The automatic design of embodied agents (e.g. robots) has existed for 31 years and is experiencing a renaissance of interest in the literature. To date however, the field has remained narrowly focused on two kinds of anatomically simple robots: (1) fully rigid, jointed bodies; and (2) fully soft, jointless bodies. Here we bridge these two extremes with the open ended creation of terrestrial endoskeletal robots: deformable soft bodies that leverage jointed internal skeletons to move efficiently across land. Simultaneous de novo generation of external and internal structures is achieved by (i) modeling 3D endoskeletal body plans as integrated collections of elastic and rigid cells that directly attach to form soft tissues anchored to compound rigid bodies; (ii) encoding these discrete mechanical subsystems into a continuous yet coherent latent embedding; (iii) optimizing the sensorimotor coordination of each decoded design using model-free reinforcement learning; and (iv) navigating this smooth yet highly non-convex latent manifold using evolutionary strategies. This yields an endless stream of novel species of "higher robots" that, like all higher animals, harness the mechanical advantages of both elastic tissues and skeletal levers for terrestrial travel. It also provides a plug-and-play experimental platform for benchmarking evolutionary design and representation learning algorithms in complex hierarchical embodied systems.

翻译：具身智能体（例如机器人）的自动设计已存在31年，并在文献中正经历复兴。然而迄今为止，该领域仍狭隘地聚焦于两类解剖结构简单的机器人：(1) 完全刚性、有关节的躯体；(2) 完全柔软、无关节的躯体。本文通过开放式创建陆地内骨骼机器人来弥合这两个极端：这类机器人具有可形变的柔软躯体，并利用关节化的内部骨架实现陆地高效运动。外部与内部结构的同步从头生成通过以下方式实现：(i) 将三维内骨骼躯体方案建模为弹性与刚性单元的集成集合，这些单元直接连接形成锚定于复合刚体的软组织；(ii) 将这些离散的机械子系统编码为连续且连贯的潜在嵌入；(iii) 使用无模型强化学习优化每个解码设计的感知运动协调；(iv) 利用进化策略在这一平滑但高度非凸的潜在流形中进行导航。由此产生了源源不断的"高级机器人"新物种，它们如同所有高等动物一般，同时利用弹性组织与骨骼杠杆的机械优势实现陆地运动。这也为在复杂分层具身系统中基准测试进化设计与表征学习算法提供了一个即插即用的实验平台。