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About

Chris J. Ostafew is a robotics researcher specializing in autonomous mobile robot navigation, learning-based control, and model predictive control for challenging outdoor environments. His most influential contributions center on the development of Learning-based Nonlinear Model Predictive Control (LB-NMPC) algorithms, which enable robots to iteratively improve path-tracking performance by learning disturbance models from experience — a powerful approach for navigating complex, off-road terrain where traditional models fall short. Ostafew's 2016 work on Robust Constrained LB-NMPC (237 citations) represents a landmark achievement, extending learning-based control to formally guarantee constraint satisfaction — a critical safety requirement when robots operate near obstacles or localization boundaries. His complementary 2015 paper (195 citations) demonstrated how vision-based systems could achieve high-performance tracking through learned models, while earlier foundational work introduced iterative learning control strategies that laid the groundwork for these advances. Beyond control, he contributed to experience-based speed scheduling and robust uncertainty handling, pushing robots toward confident, time-optimal operation. With over 600 cumulative citations, Ostafew's research has meaningfully advanced the field of field robotics, offering principled, data-driven solutions that bridge the gap between theoretical robustness guarantees and real-world autonomous performance.