Sam Schoedel

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About

Sam Schoedel is a leading researcher in the intersection of robotics, control theory, and embedded systems, with a primary focus on enabling advanced model-predictive control (MPC) on resource-constrained microcontrollers. His major contribution is the development of TinyMPC, a high-speed MPC solver that makes it feasible to deploy sophisticated control algorithms on small, low-power robotic platforms where computational resources are severely limited. This work, published in 2024, has already garnered 40 citations, reflecting its immediate impact on the field. Building on this foundation, Schoedel extended his approach to handle expressive conic constraints in his follow-up work, "Code Generation and Conic Constraints for Model-Predictive Control on Microcontrollers with Conic-TinyMPC," which addresses the computational challenges of more complex constraint modeling. His research is particularly notable for bridging the gap between theoretical control methods and practical deployment on tiny, agile robots, enabling real-time performance in highly dynamic environments. Schoedel’s work is essential reading for students and researchers working on embedded robotics, autonomous systems, and real-time control, as it provides both algorithmic innovations and practical tools for bringing MPC to the edge.