CRAFT-E：一种用于具身赋能力量接地的神经符号框架
CRAFT-E: A Neuro-Symbolic Framework for Embodied Affordance Grounding
AI总结：
CRAFT-E通过结合神经符号方法和具身感知，提供了一种可解释且可定制的框架，用于辅助机器人系统中基于赋能力量的物体选择。
摘要：
在无结构环境中运作的辅助机器人必须不仅理解物体是什么，还要理解它们能被用来做什么。这要求将基于语言的动作查询接地到既能提供请求功能又可以被物理获取的物体上。现有方法往往依赖于黑箱模型或固定的赋能力量标签，限制了透明性、可控性和可靠性，这对面向人类的应用来说是不利的。我们引入了CRAFT-E，一种模块化的神经符号框架，它将结构化的动词-属性-物体知识图与视觉-语言对齐和基于能量的抓取推理相结合。该系统生成可解释的接地路径，揭示影响物体选择的因素，并将抓取可行性作为赋能力量推断的一部分。我们进一步构建了一个具有统一标注的基准数据集，用于动词-物体兼容性、分割和抓取候选人的标注，并在物理机器人上部署了完整的流程。CRAFT-E在静态场景、基于ImageNet的功能检索和涉及20个动词和39个物体的真实世界试验中均实现了竞争性的性能。该框架在感知噪声下仍然具有鲁棒性，并提供了透明的、组件级的诊断信息。通过将符号推理与具身感知相结合，CRAFT-E为赋能力量接地的物体选择提供了一种可解释且可定制的替代方案，支持辅助机器人系统中的可信决策。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
