Approximating Constraint Manifolds Using Generative Models for Sampling-Based Constrained Motion Planning
Cihan Acar,Keng Peng Tee,Cihan Acar,Keng Peng Tee
Sampling-based motion planning under task constraints is challenging because the null-measure constraint manifold in the configuration space makes rejection sampling extremely inefficient, if not impossible. This paper presents a learning-based sampling strategy for constrained motion planning problems. We investigate the use of two well-known deep generative models, the Conditional Variational Au...
