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arXiv · 2607.10437

Interleaved POMDP Planning for Multi-Object Search in Unknown Multi-Room Household Environments

Ruochu Yang·Ziyi Xia·Huibo Zhang·Yatong Han·Yiming Zhao·Yingke Li·Fumin Zhang·Yorai Wardi·Mengxue Hou
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Abstract

Multi-object search in unknown household environments requires planning under extensive uncertainty - from unknown object locations to cluttered spaces with unobserved obstacles. POMDPs offer a principled framework for such problems but remain intractable in large domains. We propose Inter-POMDP, a novel interleaved POMDP planning algorithm that decomposes this challenge into two interacting levels: a high-level POUCT planner reasons over object distributions using LLM-informed histogram beliefs, while a low-level motion planner models navigation uncertainty with obstacle-aware particle beliefs as domain knowledge to guide high-level POUCT. This interleaved design balances planning quality and efficiency despite the large search space across unknown multi-room environments. Both simulation and real-world experiments show that our Inter-POMDP algorithm reduces collision counts by up to 63%, navigation steps by up to 35%, and detection counts by up to 32% compared with baseline methods. Full videos are https://sites.google.com/view/inter-pomdp

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Ruochu Yang, Ziyi Xia, Huibo Zhang, Yatong Han, Yiming Zhao, Yingke Li, Fumin Zhang, Yorai Wardi, Mengxue Hou. 2026-07-11. Interleaved POMDP Planning for Multi-Object Search in Unknown Multi-Room Household Environments. https://arxiv.org/abs/2607.10437

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