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Reality-Assisted Evolution of Soft Robots through Large-Scale Physical Experimentation: A Review.

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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.



Soft robotics, design optimization, embodied evolution, evolutionary robotics, Algorithms, Artificial Intelligence, Computer Simulation, Equipment Design, Robotics

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Artif Life

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MIT Press - Journals


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EPSRC (EP/T00519X/1)
EPSRC (1949869)
This work was funded by The United Kingdom Engineering and Physical Sciences Research Council (EPSRC) MOTION grant EP/N03211X/2, RoboPatient grant EP/T00519X/1 and grant RG92738 for the University of Cambridge Centre for Doctoral Training. There was additional funding from The Mathworks, Inc.