2023/05/31 by Heng Dong, Junyu Zhang, Dong, Heng +5 · 1 citation
Biochemistry, Genetics and Molecular Biology · Engineering · #Advanced biosensing and bioanalysis techniques #Artificial Intelligence (cs.AI) #DNA and Biological Computing #FOS: Computer and information sciences #Modular Robots and Swarm Intelligence
paper · pdf · doi:10.48550/arxiv.2306.00036
openalex publication_date 2023/05/31 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Robot design aims at learning to create robots that can be easily controlled and perform tasks efficiently. Previous works on robot design have proven its ability to generate robots for various tasks. However, these works searched the robots directly from the vast design space and ignored common structures, resulting in abnormal robots and poor performance. To tackle this problem, we propose a Symmetry-Aware Robot Design (SARD) framework that exploits the structure of the design space by incorporating symmetry searching into the robot design process. Specifically, we represent symmetries with the subgroups of the dihedral group and search for the optimal symmetry in structured subgroups. Then robots are designed under the searched symmetry. In this way, SARD can design efficient symmetric robots while covering the original design space, which is theoretically analyzed. We further empirically evaluate SARD on various tasks, and the results show its superior efficiency and generalizability.