A Configuration-Space Decomposition Scheme for Learning-based Collision Checking

Understand

Motion planning for robots of high degrees-of-freedom (DOFs) is an important problem in robotics with sampling-based methods in configuration space 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 C.
• In this paper, we propose a novel configuration space decomposition method and show two nice properties resulted from this decomposition.
• Using these two properties, we build a composite classifier that works compatibly with previous machine learning methods by using them as the elementary classifiers.