Effectively rearranging heterogeneous objects constitutes a high-utility skill that an intelligent robot should master. Whereas significant work has been devoted to the grasp synthesis of heterogeneous objects, little attention has been given to the planning for sequentially manipulating such objects. In this work, we examine the long-horizon sequential rearrangement of heterogeneous objects in a tabletop setting, addressing not just generating feasible plans but near-optimal ones. Toward that end, and building on previous methods, including combinatorial algorithms and Monte Carlo tree search-based solutions, we develop state-of-the-art solvers for optimizing two practical objective functions considering key object properties such as size and weight. Thorough simulation studies show that our methods provide significant advantages in handling challenging heterogeneous object rearrangement problems, especially in cluttered settings. Real robot experiments further demonstrate and confirm these advantages. Source code and evaluation data associated with this research will be available at https://github.com/arc-l/TRLB upon the publication of this manuscript.

翻译：有效整理异构物体是智能机器人应掌握的高价值技能。尽管已有大量工作致力于异构物体的抓取合成，但针对此类物体的顺序操作规划研究仍显不足。本文聚焦桌面场景下异构物体的长期顺序重排问题，不仅致力于生成可行规划，更追求接近最优的规划方案。为此，我们在现有方法（包括组合算法和基于蒙特卡洛树搜索的解决方案）基础上，开发了用于优化两个实用目标函数的最新技术求解器，该求解器充分考虑物体尺寸、重量等关键属性。全面的仿真研究表明，本方法在处理复杂异构物体重排问题（尤其高杂乱场景）时具有显著优势。真实机器人实验进一步验证并确认了这些优势。本研究的源代码与评估数据将在论文正式发表后发布于https://github.com/arc-l/TRLB。