Retrospective Open-Vocabulary Memory for Long-Term Object Search

Abstract:Long-term object search requires learning where objects usually appear from repeated but uneven observations of a changing environment. We formulate retrospective open-vocabulary memory as probabilistic inference from censored observations, where the key idea is to reason with evidence per opportunity: a detection or non-detection should influence belief only in proportion to the robot's opportunity to observe the corresponding location. We introduce ECROM, which uses this principle to estimate long-term prevalence for concepts specified only at query time and converts the resulting belief directly into an active-search prior. To evaluate this problem, we introduce a controlled long-term benchmark in ten HM3D homes that independently varies object placement and observation opportunity across repeated traversals. ECROM improves support-level AP on held-out queries by 4.5 points and search SPL by 4.2 points over the strongest competing memory in each metric. The benchmark, dataset, and code will be open-sourced.

Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)

arXiv:2610.00330 [cs.RO]

(or arXiv:2610.00330v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2610.00330 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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