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HTCE: Topological Cross-Scene Experience Reuse for Object Navigation

HTCE is a homeomorphism-inspired framework that canonicalizes and reuses navigation experience across scenes for embodied object-goal navigation. It compresses stored route descriptions by 17.7–23.6% without performance loss and achieves state-of-the-art success and SPL on HM3D and MP3D benchmarks under a frozen vision-language policy. The method improves success rate by up to 4.9 points and SPL by up to 2.8 points.

AI Brief

This paper presents a novel topological approach to experience reuse in embodied AI, with concrete benchmark gains and open-source code, offering practical value for navigation research.

arXiv:2611.04836v1 Announce Type: new Abstract: As embodied agents perform object-goal navigation across residential environments, they accumulate successful experiences whose semantic structure recurs across scenes even when geometric trajectories do not. Reusing such experience is challenging because differences in region naming and observation granularity produce distinct graph representations of similar approaches, while meaningful differences in anchors, branches, and endpoints must stay distinguishable. We introduce HTCE, a homeomorphism-inspired topological framework that canonicalizes and reuses navigation experience. Feature and topology subdivisions characterize representational variations, and their semantically constrained reductions produce comparable semantic sequences and structural signatures while preserving semantic anchors, branching relations, and navigation endpoints. HTCE then retrieves historical successful approaches through subdivision-aware matching as guidance for a frozen vision-language policy. Canonicalization compresses the experience index losslessly, merging 17.7--23.6\% of distinct stored route descriptions while retaining full task performance. Under a single frozen vision-language backbone shared by all compared systems, HTCE attains the best SR and SPL in every setting---59.9\%/31.8\% on HM3D v0.1, 71.0\%/31.5\% on HM3D v0.2, and 46.6\%/16.6\% on MP3D---with SR gains of up to 4.9 points and structured retrieval contributing up to 2.8 SPL points. The code is available in https://github.com/startracker0/htce.git
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