長期物体探索のための回顧的オープンボキャブラリメモリ
Retrospective Open-Vocabulary Memory for Long-Term Object Search
ロボットが繰り返し観測する中で「観測の機会」に応じて物体の存在確率を推定し、クエリ時に指定された概念の長期出現傾向を能動探索の事前分布に変換する手法ECROMを提案。
著者: Jiaming Wang, Zhiwei Xue, Chen Jizhuo, Peng Shiqi, Harold Soh
分類: cs.RO, cs.CV
原文アブストラクト
Long-term object search requires learning where objects usually appear from repeated but uneven observations of a changing environment. We formulate retrospective open-vocabulary memory as probabilistic inference from censored observations, where the key idea is to reason with evidence per opportunity: a detection or non-detection should influence belief only in proportion to the robot's opportunity to observe the corresponding location. We introduce ECROM, which uses this principle to estimate long-term prevalence for concepts specified only at query time and converts the resulting belief directly into an active-search prior. To evaluate this problem, we introduce a controlled long-term benchmark in ten HM3D homes that independently varies object placement and observation opportunity across repeated traversals. ECROM improves support-level AP on held-out queries by 4.5 points and search SPL by 4.2 points over the strongest competing memory in each metric. The benchmark, dataset, and code will be open-sourced.