Scene understanding is a fundamental challenge for intelligent robots, especially for social robots, which are expected to have a human-like perception, comprehension, and knowledge. This paper proposes an approach to enable robots not only to detect objects in a scene but also to understand and reason the working environments. The proposed method contains three parts, which are object detection, object semantic comprehension, and feedback on robotic comprehension. Semantic comprehension is based on dictionary definitions of objects. The category, function, property, and composition of the detected objects are analyzed. These four elements are used to assist the robot in comprehending the target object in details. The experiment part of this paper discusses the applicability of the proposed method on robots.

场景理解是智能机器人（尤其是社交机器人）面临的一项基本挑战，社交机器人应具有类似于人的感知，理解和知识。本文提出了一种方法，使机器人不仅可以检测场景中的物体，还可以理解和推理工作环境。所提出的方法包括对象检测，对象语义理解和机器人理解反馈三个部分。语义理解基于对象的字典定义。分析检测到的对象的类别，功能，属性和组成。这四个元素用于帮助机器人详细理解目标对象。本文的实验部分讨论了该方法在机器人上的适用性。