Active Model Learning and Diverse Action Sampling for Task and Motion Planning
Name
wang-iros18.pdf
Description
Accepted version
Size
1.86 MB
Format
Adobe PDF
Checksum (MD5)
1a348768ea745b97e14573b20f0741f1
Author(s)
Wang, Zi
Garrett, Caelan Reed
Kaelbling, Leslie Pack
Lozano-Perez, Tomas
Date Issued
October 2018
Publisher
IEEE
Citation
Wang, Zi, Garrett, Caelan Reed, Kaelbling, Leslie Pack and Lozano-Perez, Tomas. 2018. "Active Model Learning and Diverse Action Sampling for Task and Motion Planning."
Version
Author's final manuscript
Abstract
© 2018 IEEE. The objective of this work is to augment the basic abilities of a robot by learning to use new sensorimotor primitives to enable the solution of complex long-horizon problems. Solving long-horizon problems in complex domains requires flexible generative planning that can combine primitive abilities in novel combinations to solve problems as they arise in the world. In order to plan to combine primitive actions, we must have models of the preconditions and effects of those actions: under what circumstances will executing this primitive achieve some particular effect in the world? We use, and develop novel improvements on, state-of-the-art methods for active learning and sampling. We use Gaussian process methods for learning the conditions of operator effectiveness from small numbers of expensive training examples collected by experimentation on a robot. We develop adaptive sampling methods for generating diverse elements of continuous sets (such as robot configurations and object poses) during planning for solving a new task, so that planning is as efficient as possible. We demonstrate these methods in an integrated system, combining newly learned models with an efficient continuous-space robot task and motion planner to learn to solve long horizon problems more efficiently than was previously possible.
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/iros.2018.8594027
