benefits ofour approach over state-of-the-art trajectory generation approaches anddemonstrate its performance in challenging and large outdoor environments,including around buildings, across intersections, along trails, and off-roadterrain, using a Clearpath Husky and a Boston Dynamics Spot robot. In practice,our approach results in a 6% improvement in coverage of traversable areas andan 89% reduction in trajectory portions residing in non-traversable regions.Our video is here: https: //youtu.be/OT0q4ccGHts

MTG: Mapless Trajectory Generator with Traversability Coverage for Outdoor Navigation