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Symbolic State Space Optimization for Long Horizon Mobile Manipulation Planning.
Xiaohan Zhang, Yifeng
Zhu, Yan Ding, Yuqian Jiang, Yuke Zhu,
Peter Stone, and Shiqi Zhang.
In
International Conference on Intelligent Robots and Systems (IROS), October 2023.
Project
website (includes poster and 5-minute video presentation)
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.
@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)}
}
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