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About

David Wingate is a researcher whose work sits at the intersection of robotics, machine learning, and human-robot interaction, with particular depth in the modeling and control of soft robotic systems. His most influential contributions address one of soft robotics' central challenges: developing accurate dynamic models for systems that are inherently difficult to characterize analytically. Through a series of highly cited papers, Wingate and his collaborators demonstrated how neural networks and deep learning can be leveraged to learn nonlinear dynamic models of soft robots, enabling effective model predictive control — work that has garnered over 149 citations and spawned follow-on studies integrating first-principles physics with data-driven approaches. Beyond soft robotics, Wingate has made notable contributions to reinforcement learning, introducing physics-based model priors for object-oriented MDPs (40 citations), and to human-robot collaboration, developing data-driven frameworks for co-manipulation tasks and intent estimation. His probabilistic programming work for inferring agent goals further illustrates a broad research vision centered on building intelligent, adaptive robotic systems. Across these domains, Wingate's scholarship reflects a sustained commitment to bridging theoretical rigor with practical robotics applications.