Max Robotics — Global Service Robot Directory + US Certification

About

Eric C. Townsend’s research lies at the intersection of soft robotics, machine learning, and human-robot collaboration, with a focus on making robots safer and more intuitive partners. His most influential work, “Learning nonlinear dynamic models of soft robots for model predictive control with neural networks” (2018, 149 citations), addresses a core challenge in soft robotics: the difficulty of modeling highly deformable, nonlinear systems for real-time control. By demonstrating that neural networks can learn accurate dynamic models directly from data, Townsend enabled model predictive control for soft robots without laborious first-principles modeling—a breakthrough that has shaped subsequent work in the field. His research also explores physical human-robot co-manipulation, where he developed methods to estimate short-term human intent from force and motion cues (2017, 7 and 3 citations). These contributions aim to give robots the shared mental models that human teams use instinctively, enabling more fluid collaboration in manufacturing and assistive settings. Townsend’s work is notable for bridging data-driven modeling with practical control, offering a path toward robots that can safely and adaptively work alongside people.