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.

翻译：在现有的任务与运动规划（TAMP）研究中，通常假设由专家手动指定任务级规划的状态空间。一个精心设计的状态空间能够实现有限计算资源在任务规划与运动规划之间的合理分配。然而，在实践中构建此类任务级状态空间往往具有相当难度。本文针对包含重复导航与操作的长时域移动操作领域，提出符号化状态空间优化（S3O）方法，用于计算一组抽象位置及其二维几何具体化表征，以生成该领域中的任务运动规划方案。该方法已在仿真环境中得到充分验证，并在实际清理餐桌的移动操作机器人上完成演示。结果表明，相较于TAMP基线方法，本方法在任务完成率与执行时间上均具有显著优势。