We introduce the framework of reality-assisted evolution to summarize a growing trend towards combining model-based and model-free approaches to improve the design of physically embodied soft robots. In silico, data-driven models build, adapt, and improve representations of the target system using real-world experimental data. By simulating huge numbers of virtual robots using these data-driven models, optimization algorithms can illuminate multiple design candidates for transference to the real world. In reality, large-scale physical experimentation facilitates the fabrication, testing, and analysis of multiple candidate designs. Automated assembly and reconfigurable modular systems enable significantly higher numbers of real-world design evaluations than previously possible. Large volumes of ground-truth data gathered via physical experimentation can be returned to the virtual environment to improve data-driven models and guide optimization. Grounding the design process in physical experimentation ensures that the complexity of virtual robot designs does not outpace the model limitations or available fabrication technologies. We outline key developments in the design of physically embodied soft robots in the framework of reality-assisted evolution.

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通过大规模物理实验实现软机器人的现实辅助进化：综述

我们介绍了现实辅助进化的框架，以总结结合基于模型和无模型方法来改进物理体现软机器人设计的日益增长的趋势。在计算机中，数据驱动模型使用真实世界的实验数据构建、调整和改进目标系统的表示。通过使用这些数据驱动模型模拟大量虚拟机器人，优化算法可以阐明多个设计候选方案，以便转移到现实世界。实际上，大规模物理实验促进了多个候选设计的制造、测试和分析。自动化组装和可重新配置的模块化系统能够实现比以前更多的实际设计评估。通过物理实验收集的大量真实数据可以返回到虚拟环境中，以改进数据驱动模型并指导优化。在物理实验中建立设计过程可确保虚拟机器人设计的复杂性不会超过模型限制或可用的制造技术。我们概述了在现实辅助进化框架中物理体现软机器人设计的关键发展。