机器人理解人类行为需依赖社会惯例，而非仅靠环境数据。
Semantics in Robotics: Environmental Data Can't Yield Conventions of Human Behaviour
- 将语义定义为人类行为惯例，包括标签、空间位置和物体用途规范。
- 环境数据无法直接提供物体使用惯例，需额外社会性知识。
- 适用于研究人机交互与具身智能的学者，强调社会约定的重要性。
在机器人学与人工智能中，'语义'并无标准定义，通常指为辅助人机交互而提供的附加信息。多数研究者虽隐含意识到这些信息无法从环境数据中直接提取，但未明言原因。本文明确指出，所谓语义本质上是人类行为的惯例集合，包括标签、空间位置、本体论及物体可操作性（affordances）。其中，物体使用惯例尤其困难，因其不仅需要超越环境数据的社会规范，还需理解物理规律与物体组合关系——若完全实现，将等同于人工超智能。
原文摘要 · Abstract (English)
The word semantics, in robotics and AI, has no canonical definition. It usually serves to denote additional data provided to autonomous agents to aid HRI. Most researchers seem, implicitly, to understand that such data cannot simply be extracted from environmental data. I try to make explicit why this is so and argue that so-called semantics are best understood as data comprised of conventions of human behaviour. This includes labels, most obviously, but also places, ontologies, and affordances. Object affordances are especially problematic because they require not only semantics that are not in the environmental data (conventions of object use) but also an understanding of physics and object combinations that would, if achieved, constitute artificial superintelligence.
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