用知识图谱让机器人解释为何能通过或受阻，更懂人类环境。
Ontology-Guided Reasoning for Affordance-Based Explanations of Robot Navigation
- 构建局部可用性本体，整合物体、功能与空间关系。
- 在生成场景中比纯语义模型更准识别解释因素。
- 适合需要可解释自主决策的机器人应用。
本文提出基于本体的可用性推理机制，用于解释机器人导航行为。在人类环境中，仅检测路径受阻不够，还需推断附近物体的功能、可能的状态变化及哪些改变能安全继续前进。为此，我们构建了一个局部可用性本体，包含邻近实体、其可用性、状态及定性空间关系，并评估假设的物体-可用性状态变化作为解释因子。该方法生成不仅语义上可理解，且具行动指导意义的解释。我们在一个轻量级机器人图书管理员场景基准上实现并评估，采用程序化生成的导航案例。结果表明，在语义干扰增加时，本体引导推理比仅依赖语义的基线方法更准确识别相关解释因素，且保持鲁棒性。论文主张，可用性本体不仅是环境的语义描述工具，更可作为可解释性与可靠自主性的推理基础。
原文摘要 · Abstract (English)
This paper proposes ontology-guided reasoning for affordance-based explanations of robot navigation. In human environments, it is not sufficient for a robot to detect that its route is blocked. It must also reason about what nearby objects afford, which state changes are possible, and which of these changes would allow it to continue safely. We address this problem by representing nearby entities, their affordances, affordance states, and qualitative spatial relations in a local affordance ontology and by evaluating hypothetical object--affordance state changes as candidate explanation factors. This yields explanations that are not only semantically grounded but also actionable. We instantiate the approach in a lightweight benchmark centered on a robot librarian scenario and evaluate it on procedurally generated navigation cases. The results show that ontology-guided reasoning identifies relevant explanation factors more accurately than a semantic-only baseline and remains robust as semantic clutter increases. Overall, the paper argues that affordance ontologies can serve not merely as semantic descriptions of the environment, but as reasoning foundations for explainability and reliable robot autonomy.
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