Admissible Abstractions for Near-optimal Task and Motion Planning
Name
1806.00805.pdf
Description
Accepted version
Size
5.34 MB
Format
Adobe PDF
Checksum (MD5)
cc760be2a7b33fd70598e50c60b97881
Author(s)
Vega-Brown, William R
Roy, Nicholas
Date Issued
July 2018
Journal
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
Publisher
International Joint Conferences on Artificial Intelligence Organization
Citation
Vega-Brown, William and Nicholas Roy. "Admissible Abstractions for Near-optimal Task and Motion Planning." Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, July 2018, Stockholm, Sweden, International Joint Conferences on Artificial Intelligence Organization, July 2018 © 2018 International Joint Conferences on Artificial Intelligence
Version
Author's final manuscript
Abstract
We define an admissibility condition for abstractions expressed using angelic semantics and show that these conditions allow us to accelerate planning while preserving the ability to find the optimal motion plan. We then derive admissible abstractions for two motion planning domains with continuous state. We extract upper and lower bounds on the cost of concrete motion plans using local metric and topological properties of the problem domain. These bounds guide the search for a plan while maintaining performance guarantees. We show that abstraction can dramatically reduce the complexity of search relative to a direct motion planner. Using our abstractions, we find near-optimal motion plans in planning problems involving 1013 states without using a separate task planner.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.24963/ijcai.2018/674
