标题： 具有结构子群的对称感知机器人设计 显示英文标题

标题： Symmetry-Aware Robot Design with Structured Subgroups

摘要： 机器人设计旨在学习创建易于控制并能高效执行任务的机器人。 机器人设计的先前工作已经证明了其生成适用于各种任务的机器人的能力。 然而，这些工作直接从庞大的设计空间中搜索机器人，并忽略了常见结构，导致生成异常机器人和性能不佳。 为了解决这个问题，我们提出了一种对称感知的机器人设计（SARD）框架，该框架通过在机器人设计过程中引入对称性搜索来利用设计空间的结构。 具体来说，我们用二面体群的子群表示对称性，并在结构化的子群中搜索最优对称性。 然后在搜索到的对称性下设计机器人。 这样，SARD可以在覆盖原始设计空间的同时设计出高效的对称机器人，这在理论上得到了分析。 我们进一步在各种任务上对SARD进行了实证评估，结果表明其具有优越的效率和泛化能力。 显示英文摘要

摘要： 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.