Learning the Problem-Optimum Map: Analysis and Application to Global Optimization in Robotics

Published

Journal Article

© 2016 IEEE. 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 problems, wherein large solution databases are used to produce near-optimal solutions in a submillisecond time on a standard PC.

Full Text

Duke Authors

Cited Authors

  • Hauser, K

Published Date

  • February 1, 2017

Published In

Volume / Issue

  • 33 / 1

Start / End Page

  • 141 - 152

International Standard Serial Number (ISSN)

  • 1552-3098

Digital Object Identifier (DOI)

  • 10.1109/TRO.2016.2623345

Citation Source

  • Scopus