Identifying and categorizing specific robot tasks, behaviors, and resources is an essential precursor to reproducing and evaluating robotics experiments across laboratories and platforms. Without some means of capturing how one environment, platform, or behavior differs from another, we cannot begin to establish the performance impact of these changes or predict a robot's performance in a novel environment. As a first step towards experimental reproducibility, existing taxonomies in the field of robotics are reviewed and common patterns of structure and form extracted, identifying both the properties they share with traditional taxonomies and the necessary structural elements that draw from other classification and categorization systems. The diversity of taxonomy subjects and subsequent difficulty in harmonization of conceptual underpinnings is noted. Robotics taxonomies are shown to be deeply fragmented in structure and form and to require notation that can support complex relationships.
翻译:确定和分类具体的机器人任务、行为和资源是复制和评价跨实验室和平台的机器人实验的基本前提。如果没有某种方法来了解一种环境、平台或行为如何与另一种环境、平台或行为不同,我们就无法开始确定这些变化的性能影响或预测机器人在新环境中的性能。作为实验性再复制的第一步,对机器人领域现有的分类进行了审查,并提取了共同的结构和形式模式,确定了它们与传统分类共有的特性以及从其他分类和分类制度中提取的必要结构要素。注意到了分类科目的多样性以及随后在协调概念基础方面的困难。 机器人分类在结构和形式上被证明是极为分散的,并需要能够支持复杂关系的标记。