Approximate Inference-based Motion Planning by Learning and Exploiting Low-Dimensional Latent Variable Models

TL;DR

This paper introduces a novel framework that combines low-dimensional latent variable models and approximate inference to efficiently generate high-quality motion plans for high-DOF robots, overcoming the curse of dimensionality.

Contribution

It proposes integrating GP-LVM with an approximate inference algorithm to transform high-dimensional motion planning into a tractable probabilistic inference problem.

Findings

Efficient motion planning for high-DOF robots demonstrated.

Low-dimensional latent space effectively captures complex configurations.

Approximate inference yields high-quality trajectories.

Abstract

This work presents an efficient framework to generate a motion plan of a robot with high degrees of freedom (e.g., a humanoid robot). High-dimensionality of the robot configuration space often leads to difficulties in utilizing the widely-used motion planning algorithms, since the volume of the decision space increases exponentially with the number of dimensions. To handle complications arising from the large decision space, and to solve a corresponding motion planning problem efficiently, two key concepts are adopted in this work: First, the Gaussian process latent variable model (GP-LVM) is utilized for low-dimensional representation of the original configuration space. Second, an approximate inference algorithm is used, exploiting through the duality between control and estimation, to explore the decision space and to compute a high-quality motion trajectory of the robot. Utilizing the GP-LVM and the duality between control and estimation, we construct a fully probabilistic generative model with which a high-dimensional motion planning problem is transformed into a tractable inference problem. Finally, we compute the motion trajectory via an approximate inference algorithm based on a variant of the particle filter. The resulting motions can be viewed in the supplemental video. ( https://youtu.be/kngEaOR4Esc )