Emerging soft machines require high-performance strain sensors to achieve closed-loop feedback control. Predicting the performance of a soft robotic sensor from its composition and morphology is nearly impossible with traditional computational approaches. Machine learning (ML) is a versatile tool to uncover complex correlations between fabrication recipes and sensor performance, yet the limited acquisition rate of high-quality data hinders the development of high-accuracy prediction models at the device level. In this talk, I will demonstrate our recent work of using an ML model to predict device-level performance and recommend new material compositions for soft machine applications. I will present a three-stage ML framework to construct a prediction model capable of automating the design of strain sensors across a wide strain range from10,000 virtual data points followed by genetic algorithm-based selection to optimize the prediction accuracy of ML model. An ultimate prediction model is finally constructed and able to (1) predict sensor characteristics based on fabrication recipes and (2) recommend feasible fabrication recipes for adequate strain sensors. As final demonstrations, model-suggested strain sensors are integrated into soft gripper and batoid-like swimmer to endow them with real-time sensing capabilities. Po-Yen Chen Assistant Professor Department of Chemical and Biomolecular Engineering University of Maryland To ask the speaker a question, click on the speech bubble icon in the lower right hand corner and type in the question in the window that pops up. The question will be sent directly to us. Please note that there is a little bit of a delay when streaming. What participants see is a few minutes behind what is happening at our end. The longer we stream, the greater the delay may become so the questions submitted at the very end may not reach us in time. The best way to get the questions answered is to send them as they come up.