Intelligence of physical agents, such as human-made (e.g., robots, autonomous cars) and biological (e.g., animals, plants) ones, is not only enabled by their computational intelligence (CI) in their brain, but also by their physical intelligence (PI) encoded in their body. Therefore, it is essential to advance the PI of human-made agents as much as possible, in addition to their CI, to operate them in unstructured and complex real-world environments like the biological agents. This article gives a perspective on what PI paradigm is, when PI can be more significant and dominant in physical and biological agents at different length scales and how bioinspired and abstract PI methods can be created in agent bodies. PI paradigm aims to synergize and merge many research fields, such as mechanics, materials science, robotics, mechanical design, fluidics, active matter, biology, self-assembly and collective systems, to enable advanced PI capabilities in human-made agent bodies, comparable to the ones observed in biological organisms. Such capabilities would progress the future robots and other machines beyond what can be realized using the current frameworks.

诸如人类（例如，机器人，自动驾驶汽车）和生物（例如，动物，植物）等物理代理的智能，不仅取决于他们大脑中的计算智能（CI），而且还取决于他们的物理智能。 （PI）编码在他们的体内。因此，至关重要的是，除了CI以外，还应尽可能提高人为因素的PI，以便在非结构化和复杂的现实世界环境（如生物因素）中操作它们。本文就什么是PI范例，什么时候PI在不同长度范围的物理和生物代理中更重要和占主导地位以及如何在代理主体中创建具有生物启发性和抽象的PI方法提供了一个观点。PI范式旨在整合和合并许多研究领域，例如力学，材料科学，机器人技术，机械设计，流体力学，活性物质，生物学，自组装和集体系统，以实现与生物有机体中观察到的类似的先进的PI功能，使其能够在人造制剂体内发挥作用。这样的功能将使未来的机器人和其他机器的发展超出使用当前框架所能实现的范围。