发现机器人形态与控制协同设计的结构规律，提升搜索效率36%。
Identifying and Exploiting Structure in Robot Co-Design
- 识别出形态与控制间的低维流形结构，压缩搜索空间。
- 高质量区域需扩展搜索维度，且沿联合维度变化显著。
- 算法比现有方法少10倍评估次数，适合复杂机器人设计。
机器人形态与控制的协同设计是高维搜索问题，其效率取决于对形态与控制交互所形成结构的利用。本文分析了两种软体运动和两种操作任务的协同设计空间，发现三个跨区域一致的模式：1）在局部区域内，性能沿低维流形变化，正交方向变化极小，有效降低搜索维度；2）高质量区域中，性能方差分布在更多维度上，要求搜索时逐步增加维度；3）高质量区域中，性能沿形态-控制联合维度变化，需沿这些维度搜索。基于此提出高效协同设计算法，其生成结果比当前最优算法提升36%，且仅需约十分之一的函数评估次数即可达到相近性能。通过对比各算法探索模式并进行消融实验，证明结构利用是实现高效协同设计的关键。
原文摘要 · Abstract (English)
Co-design of a robot's morphology and control is a high-dimensional search problem. Efficient search depends on exploiting the structure shaped by the interaction between morphology and control. In this paper, we analyze co-design landscapes of two soft locomotion and two manipulation tasks. We identify three patterns that are consistent across regions of their co-design spaces: 1) Within a region, quality varies along a low-dimensional manifold, with minimal variation orthogonal to it, reducing the effective search space dimensionality. 2) In higher-quality regions, the variance in quality is spread across more dimensions, necessitating search to expand dimensionality as quality improves. 3) In higher-quality regions, quality varies along joint morphology-control dimensions, requiring search along them. We leverage these insights to devise an efficient co-design algorithm. This algorithm yields co-designs $36\%$ better than those of state-of-the-art algorithms. Moreover, these benchmark algorithms required about an order of magnitude more function evaluations to achieve co-designs of comparable quality to ours. We inspect the exploration patterns of all algorithms and ablate our algorithm, demonstrating that exploiting the identified structure enables efficient co-design.
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