Motion Planning by Learning the Solution Manifold in Trajectory Optimization

TL;DR

This paper introduces a deep generative model that learns an infinite set of collision-free trajectories for motion planning, enabling the discovery of diverse solutions in non-convex optimization landscapes.

Contribution

It proposes a novel method to learn an infinite set of solutions in trajectory optimization by modeling latent representations, surpassing previous finite-solution approaches.

Findings

Model captures an infinite set of homotopic solutions

Generates diverse collision-free trajectories

Outperforms existing methods in diversity of solutions

Abstract

The objective function used in trajectory optimization is often non-convex and can have an infinite set of local optima. In such cases, there are diverse solutions to perform a given task. Although there are a few methods to find multiple solutions for motion planning, they are limited to generating a finite set of solutions. To address this issue, we presents an optimization method that learns an infinite set of solutions in trajectory optimization. In our framework, diverse solutions are obtained by learning latent representations of solutions. Our approach can be interpreted as training a deep generative model of collision-free trajectories for motion planning. The experimental results indicate that the trained model represents an infinite set of homotopic solutions for motion planning problems.