Optimization in the now: Dynamic peephole optimization for hierarchical planning
Name
Lozano-Perez_Optimization in the now.pdf
Size
398.27 KB
Format
Adobe PDF
Checksum (MD5)
0a503301d1c0444aca93d73521279a76
Author(s)
Hadfield-Menell, Dylan
Lozano-Perez, Tomas
Kaelbling, Leslie P.
Date Issued
May 2013
Journal
Proceedings of the 2013 IEEE International Conference on Robotics and Automation
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Hadfield-Menell, Dylan, Leslie Pack Kaelbling, and Tomas Lozano-Perez. “Optimization in the Now: Dynamic Peephole Optimization for Hierarchical Planning.” 2013 IEEE International Conference on Robotics and Automation (May 2013).
Version
Author's final manuscript
Abstract
For robots to effectively interact with the real world, they will need to perform complex tasks over long time horizons. This is a daunting challenge, but recent advances using hierarchical planning have been able to provide leverage on this problem. Unfortunately, this approach makes no effort to account for the execution cost of an abstract plan and often arrives at poor quality plans. This paper outlines a method for dynamically improving a hierarchical plan during execution. We frame the underlying question as one of evaluating the resource needs of an abstract operator and propose a general way to approach estimating them. We ran experiments in challenging domains and observed up to 30% reduction in execution cost when compared with a standard hierarchical planner.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/ICRA.2013.6631225
