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

arXiv: https://arxiv.org/abs/1711.08275 · HTML: https://mlsanity.com/paper/1711.08275/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Nov 2017.

## Built on by (most cited first, 1 of 1)

- [A Factor Graph Model of Trust for a Collaborative Multi-Agent System](https://mlsanity.com/paper/2402.07049/) (2024)

## Abstract (arXiv)

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 )