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University of Central Florida, University of Penn- sylvania
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AD-MPCC achieves faster lap times and enhanced safety in autonomous racing by dynamically adapting to road conditions in real time.
Achieve safer and more performant robot control in iterative tasks by using normalizing flows to learn value functions within a model predictive control framework.
Autonomous vehicles can now nimbly navigate complex 3D terrain thanks to a novel MPC framework that learns and adapts to nonplanar geometry in real-time.
