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

Understand

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.