Constrained Motion Planning Networks X

Constrained motion planning is a challenging field of research, aiming for computationally efficient methods that can find collision-free path on the constraint manifolds between given start and goal configuration. These problems come up surprisingly frequently, such as in robot manipulation performing daily life assistive tasks. However, few solutions to constrained are available, those exist struggle with high computational time complexity finding solution manifolds. To address this challenge, we present Motion Planning Networks X (CoMPNetX). It neural approach, comprising conditional deep generator discriminator gradients-based fast projection operator. We also introduce task scene representations conditioned which CoMPNetX generates implicit manifold configurations turbo-charge any underlying classical planner sampling-based quickly solving complex show our method finds success rates lower computation times than state-of-the-art traditional path-finding tools various scenarios.