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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References
Agrawal, A. & Choudhary, A. APL Materials 4, 053208 (2016).
Yang, H. et al. Nat. Mach. Intell. 4, 84–94 (2022).
Laschi, C., Mazzolai, B. & Cianchetti, M. Sci. Robotics 1, eaah3690 (2016).
Cao, B. et al. ACS Nano. 12, 7434–7444 (2018).
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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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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