Efficient tabletop rearrangement remains challenging due to collisions and the need for temporary buffering when target poses are obstructed. Prehensile pick-and-place provides precise control but often requires extra moves, whereas non-prehensile pushing can be more efficient but suffers from complex, imprecise dynamics. This paper proposes push-placement, a hybrid action primitive that uses the grasped object to displace obstructing items while being placed, thereby reducing explicit buffering. The method is integrated into a physics-in-the-loop Monte Carlo Tree Search (MCTS) planner and evaluated in the PyBullet simulator. Empirical results show push-placement reduces the manipulator travel cost by up to 11.12% versus a baseline MCTS planner and 8.56% versus dynamic stacking. These findings indicate that hybrid prehensile/non-prehensile action primitives can substantially improve efficiency in long-horizon rearrangement tasks.

翻译：由于碰撞以及目标位姿被遮挡时需要临时缓冲，高效的桌面重排任务仍具挑战性。抓取式的拾放操作提供了精确控制，但通常需要额外的移动；而非抓取式的推动操作可能更高效，却受制于复杂且不精确的动力学特性。本文提出推放，这是一种混合动作基元，它在放置被抓取物体的同时，利用该物体来移开遮挡物，从而减少显式缓冲。该方法被集成到一个物理在环的蒙特卡洛树搜索规划器中，并在 PyBullet 模拟器中进行评估。实验结果表明，与基线 MCTS 规划器相比，推放操作可将机械臂移动成本降低高达 11.12%；与动态堆叠方法相比，可降低 8.56%。这些发现表明，融合抓取/非抓取的混合动作基元能显著提升长视野重排任务的效率。