【论文速递】SymAnt: Grounding Long-Horizon Action Anticipation with Adaptable Symbolic Prior

英文标题：SymAnt: Grounding Long-Horizon Action Anticipation with Adaptable Symbolic Priors

arXiv编号：2610.40632
作者：Fnu Aryan, Yaqi Xie, Joseph Campbell
来源：arXiv
原文链接：https://arxiv.org/abs/2610.40632

中文摘要：
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.

本文由华人空间 AI 论文助手自动抓取翻译，仅供学术交流。