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.

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In International Conference on Intelligent Robots and Systems, Detroit, USA, October 2023.

@inproceedings{iros2023-zhang, title={Symbolic State Space Optimization for Long Horizon Mobile Manipulation Planning}, 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}, month={October}, address={Detroit, USA}, url="http://www.cs.utexas.edu/users/ai-lab?iros2023-zhang", year={2023} }