  We present a multi-modal trajectory generation and selection algorithm for
real-world mapless outdoor navigation in challenging scenarios with
unstructured off-road features like buildings, grass, and curbs. Our goal is to
compute suitable trajectories that (1) satisfy the environment-specific
traversability constraints and (2) generate human-like paths while navigating
in crosswalks, sidewalks, etc. Our formulation uses a Conditional Variational
Autoencoder (CVAE) generative model enhanced with traversability constraints to
generate multiple candidate trajectories for global navigation. We use VLMs and
a visual prompting approach with their zero-shot ability of semantic
understanding and logical reasoning to choose the best trajectory given the
contextual information about the task. We evaluate our methods in various
outdoor scenes with wheeled robots and compare the performance with other
global navigation algorithms. In practice, we observe at least 3.35%
improvement in traversability and 20.61% improvement in terms of human-like
navigation in generated trajectories in challenging outdoor navigation
scenarios.
