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：有效整理异质物品是智能机器人应掌握的一项高实用性技能。尽管已有大量工作致力于异质物品的抓取综合研究，但针对此类物品顺序操作的规划却鲜少受到关注。本文研究桌面上异质物品的长期顺序重排问题，不仅旨在生成可行规划，更要实现近最优规划。为此，我们基于现有方法（包括组合算法和基于蒙特卡洛树搜索的解决方案），开发了用于优化两个实用目标函数（考虑尺寸、重量等关键物体属性）的先进求解器。充分的仿真研究表明，我们的方法在处理具有挑战性的异质物品重排问题（尤其是杂乱场景）时具有显著优势。真实机器人实验进一步验证并证实了这些优势。本研究的源代码及评估数据将在论文发表后于 https://github.com/arc-l/TRLB 公开。