能動物体探索に必要な世界モデルとは？検出誤り下での検討

原文アブストラクト

In active object search, a robot must decide where to look and when to stop. Imperfect detection complicates both decisions: a non-detection may indicate occlusion, an out-of-view target, or a false negative, while a detection may be a false positive. This raises the question of which representations a world model needs to support reliable search under imperfect detection. We introduce Bayesian Active Miss-aware Object Search (BAMOS) to investigate how target belief, occupancy belief, and detector errors affect search decisions. Through comprehensive ablations in Isaac Sim and HSSD houses, we examine the roles of these representations and their update mechanisms across different detector configurations. Notably, target belief and its update mechanisms are critical to search performance, with their contribution generally increasing as detection errors become more prevalent. These findings highlight the importance of maintaining and updating target beliefs to support reliable active search under uncertain detector observations, providing insights into the design of world models for embodied agents. Code is available in https://github.com/gokyeongryeol/BAMOS.