用抓取物推移障碍物，减少搬运成本。
Push-Placement: A Hybrid Approach Integrating Prehensile and Non-Prehensile Manipulation for Object Rearrangement
- 抓取时同步推动障碍物，减少额外移动。
- 实验显示搬运距离降低11.12%（对比基线）。
- 适合长周期桌面重排任务的机器人规划。
高效桌面上的物品重排仍面临碰撞和目标位姿被遮挡需临时缓冲的挑战。抓取放置虽精度高但常需额外动作，而非抓取式推动效率更高但动态复杂且不精准。本文提出推放（push-placement）混合动作原语，利用抓取物在放置过程中推动障碍物，从而减少显式缓冲需求。该方法集成于物理驱动的蒙特卡洛树搜索（MCTS）规划器中，并在PyBullet仿真器中评估。实验证明，相较于基线MCTS规划器，推放可降低11.12%的机械臂移动成本；相比动态堆叠方法，降低8.56%。结果表明，混合抓取与非抓取动作原语能显著提升长周期重排任务的效率。
原文摘要 · Abstract (English)
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.
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