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.

翻译：本研究探讨了以拾取-投掷作为拾取-放置的替代方案，使机器人能够扩展工作范围并提升任务效率。尽管投掷操作能提高物体排列效率，但放置环境会显著影响投掷成功率。为实现精确高效的物体排列，我们提出根据放置环境估计的任务难度，在拾取-放置与拾取-投掷操作间进行自适应选择。该方法通过自监督学习同步掌握投掷动作，并借助暴力搜索策略确定任务执行方案。仿真实验与真实场景中多种矩形物体排列的测试结果验证了所提方法的有效性。