用重抓取地图加速复杂抓取规划，提升搜索效率。
Regrasp Maps for Sequential Manipulation Planning
- 构建重抓取地图抽象状态空间，预判可能的抓取位置与序列。
- 通过失败反馈迭代优化地图，显著减少规划搜索时间。
- 适合需要多次重抓取的复杂场景，如杂乱环境下的机械臂操作。
针对受限且杂乱环境中的操作问题，此类场景需在未知位置进行多次重抓取。本文提出将可能的重抓取区域和抓取序列信息融入基于优化的任务与运动规划（TAMP）求解器，以加速搜索过程。核心思想是引入一种状态空间抽象——重抓取地图，用于捕捉配置空间中不同区域可实现抓取的组合，并为求解器提供模式切换猜测及物体放置的额外约束。通过交替执行重抓取地图的构建、基于失败重构的自适应调整以及求解TAMP（子）问题，我们提出了一种对挑战性重抓取任务具有鲁棒性的搜索方法。
原文摘要 · Abstract (English)
We consider manipulation problems in constrained and cluttered settings, which require several regrasps at unknown locations. We propose to inform an optimization-based task and motion planning (TAMP) solver with possible regrasp areas and grasp sequences to speed up the search. Our main idea is to use a state space abstraction, a regrasp map, capturing the combinations of available grasps in different parts of the configuration space, and allowing us to provide the solver with guesses for the mode switches and additional constraints for the object placements. By interleaving the creation of regrasp maps, their adaptation based on failed refinements, and solving TAMP (sub)problems, we are able to provide a robust search method for challenging regrasp manipulation problems.
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