Interleaved POMDP Planning for Multi-Object Search in Unknown Multi-Room Household Environments
2026-07-11 • Robotics
Robotics
AI summaryⓘ
The authors created a new method called Inter-POMDP to help robots search for objects in unknown homes more efficiently. They split the problem into two parts: one that plans where objects might be based on language model predictions, and another that handles moving around obstacles carefully. This combined approach helps the robot plan better routes and avoid crashes. Their tests showed Inter-POMDP reduced collisions, travel steps, and the number of times objects had to be checked compared to other methods.
POMDPPOUCTParticle BeliefsLarge Language ModelsMulti-object SearchNavigation PlanningObstacle AvoidanceHistogram BeliefsRobot Motion PlanningUncertainty
Authors
Ruochu Yang, Ziyi Xia, Huibo Zhang, Yatong Han, Yiming Zhao, Yingke Li, Fumin Zhang, Yorai Wardi, Mengxue Hou
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