Learning the Problem-Optimum Map: Analysis and Application to Global Optimization in Robotics
Robotics
2016-05-17 v1
Abstract
This paper describes a data-driven framework for approximate global optimization in which precomputed solutions to a sample of problems are retrieved and adapted during online use to solve novel problems. This approach has promise for real-time applications in robotics, since it can produce near-globally optimal solutions orders of magnitude faster than standard methods. This paper establishes theoretical conditions on how many and where samples are needed over the space of problems to achieve a given approximation quality. The framework is applied to solve globally optimal collision-free inverse kinematics (IK) problems, wherein large solution databases are used to produce near-optimal solutions in sub-millisecond time on a standard PC.
Cite
@article{arxiv.1605.04636,
title = {Learning the Problem-Optimum Map: Analysis and Application to Global Optimization in Robotics},
author = {Kris Hauser},
journal= {arXiv preprint arXiv:1605.04636},
year = {2016}
}