### Abstract

Trajectory optimization for motion planning requires good initial guesses to obtain good performance. In our proposed approach, we build a memory of motion based on a database of robot paths to provide good initial guesses online. The memory of motion relies on function approximators and dimensionality reduction techniques to learn the mapping between the task and the robot paths. Three function approximators are compared: k-Nearest Neighbor, Gaussian Process Regression, and Bayesian Gaussian Mixture Regression. In addition, we show that the usage of the memory of motion can be improved by using an ensemble method, and that the memory can also be used as a metric to choose between several possible goals. We demonstrate the proposed approach with the motion planning on the dual-arm robot PR2 and the humanoid robot Atlas.

### Bibtex reference

@article{Lembono20RAL,
author="Lembono, T. S. and Paolillo, A. and Pignat, E. and Calinon, S.",
title="Memory of Motion for Warm-starting Trajectory Optimization",
journal="{IEEE} Robotics and Automation Letters ({RA-L})",
year="2020",
volume="",
number="",
pages="",
doi=""
}