Kinematic Transfer Learning of Sampling Distributions for Manipulator Motion Planning
Peter Lehner,Máximo A. Roa,Alin Albu-Schäffer,Peter Lehner,Máximo A. Roa,Alin Albu-Schäffer
Recent research has shown that guiding sampling-based planners with sampling distributions, learned from previous experiences via density estimation, can significantly decrease computation times for motion planning. We propose an algorithm that can estimate the density from the experiences of a robot with different kinematic structure, on the same task. The method allows to generalize collected da...
