Learning Continuous Cost-to-Go Functions for Non-holonomic Systems
Jinwook Huh,Daniel D. Lee,Volkan Isler,Jinwook Huh,Daniel D. Lee,Volkan Isler
This paper presents a supervised learning method to generate continuous cost-to-go functions of non-holonomic systems directly from the workspace description. Supervision from informative examples reduces training time and improves network performance. The manifold representing the optimal trajectories of a non-holonomic system has high-curvature regions which can not be efficiently captured with ...
