Real-World Robotics: Learning to Plan for Robust Execution
Abstract
In executing classical plans in the real world, small discrepancies between a planner's internal representations and the real world are unavoidable. These can conspire to cause real-world failures even though the planner is sound and, therefore, proves that a sequence of actions achieves the goal. Permissive planning, a machine learning extension to classical planning, is one response to this difficulty. This paper describes the permissive planning approach and presents GRASPER, a permissive planning robotic system that learns to robustly pick up novel objects.
Cite
Text
Bennett and DeJong. "Real-World Robotics: Learning to Plan for Robust Execution." Machine Learning, 1996. doi:10.1023/A:1018274104280Markdown
[Bennett and DeJong. "Real-World Robotics: Learning to Plan for Robust Execution." Machine Learning, 1996.](https://mlanthology.org/mlj/1996/bennett1996mlj-realworld/) doi:10.1023/A:1018274104280BibTeX
@article{bennett1996mlj-realworld,
title = {{Real-World Robotics: Learning to Plan for Robust Execution}},
author = {Bennett, Scott W. and DeJong, Gerald},
journal = {Machine Learning},
year = {1996},
pages = {121-161},
doi = {10.1023/A:1018274104280},
volume = {23},
url = {https://mlanthology.org/mlj/1996/bennett1996mlj-realworld/}
}