通过敲击-抓取协同策略，高效规划密集桌面上方块的最优重排。
Optimal Knock-Pick Planning for Tightly Packed Tabletop Blocks With Parallel Grippers
- 构建图抽象模型，用最大权匹配求解最少动作数
- 在不同网格规模下实验验证，支持真实仿真环境
- 为夹持与非夹持动作协同提供理论基础
当平行夹爪因缺乏足够间隙而无法抓取密集排列的桌面上方块时，重排任务变得困难。本文研究了均匀尺寸方块在平面网格布局下的实际场景问题。由于纯抓取可能不可行，引入方向性敲击原语，并提出最优敲击-抓取规划问题。通过一系列抽象，仅覆盖最小约束装置以识别必要敲击动作；利用图抽象上的最大权完美匹配，实现多项式时间内的最优计划求解，最小化操作次数。在合成环境及IsaacSim中报告了不同网格规模的实验结果。理论发现为严谨构建夹持与非夹持动作交织的高效操控策略提供了有希望的起点。
原文摘要 · Abstract (English)
Rearranging densely packed tabletop objects is challenging when parallel-gripper picks are infeasible without sufficient clearance around an object. This work studies the problem characteristics for practically motivated settings with uniformly sized blocks placed at planar tabletop grid locations. Since purely prehensile removal can become infeasible, a directional knock primitive is therefore introduced and the optimal knock-pick variant of the problem is formulated. The work proposes a series of abstractions wherein minimal constraining gadgets are covered to identify the necessary knocks. Utilizing a maximum-weight perfect matching on a graphical abstraction yields efficient polynomial-time computation of the optimal plan that minimizes the number of actions. Experiments are reported for increasing grid sizes in synthetic settings as well as in IsaacSim. The theoretical observations provide a promising stepping stone towards rigorously building efficient manipulation strategies that interleave prehensile and non-prehensile actions.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。