CUED Publications database

Reality-assisted evolution of soft robots through large-scale physical experimentation: A review

Howison, T and Hauser, S and Hughes, J and Iida, F (2021) Reality-assisted evolution of soft robots through large-scale physical experimentation: A review. Artificial Life, 26. pp. 484-506. ISSN 1064-5462

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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.

Item Type: Article
Uncontrolled Keywords: cs.RO cs.RO
Divisions: Div F > Machine Intelligence
Depositing User: Cron Job
Date Deposited: 25 Sep 2020 21:36
Last Modified: 13 Apr 2021 10:46
DOI: 10.1162/artl_a_00330