Learning composable models of parameterized skills
Name
ICRA17.pdf
Description
Accepted version
Size
547.68 KB
Format
Adobe PDF
Checksum (MD5)
51719406abc782ea9a3756584a168584
Author(s)
Kaelbling, Leslie Pack
Lozano-Perez, Tomas
Date Issued
May 2017
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Kaelbling, Leslie Pack and Lozano-Perez, Tomas. 2017. "Learning composable models of parameterized skills."
Version
Author's final manuscript
Abstract
© 2017 IEEE. There has been a great deal of work on learning new robot skills, but very little consideration of how these newly acquired skills can be integrated into an overall intelligent system. A key aspect of such a system is compositionality: newly learned abilities have to be characterized in a form that will allow them to be flexibly combined with existing abilities, affording a (good!) combinatorial explosion in the robot's abilities. In this paper, we focus on learning models of the preconditions and effects of new parameterized skills, in a form that allows those actions to be combined with existing abilities by a generative planning and execution system.
MIT Department
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/10.1109/icra.2017.7989109
