Unlike most human-engineered systems, biological systems are emergent from low-level interactions, allowing much broader diversity and superior adaptation to the complex environments. Inspired by the process of morphogenesis in nature, a bottom-up design approach for robot morphology is proposed to treat a robot's body as an emergent response to underlying processes rather than a predefined shape. This paper presents Loopy, a "Swarm-of-One" polymorphic robot testbed that can be viewed simultaneously as a robotic swarm and a single robot. Loopy's shape is determined jointly by self-organization and morphological computing using physically linked homogeneous cells. Experimental results show that Loopy can form symmetric shapes consisting of lobes. Using the the same set of parameters, even small amounts of initial noise can change the number of lobes formed. However, once in a stable configuration, Loopy has an "inertia" to transfiguring in response to dynamic parameters. By making the connections among self-organization, morphological computing, and robot design, this paper lays the foundation for more adaptable robot designs in the future.

翻译：与多数人工设计系统不同，生物系统通过低层级交互实现涌现，从而展现出更丰富的多样性及对复杂环境更强的适应性。受自然界形态发生过程启发，本文提出一种自下而上的机器人形态设计方法，将机器人身体视为底层过程的涌现响应而非预设形状。本文介绍名为Loopy的"单体群集"多态机器人实验平台，该平台可同时被视为机器人集群与单一机器人个体。Loopy的形态由利用物理耦合同质单元的自组织与形态计算共同决定。实验结果表明，Loopy能形成由分叶构成的对称形态。使用相同参数集时，即使微小的初始噪声也会改变分叶数量。然而，一旦进入稳定构型，Loopy对参数动态变化引发的形态重构具有"惯性"。本文通过建立自组织、形态计算与机器人设计之间的关联，为未来更具适应性的机器人设计奠定基础。