The intelligent soft robot has received wide attention from both academia and the industry due to its remarkable adaptability. It performs intelligent behavioral learning and evolved morphologies in unpredictable environmental conditions. However, designing a soft robot with a well-adapted morphology involves searching through a large number of possible structures. Furthermore, to learn control tasks in diverse environments, the robot performs computationally intensive numerical simulations, which is time-consuming for evaluating the performance of robots. To address both issues, a multi-fidelity evolutionary algorithm is proposed, which consists of three main components. Firstly, a niching-based fidelity adjustment strategy is introduced to significantly reduce the evaluation cost by training the controller of each robot for only a small number of simulation steps. In particular, considering the estimation errors of the low-fidelity evaluation, the population is divided into multiple subpopulations with different fidelity levels for parallel optimization. Secondly, an effective morphology transfer strategy is proposed to improve the quality of offspring by transferring the local structure of robots in different subpopulations. Finally, a fast local search is developed to enhance the search efficiency of the algorithm without performing additional control simulations. The experimental results on 31 test tasks demonstrate that the proposed algorithm outperforms the SOTA design algorithms on 25 test tasks, especially when the computational budget is limited. Compared to the baseline algorithms, our algorithm reduces the computational cost by 60$\%$% while achieving similar performance.

中文翻译：

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一种基于形态迁移的软体机器人设计多保真度进化算法


这款智能软体机器人因其卓越的适应性而受到学术界和工业界的广泛关注。它在不可预测的环境条件下执行智能行为学习和进化形态。然而，设计具有良好形态的软体机器人涉及搜索大量可能的结构。此外，为了学习不同环境中的控制任务，机器人会执行计算密集型数值模拟，这对于评估机器人的性能来说非常耗时。为了解决这两个问题，提出了一种多保真度进化算法，它由三个主要部分组成。首先，引入一种基于 niching 的保真度调整策略，通过仅对每个机器人的控制器进行少量仿真步骤的训练，显著降低评估成本;特别是，考虑到低保真度评估的估计误差，将总体划分为具有不同保真度水平的多个子总体，以进行并行优化。其次，提出了一种有效的形态转移策略，通过转移不同亚群中机器人的局部结构来提高后代的质量。最后，开发了一种快速的本地搜索来提高算法的搜索效率，而无需执行额外的控制仿真。在 31 个测试任务上的实验结果表明，所提算法在 25 个测试任务上优于 SOTA 设计算法，尤其是在计算预算有限的情况下。与基线算法相比，我们的算法在实现相似性能的同时，将计算成本降低了 60$\%$%。