Identifying and Exploiting Structure in Robot Co-Design

Abstract: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.

Robotics (cs.RO)

arXiv:2604.11768 [cs.RO]

(or arXiv:2604.11768v2 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2604.11768 Focus to learn more arXiv-issued DOI via DataCite

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.