Interleaved POMDP Planning Improves Multi-Object Search in Unknown Household Environments

Jul 14, 2026

A new algorithm, Inter-POMDP, addresses the challenge of multi-object search in unknown, multi-room household environments by decomposing the problem into two interacting planning levels: a high-level POUCT planner using LLM-informed beliefs about object locations, and a low-level motion planner that accounts for navigation uncertainty with obstacle-aware particle beliefs. Experiments in both simulation and real-world settings demonstrate that Inter-POMDP reduces collisions by up to 63%, navigation steps by up to 35%, and detection counts by up to 32% compared to baseline methods.

Why it matters: This work represents a significant advance in autonomous robot planning, enabling more efficient and safer multi-object search in complex, real-world household environments.

Full story at: arXiv Robotics ↗