提升移动机器人桌面上物品重排的效率与质量。
Mobile Manipulation Planning for Tabletop Rearrangement
- 从单个站位执行多个抓取放置动作，减少无效移动。
- 引入状态重探索机制，显著提升解的质量与收敛速度。
- 适合需要高效重排的移动机器人场景，如家庭服务、仓储分拣。
高效桌面上物品重排规划旨在以最小总成本获得高质量解，但受限于物体间的依赖关系及临时放置空间不足，任务极具挑战性。对于移动机器人而言，还需在桌子周围导航，且可达区域受限，进一步增加复杂度。基于A*的方法虽能生成高质量解，但随物体数量增加而难以扩展；蒙特卡洛树搜索（MCTS）作为在线算法虽具灵活性，但收敛至优质解的速度仍较慢。先前工作通过强制机器人每次操作前移动至最近位置加速收敛，导致效率下降。为此，本文提出改进策略：允许机器人在单一站位完成多次操作，避免频繁移动；同时引入状态重探索机制，持续优化路径。实验表明，本方法在解质量与规划时间上均优于现有主流规划器。
原文摘要 · Abstract (English)
Efficient tabletop rearrangement planning seeks to find high-quality solutions while minimizing total cost. However, the task is challenging due to object dependencies and limited buffer space for temporary placements. The complexity increases for mobile robots, which must navigate around the table with restricted access. A*-based methods yield high-quality solutions, but struggle to scale as the number of objects increases. Monte Carlo Tree Search (MCTS) has been introduced as an anytime algorithm, but its convergence speed to high-quality solutions remains slow. Previous work~\cite{strap2024} accelerated convergence but required the robot to move to the closest position to the object for each pick and place operation, leading to inefficiencies. To address these limitations, we extend the planner by introducing a more efficient strategy for mobile robots. Instead of selecting the nearest available location for each action, our approach allows multiple operations (e.g., pick-and-place) from a single standing position, reducing unnecessary movement. Additionally, we incorporate state re-exploration to further improve plan quality. Experimental results show that our planner outperforms existing planners both in terms of solution quality and planning time.
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