Symbolic State Space Optimization for Long Horizon Mobile Manipulation Planning
Xiaohan Zhang,Yifeng Zhu,Yan Ding,Yuqian Jiang,Yuke Zhu,Peter Stone,Shiqi Zhang,Xiaohan Zhang,Yifeng Zhu,Yan Ding,Yuqian Jiang,Yuke Zhu,Peter Stone,Shiqi Zhang
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...
