根据任务难易度选择抓取放置或投掷，提升机器人摆放效率
Task-Difficulty-Aware Efficient Object Arrangement Leveraging Tossing Motions
- 基于环境信息动态判断该用抓放还是投掷
- 自监督学习投掷动作，暴力搜索最优决策策略
- 实测与仿真验证在多种布局下有效提升效率
本研究探索将抓取-投掷（PT）作为抓取-放置（PP）的替代方案，使机器人扩展作业范围并提高任务效率。尽管PT能提升摆放效率，但放置环境对投掷成功率有显著影响。为实现精准高效的物体排列，我们提出根据放置环境估算任务难度，决定采用PP或PT。方法通过自监督学习同时训练投掷动作与基于暴力搜索的决策策略。实验结果在多种矩形物体排列场景的模拟与真实测试中均验证了该方法的有效性。
原文摘要 · Abstract (English)
This study explores a pick-and-toss (PT) as an alternative to pick-and-place (PP), allowing a robot to extend its range and improve task efficiency. Although PT boosts efficiency in object arrangement, the placement environment critically affects the success of tossing. To achieve accurate and efficient object arrangement, we suggest choosing between PP and PT based on task difficulty estimated from the placement environment. Our method simultaneously learns the tossing motion through self-supervised learning and the task determination policy via brute-force search. Experimental results validate the proposed method through simulations and real-world tests on various rectangular object arrangements.
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