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SOFT ROBOTICS

Data-driven design of soft sensors

Nature Machine Intelligence volume 4, pages 194–195 (2022)Cite this article

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Predicting the performance of a tactile sensor from its composition and morphology is nearly impossible with traditional computational approaches. Machine learning can not only predict device-level performance, but also recommend new material compositions for soft machine applications.

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Fig. 1: Active learning for sensor design.

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  1. Department of Materials Science and Engineering, University of Pennsylvania, Philadelphia, PA, USA

    James T. Glazar & Vivek B. Shenoy

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  1. James T. Glazar
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  2. Vivek B. Shenoy
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Correspondence to Vivek B. Shenoy.

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The authors declare no competing interests.

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Glazar, J.T., Shenoy, V.B. Data-driven design of soft sensors. Nat Mach Intell 4, 194–195 (2022). https://doi.org/10.1038/s42256-022-00453-z

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