Tifanny Portela

ELMP: Efficient Learning for Motion Planning via Analytical Policy Gradients

Yixiao Li, Tifanny Portela, Jordis Herrmann, René Zurbrügg, Marco Hutter

Neural Motion Planners (NMPs) enable fast reactive motion generation, but adapting them to new environments typically requires recollecting large expert datasets, which is computationally prohibitive. We propose ELMP, a framework for data-efficient adaptation via self-supervised…Read more