arXiv 2307.118892023-07-21cs.RO

面向长时程移动操作规划的符号状态空间优化

Symbolic State Space Optimization for Long Horizon Mobile Manipulation Planning

The State University of New York at Binghamton（纽约州立大学宾汉姆顿分校）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）；Sony AI（索尼人工智能）

Xiaohan Zhang, Yifeng Zhu, Yan Ding, Yuqian Jiang, Yuke Zhu, Peter Stone, Shiqi Zhang

备注：: To be published in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2023

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AI总结：

针对长时程移动操作中任务级状态空间构建困难的问题，提出S3O方法计算抽象位置及其2D几何锚定以生成任务-运动规划，在仿真和真实机器人实验中均优于现有TAMP基准。

摘要：

在现有任务与运动规划（TAMP）研究中，普遍假设由专家手动指定任务级规划的状态空间。完善的状态空间能在任务规划与运动规划之间合理分配有限的计算资源，但在实际场景中构建这类任务级状态空间并非易事。本文针对包含重复导航与操作的长时程移动操作领域展开研究，提出符号状态空间优化（S3O）方法，用于计算一组抽象位置及其二维几何锚定关系，以生成该领域下的任务-运动规划方案。我们在仿真环境中对该方法进行了广泛评估，并在一台负责清理餐桌的真实移动操作机器人上进行了验证。结果表明，所提方法在任务完成率与执行时间上均优于TAMP基准方法。

英文摘要：

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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。