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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.

Data science has revolutionized a wide range of scientific disciplines, from linguistics to image analysis. This ‘fourth paradigm’ of science offers a promising avenue for synthesizing the mountains of data generated by experiments, theory and simulations into novel insights and actionable recommendations1. Recently, Yang et al. harnessed this power to design sensors for soft machines2. Their work represents a major step forward in integrating new data science techniques with scientific experimentation to design new devices.

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Fig. 1: Active learning for sensor design.

References

  1. Agrawal, A. & Choudhary, A. APL Materials 4, 053208 (2016).

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  2. Yang, H. et al. Nat. Mach. Intell. 4, 84–94 (2022).

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  3. Laschi, C., Mazzolai, B. & Cianchetti, M. Sci. Robotics 1, eaah3690 (2016).

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  4. Cao, B. et al. ACS Nano. 12, 7434–7444 (2018).

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  5. Lundberg, S. M. & Lee, S.-I. in Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS’17) 4768–4777 (ACM, 2017).

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Authors and Affiliations

  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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  • DOI: https://doi.org/10.1038/s42256-022-00453-z

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