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

Personal use of this material is permitted. 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. T o 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-L VM) 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-L VM 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 . OR robotic motion planning, a trajectory is designed for robot states through a complex configuration space from an initial state to perform a given task. The planning problem is formulated as an optimal control (OC) problem considering the robot dynamics for a feasible motion trajectory and the cost function for the task.