Soft machines — a subcategory of robotics that uses deformable materials instead of rigid links — are an emerging technology commonly used in wearable robotics and biomimetics (e.g., prosthetic limbs).

A University of Maryland (UMD) research team led by Po-Yen Chen, Professor of Chemical and Biomolecular Engineering, has created a machine learning (ML) framework for a prediction model to do two soft machine design tasks: (1) predict sensor performance based on a fabrication recipe and (2) recommend feasible fabrication recipes for adequate strain sensors.

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