Co-designing a robot's morphology and control can ensure synergistic interactions between them, prevalent in biological organisms. However, co-design is a high-dimensional search problem. To make this search tractable, we need a systematic method for identifying inductive biases tailored to its structure. In this paper, we analyze co-design landscapes for soft locomotion and manipulation tasks and identify three patterns that are consistent across regions of their co-design spaces. We observe that within regions of co-design space, quality varies along a low-dimensional manifold. Higher-quality regions exhibit variations spread across more dimensions, while tightly coupling morphology and control. We leverage these insights to devise an efficient co-design algorithm. Since the precise instantiation of this structure varies across tasks and is not known a priori, our algorithm infers it from information gathered during search and adapts to each task's specific structure. This yields $36\%$ more improvement than benchmark algorithms. Moreover, our algorithm achieved more than two orders of magnitude in sample efficiency compared to these benchmark algorithms, demonstrating the effectiveness of leveraging inductive biases to co-design.

翻译：机器人的形态与控制的协同设计能够确保二者之间的协同作用，这在生物有机体中普遍存在。然而，协同设计是一个高维搜索问题。为了使这种搜索易于处理，我们需要一种系统的方法来识别针对其结构定制的归纳偏置。在本文中，我们分析了软体运动和操作任务的协同设计地形，并识别出在其协同设计空间区域中一致的三个模式。我们观察到，在协同设计空间区域内，质量沿着一个低维流形变化。更高质量的区域表现出分布在更多维度上的变化，同时紧密耦合形态和控制。我们利用这些见解设计了一种高效的协同设计算法。由于这种结构的具体实例化因任务而异且无法先验已知，我们的算法从搜索过程中收集的信息中推断出该结构，并适应每个任务的特定结构。这比基准算法提高了36%。此外，我们的算法在样本效率上比这些基准算法高出两个数量级以上，展示了利用归纳偏置进行协同设计的有效性。