どこを見るべきかを知る：空間・意味融合による対話型物体探索
Knowing Where to Look: Interactive Object Search via Spatial-Semantic Fusion
VLMの意味的な尤もらしさと幾何的な占有証拠をベイズフィルタで3Dボクセル信念場に統合し、視点変更・移動・棚開けなどの行動選択を行う物体探索システムを構築、双腕移動ヒューマノイドで実証した。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
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著者: Aditya Agarwal, Lucy Cai, Kelly Lu, Travis Manderson, Nicholas Roy, Tomás Lozano-Pérez, Leslie Pack Kaelbling
分類: cs.RO
原文アブストラクト
When searching for an object, humans naturally combine semantic intuition - reasoning about where the object is likely to be stored - with geometric awareness of what parts of the space are visible, occluded, or unexplored. Effective robot object search requires the same joint reasoning, yet existing methods rely on only one: geometric methods maintain principled spatial beliefs but lack deep semantic understanding, while language-model based methods leverage commonsense knowledge but maintain no explicit belief over space. We argue that jointly reasoning over semantic and geometric evidence in an explicit 3D belief enables human-like interactive object search that outperforms either modality alone. To test this claim, we build a system that accumulates per-object VLM plausibility scores and geometric occupancy evidence via Bayesian log-odds filtering into a unified 3D voxel belief field. High-belief regions in the combined belief field drive action selection at each step: taking a closer look from a different viewpoint, navigating toward an unexplored region of the workspace, or physically interacting with a closed cabinet. We deploy this system end-to-end on a bimanual mobile humanoid and find that it outperforms geometry-only search and LLM-based planning methods. Project website - https://skymanaditya1.github.io/objsearch-website/