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