ATOM: Abstract Transition and Option Modeling - Experience-Informed Hierarchical Navigation

Abstract:We propose ATOM (Abstract Transition and Option Modeling), a hierarchical navigation framework that combines higher-level graph planning with reusable lower-level option policies for efficient repeated navigation in large-scale 3D environments represented by hierarchical structures such as 3D scene graphs (3DSGs). ATOM operates on a hierarchical graph representation, implemented using, for instance, the Rooms and Places layers of a 3DSG, where each directed abstract transition is associated with a navigation option. For each transition, ATOM computes and stores a reusable option policy along with its state-dependent cost-to-go. Across repeated navigation tasks, ATOM maintains a smoothed empirical distribution over option initiation states and uses it to progressively refine the corresponding abstract transition cost. Ablation studies show that ATOM reduces cumulative repeated-planning time by 97.5% relative to a monolithic MDP baseline while maintaining trajectories within approximately 7% of a Global Places A* shortest-path reference. Experience-informed refinement further reduces abstract transition-cost estimation error by 10.3%. Physical experiments validate ATOM on an F1Tenth vehicle, successfully completing all five navigation queries with 43% of inter-region option executions reusing previously generated solutions.

8 pages, 12 figures. To be submitted to IEEE Robotics and Automation Letters (RA-L)

Robotics (cs.RO)

arXiv:2610.17555 [cs.RO]

(or arXiv:2610.17555v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2610.17555 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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