用分层规划解决未知多房间环境下的多物搜索问题。
Interleaved POMDP Planning for Multi-Object Search in Unknown Multi-Room Household Environments
- 高阶用LLM增强的直方图信念，低阶用障碍物感知粒子信念
- 实测碰撞减少63%，导航步数减少35%，探测次数减少32%
- 适合复杂未知家居环境中的机器人搜索任务
在未知家庭环境中进行多物体搜索需应对极大不确定性——包括物体位置未知、空间杂乱且存在未观测障碍物。部分可观测马尔可夫决策过程（POMDP）虽为该类问题提供理论框架，但在大规模场景中仍难求解。本文提出Inter-POMDP，一种新型分层式POMDP规划算法，将挑战分解为两个交互层级：高阶POUCT规划器基于LLM启发的直方图信念推理物体分布；低阶运动规划器则利用障碍物感知的粒子信念建模导航不确定性，作为领域知识指导高阶规划。该分层设计在未知多房间大空间下兼顾了规划质量与效率。仿真与真实实验表明，相比基线方法，Inter-POMDP使碰撞次数减少最高达63%，导航步数减少最多35%，检测次数减少最多32%。完整视频见https://sites.google.com/view/inter-pomdp。
原文摘要 · Abstract (English)
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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