CAVEAT: Recurrent Multimodal Diffusion Planning for Mapless Aerial Exploration

Abstract:Can exploratory UAV waypoint sequences be generated from multimodal onboard observations and a fixed-dimensional recurrent internal state without maintaining a persistent global map in the deployed policy? We investigate this question through CAVEAT, a diffusion policy conditioned on a recurrent internal state updated from fused LiDAR, visual, and pose features and trained from trajectories generated by the map-based FUELv2 expert. Rolling inference partially warm-starts consecutive predictions, while a temporary local signed distance field provides heuristic obstacle guidance. Simulation results evaluate both inference mechanisms and compare CAVEAT with its demonstration-generating expert. Proof-of-concept experiments on a Flyability Elios 3 demonstrate partial exploration of a previously unseen indoor environment and target-directed visual servoing using a separately trained policy.

Robotics (cs.RO); Machine Learning (cs.LG)

arXiv:2609.06623 [cs.RO]

(or arXiv:2609.06623v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.06623 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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