Configuration Space Decomposition for Learning-based Collision Checking in High-DOF Robots
Yiheng Han,Wang Zhao,Jia Pan,Yong-Jin Liu,Yiheng Han,Wang Zhao,Jia Pan,Yong-Jin Liu
Motion planning for robots of high degrees-of-freedom (DOFs) is an important problem in robotics with sampling-based methods in configuration space $\mathcal{C}$ as one popular solution. Recently, machine learning methods have been introduced into sampling-based motion planning methods, which train a classifier to distinguish collision free subspace from in-collision subspace in $\mathcal{C}$. In ...
