Speeding Up Optimization-based Motion Planning through Deep Learning
Johannes Tenhumberg,Darius Burschka,Berthold Bäuml,Johannes Tenhumberg,Darius Burschka,Berthold Bäuml
Planning collision-free motions for robots with many degrees of freedom is challenging in environments with complex obstacle geometries. Recent work introduced the idea of speeding up the planning by encoding prior experience of successful motion plans in a neural network. However, this “neural motion planning” did not scale to complex robots in unseen 3D environments as needed for real-world appl...
