存在时序物体移除的桌面擦拭双臂协作
Dual-arm Cooperation for Tabletop Wiping with Temporal Object Removal
AI总结：
该研究提出一种双臂协作规划框架，通过分配一只手臂移动物体、另一只手臂擦拭，结合Geo-MILP与KOMO算法，提升桌面擦拭的覆盖范围与时间效率，优于TAMP基线。
摘要：
针对存在散落物体的桌面擦拭任务，我们设计了一种双臂协作规划框架。与仅覆盖自由区域并绕开障碍物的传统表面清洁策略不同，该框架明确分配一只手臂用于重新排列物体，另一只手臂执行覆盖式擦拭，从而能够触及此前被遮挡的区域。具体而言，我们首先构建了一组擦拭和物体重新排列的动作原语。基于这些原语，我们引入了Geo-MILP，这是一种以混合整数线性规划（MILP）形式构建的几何感知任务规划器。通过集成基于逻辑的本德分解（LBBD），Geo-MILP迭代优化动作序列和时序以解决几何冲突，促进擦拭与物体重新排列之间的高效协作。随后，我们采用K阶马尔可夫优化（KOMO）生成平滑、无碰撞的运动，以确保可行的执行。仿真和物理实验表明，所提框架不仅通过触及被遮挡区域提高了可达覆盖范围，还与任务与运动规划（TAMP）基线相比显著提升了时间效率，规划时间最多减少9倍，执行总时长缩短约40%。
英文摘要：
We devise a dual-arm cooperative planning framework for wiping table surface in the presence of scattered objects. Unlike conventional surface-cleaning strategies that only cover free areas and detour around obstacles, our framework explicitly assigns one arm to rearrange objects while the other performs coverage wiping, thereby enabling access to previously obstructed regions. Specifically, we first construct a set of wiping and object-rearrangement action primitives. Based on these primitives, we introduce Geo-MILP, a geometry-aware task planner formulated as Mixed-Integer Linear Programming (MILP). By integrating Logic-Based Benders Decomposition (LBBD), Geo-MILP iteratively optimizes action sequencingand timing to resolve geometric conflicts, promoting efficient cooperation between wiping and object rearrangement. We then employ K-Order Markov Optimization (KOMO) to generate smooth, collision-free motions to ensure feasible execution. Simulation and physical experiments demonstrate that the proposed framework not only increases reachable coverage by enabling access to obstructed areas, but also significantly improves time efficiency compared to Task and Motion Planning (TAMP) baselines, reducing planning time by up to 9 times and execution makespan by approximately 40%.
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。
