Robots that interact with humans in a physical space or application need to think about the person's posture, which typically comes from visual sensors like cameras and infra-red. Artificial intelligence and machine learning algorithms use information from these sensors either directly or after some level of symbolic abstraction, and the latter usually partitions the range of observed values to discretize the continuous signal data. Although these representations have been effective in a variety of algorithms with respect to accuracy and task completion, the underlying models are rarely interpretable, which also makes their outputs more difficult to explain to people who request them. Instead of focusing on the possible sensor values that are familiar to a machine, we introduce a qualitative spatial reasoning approach that describes the human posture in terms that are more familiar to people. This paper explores the derivation of our symbolic representation at two levels of detail and its preliminary use as features for interpretable activity recognition.
翻译:在物理空间或应用中与人类互动的机器人需要考虑一个人的姿势,这种姿势通常来自摄像机和红外线等视觉传感器。人工智能和机器学习算法直接或在某些象征性抽象程度之后使用来自这些传感器的信息,而后者通常将观测到的值范围分割开来,将连续信号数据分解。虽然这些表示在各种算法中对于准确性和任务完成有效,但基本模型很少可以解释,也使得其产出更难向提出要求的人解释。我们不注重机器熟悉的可能传感器值,而是采用定性空间推理法,用人们更熟悉的术语描述人类的态势。本文探讨了我们象征性表示的两个细节层次及其初步用途,作为可解释活动识别特征的衍生。