SymAnt：以可适配符号先验为基础的长时程动作预测
SymAnt: Grounding Long-Horizon Action Anticipation with Adaptable Symbolic Priors
Fnu Aryan, Yaqi Xie, Joseph Campbell, Woojun Kim, Kwonjoon Lee, Hossein Nourkhiz Mahjoub, Ehsan Moradi-Pari, Katia Sycara 等 9 位作者
AI 速览：SymAnt 用神经-符号方法结合场景图与知识图构建符号上下文，从短观测窗口迭代生成并精炼长时程未来动作序列。
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.