Reality-assisted evolution of soft robots through large-scale physical experimentation: a review
This incremental review addresses the problem of designing more effective soft robots for applications in robotics and automation by summarizing existing trends without introducing new methods.
The paper reviews the reality-assisted evolution framework, which combines model-based and model-free approaches to enhance the design of physically embodied soft robots by integrating large-scale physical experimentation with data-driven simulations, resulting in improved design candidates and model accuracy.
In this review 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 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 under the framework of reality-assisted evolution.