在杂乱桌面上有效重排异构物体
Effectively Rearranging Heterogeneous Objects on Cluttered Tabletops
AI总结：
本研究针对杂乱桌面上的异构物体长时序顺序重排问题，基于组合算法和Monte Carlo tree search开发了能优化大小和重量等属性的先进求解器，仿真与真实机器人实验均证实其在复杂重排任务中的显著优势。
摘要：
有效重排异构物体是智能机器人应掌握的高实用性技能。尽管已有大量工作致力于异构物体的抓取合成，但鲜少关注对此类物体的顺序操作规划。在本研究中，我们探讨了桌面环境下异构物体的长时序顺序重排，不仅生成可行计划，还追求近最优方案。为此，基于组合算法和基于Monte Carlo tree search的解决方案等先前方法，我们开发了最先进的求解器，以优化考虑物体大小和重量等关键属性的两个实用目标函数。详尽的仿真研究表明，我们的方法在处理具有挑战性的异构物体重排问题（尤其是在杂乱环境中）时具有显著优势。真实机器人实验进一步展示并证实了这些优势。与本研究的源代码和评估数据将在本手稿发表时于 https://github.com/arc-l/TRLB 提供。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
