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Data\u2010driven methods such as machine learning are especially suited to systems with governing functions that are unknown, impractical or impossible to represent analytically, or computationally intractable to integrate into real\u2010world solutions. Function approximation with careful formulation of the machine learning architecture enables the encoding of dynamic behavior and nonlinearities, with the added potential to address hysteresis and nonstationary behavior. Supervised learning and reinforcement learning in simulation and on a wide variety of physical robotic systems have shown promising results for the use of empirical data\u2010driven methods as a solution to contemporary soft robotics problems.<\/jats:p><\/jats:sec>","DOI":"10.1002\/aisy.201900171","type":"journal-article","created":{"date-parts":[[2020,1,22]],"date-time":"2020-01-22T06:44:40Z","timestamp":1579675480000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":201,"title":["Machine Learning for Soft Robotic Sensing and Control"],"prefix":"10.1002","volume":"2","author":[{"given":"Keene","family":"Chin","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering Carnegie Mellon University  Pittsburgh PA 15213 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tess","family":"Hellebrekers","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering Carnegie Mellon University  Pittsburgh PA 15213 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6469-9645","authenticated-orcid":false,"given":"Carmel","family":"Majidi","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering Carnegie Mellon University  Pittsburgh PA 15213 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2020,3,3]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.1080\/11762320802557865"},{"key":"e_1_2_7_3_1","doi-asserted-by":"publisher","DOI":"10.1002\/anie.201800907"},{"key":"e_1_2_7_4_1","doi-asserted-by":"publisher","DOI":"10.1002\/admt.201800477"},{"key":"e_1_2_7_5_1","doi-asserted-by":"publisher","DOI":"10.1038\/nature14543"},{"key":"e_1_2_7_6_1","doi-asserted-by":"publisher","DOI":"10.1089\/soro.2013.0001"},{"key":"e_1_2_7_7_1","first-page":"705","volume-title":"IEEE\/RSJ Int. 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