Morgan T. Gillespie

Brigham Young University, RWTH Aachen University

Papers

Total Citations

339

H-Index

About

Morgan T. Gillespie is a pioneering robotics researcher whose work sits at the intersection of soft robotics, machine learning, and control systems. Gillespie has made significant contributions to solving one of the field's most persistent challenges: developing reliable, model-based control strategies for soft robotic systems, which are notoriously difficult to model due to their inherently compliant and nonlinear dynamics. Their most influential work, "Learning nonlinear dynamic models of soft robots for model predictive control with neural networks" (2018, 149 citations), demonstrated how neural networks could be leveraged to learn accurate dynamic models of soft robots, enabling sophisticated model predictive control without the burden of hand-crafted analytical models. This was complemented by earlier research applying model predictive control to pneumatically actuated humanoid robots (2016, 119 citations), helping lay the groundwork for safer human-robot interaction in unstructured environments. Gillespie also advanced the simultaneous control of position and stiffness in inflatable soft robots (71 citations), broadening the functional versatility of compliant platforms. Collectively, Gillespie's research has garnered over 339 citations, reflecting meaningful influence on how researchers approach the control of next-generation robots designed to work safely and effectively alongside humans.