CRAFT-E: 身体性を持つアフォーダンス接地のためのニューロシンボリックフレームワーク
CRAFT-E: A Neuro-Symbolic Framework for Embodied Affordance Grounding
動詞と物体の知識グラフ、視覚言語アラインメント、把持可能性推論を組み合わせ、指示された動作に適した物体を解釈可能に選択するニューロシンボリック手法を提案し、実機で検証した。
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？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
※ AIが要旨から生成した要約です。正確性は原文をご確認ください。
著者: Zhou Chen, Joe Lin, Carson Bulgin, Sathyanarayanan N. Aakur
分類: cs.RO, cs.AI
原文アブストラクト
Assistive robots operating in unstructured environments must understand not only what objects are, but what they can be used for. This requires grounding language-based action queries to objects that both afford the requested function and can be physically retrieved. Existing approaches often rely on black-box models or fixed affordance labels, limiting transparency, controllability, and reliability for human-facing applications. We introduce CRAFT-E, a modular neuro-symbolic framework that composes a structured verb-property-object knowledge graph with visual-language alignment and energy-based grasp reasoning. The system generates interpretable grounding paths that expose the factors influencing object selection and incorporates grasp feasibility as an integral part of affordance inference. We further construct a benchmark dataset with unified annotations for verb-object compatibility, segmentation, and grasp candidates, and deploy the full pipeline on a physical robot. CRAFT-E achieves competitive performance in static scenes, ImageNet-based functional retrieval, and real-world trials involving 20 verbs and 39 objects. The framework remains robust under perceptual noise and provides transparent, component-level diagnostics. By coupling symbolic reasoning with embodied perception, CRAFT-E offers an interpretable and customizable alternative to end-to-end models for affordance-grounded object selection, supporting trustworthy decision-making in assistive robotic systems.
