让机器人像人一样推物重排，更高效直观。
Object-Centric Kinodynamic Planning for Nonprehensile Robot Rearrangement Manipulation
- 以物体为中心规划运动路径，再由机器人实时推动物体实现
- 在仿真和真实机器人上均显著提升任务效率与动作合理性
- 首次提出非抓取重排的标准化评测协议，适合机器人研究者
非抓取操作（如推移）在多物体重排任务中至关重要。传统方法生成的机器人中心动作不符合人类直觉且效率低下。为此，本文采用物体中心规划范式，提出统一框架以应对大规模、物理复杂的非抓取重排问题，克服建模误差与现实不确定性。假设每个物体可自主移动，规划器先计算目标物体运动，再通过闭环推拉策略在线生成机器人动作。大量实验表明，该框架在仿真与真实机器人上均生成更直观、高效的机器人动作。此外，本文还提出一套基准评测协议，推动非抓取重排研究的标准化发展。
原文摘要 · Abstract (English)
Nonprehensile actions such as pushing are crucial for addressing multi-object rearrangement problems. Many traditional methods generate robot-centric actions, which differ from intuitive human strategies and are typically inefficient. To this end, we adopt an object-centric planning paradigm and propose a unified framework for addressing a range of large-scale, physics-intensive nonprehensile rearrangement problems challenged by modeling inaccuracies and real-world uncertainties. By assuming each object can actively move without being driven by robot interactions, our planner first computes desired object motions, which are then realized through robot actions generated online via a closed-loop pushing strategy. Through extensive experiments and in comparison with state-of-the-art baselines in both simulation and on a physical robot, we show that our object-centric planning framework can generate more intuitive and task-effective robot actions with significantly improved efficiency. In addition, we propose a benchmarking protocol to standardize and facilitate future research in nonprehensile rearrangement.
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