Vision-language models (VLMs) have demonstrated remarkable capabilities in understanding human-object interactions, but their application to robotic systems with non-humanoid morphologies remains largely unexplored. This work investigates whether VLMs can effectively infer affordances for robots with fundamentally different embodiments than humans, addressing a critical gap in the deployment of these models for diverse robotic applications. We introduce a novel hybrid dataset that combines annotated real-world robotic affordance-object relations with VLM-generated synthetic scenarios, and perform an empirical analysis of VLM performance across multiple object categories and robot morphologies, revealing significant variations in affordance inference capabilities. Our experiments demonstrate that while VLMs show promising generalisation to non-humanoid robot forms, their performance is notably inconsistent across different object domains. Critically, we identify a consistent pattern of low false positive rates but high false negative rates across all morphologies and object categories, indicating that VLMs tend toward conservative affordance predictions. Our analysis reveals that this pattern is particularly pronounced for novel tool use scenarios and unconventional object manipulations, suggesting that effective integration of VLMs in robotic systems requires complementary approaches to mitigate over-conservative behaviour while preserving the inherent safety benefits of low false positive rates.

翻译：视觉-语言模型（VLM）在理解人-物交互方面展现了卓越的能力，但其在非人形形态机器人系统中的应用仍基本未被探索。本研究探讨VLM能否有效推断与人类具身形式根本不同的机器人的可操作性，填补了此类模型在多样化机器人应用中部署的关键空白。我们提出一种新型混合数据集，结合了标注的真实世界机器人可操作性-物体关系与VLM生成的合成场景，并对多个物体类别和机器人形态下的VLM性能进行实证分析，揭示了可操作性推断能力的显著差异。实验表明，尽管VLM在非人形机器人形式上展现出有前景的泛化能力，其在不同物体领域的表现却明显不一致。关键在于，我们识别出所有形态和物体类别中假阳性率低但假阴性率持续偏高的模式，表明VLM倾向于保守的可操作性预测。分析进一步揭示，这种模式在新颖工具使用场景与非常规物体操作中尤为突出，提示将VLM有效集成到机器人系统中需要互补性方法，以在保持低假阳性率的固有安全性优势的同时，缓解过度保守的行为。