@COMMENT This file was generated by bib2html.pl <http://www.cs.cmu.edu/~pfr/misc_software/index.html#bib2html> version 0.90
@COMMENT written by Patrick Riley <http://www.cs.cmu.edu/~pfr>
@COMMENT This file came from Peter Stone's publication pages at
@COMMENT http://www.cs.utexas.edu/~pstone/papers
@InProceedings{iros2023-zhang,
author={Xiaohan Zhang and Yifeng Zhu and Yan Ding and Yuqian Jiang and Yuke Zhu and Peter Stone and Shiqi Zhang},
booktitle={International Conference on Intelligent Robots and Systems (IROS)},
title={Symbolic State Space Optimization for Long Horizon Mobile Manipulation Planning},
month={October},
year={2023},
doi={},
location={Detroit, USA},
abstract={In existing task and motion planning (TAMP) research, it is a common assumption that experts manually specify the state space for task-level planning. A well-developed state space enables the desirable distribution of limited computational resources between task planning and motion planning. However, developing such task-level state spaces can be non-trivial in practice. In this paper, we consider a long horizon mobile manipulation domain including repeated navigation and manipulation. We propose Symbolic State Space Optimization~(S3O) for computing a set of abstracted locations and their 2D geometric groundings for generating task-motion plans in such domains. Our approach has been extensively evaluated in simulation and demonstrated on a real mobile manipulator working on clearing up dining tables. Results show the superiority of the proposed method over TAMP baselines in task completion rate and execution time.},
wwwnote={<a href="https://sites.google.com/view/s3o">Project website</a> (includes poster and 5-minute video presentation)},
}