SymAnt: 適応可能な記号的事前知識で長期的行動予測を接地する
SymAnt: Grounding Long-Horizon Action Anticipation with Adaptable Symbolic Priors
短い動画からシーングラフと知識グラフを統合した記号的文脈を構築し、それを事前知識として未来の行動系列を反復的に予測するニューロシンボリック手法を提案した。
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: Fnu Aryan, Yaqi Xie, Joseph Campbell, Woojun Kim, Kwonjoon Lee, Hossein Nourkhiz Mahjoub, Ehsan Moradi-Pari, Katia Sycara, Simon Stepputtis
分類: cs.RO, cs.CV
原文アブストラクト
As robots and intelligent systems increasingly interact with humans, they must anticipate long sequences of future actions from only brief observations. While purely neural models typically require extensive observations to predict future actions and remain difficult to interpret and adjust, symbolic priors offer a structured alternative. To this end, we introduce SymAnt, a novel neuro-symbolic approach that unifies the expressiveness of neural networks with the structured reasoning of scene and knowledge graphs. Given a short video, SymAnt performs a joint-search over both graphs to construct a symbolic context, enabling unified reasoning over spatial relationships, object attributes, affordances, and associated actions. This symbolic context serves as a prior for an iterative refinement process that progressively generates and improves the sequence of future actions. By grounding predictions in symbolic context, SymAnt anticipates long-horizon actions from short observation windows in a parameter-efficient manner, while enabling users to directly edit the underlying knowledge to adjust predictions without further training. Through our experiments, we demonstrate the effectiveness of SymAnt, particularly when only short observation contexts are available, across five diverse datasets: Breakfast, 50 Salads, EPIC-KITCHENS, EGTEA Gaze+, and Assembly101.