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

• URL: http://arxiv.org/abs/2607.10437v1
• Date: Sat, 11 Jul 2026 18:43:02 GMT
• Title: Interleaved POMDP Planning for Multi-Object Search in Unknown Multi-Room Household Environments
• Abstract summary: Multi-object search in unknown household environments requires planning under extensive uncertainty.<n> POMDPs offer a principled framework for such problems but remain intractable in large domains.<n>We propose Inter-POMDP, a novel interleaved POMDP planning algorithm.<n>Our 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.
• Score: 9.077274919759326
• License: http://creativecommons.org/licenses/by/4.0/
• 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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