Non-prehensile pushing actions have the potential to singulate a target object from its surrounding clutter in order to facilitate the robotic grasping of the target. To address this problem we utilize a heuristic rule that moves the target object towards the workspace's empty space and demonstrate that this simple heuristic rule achieves singulation. Furthermore, we incorporate this heuristic rule to the reward in order to train more efficiently reinforcement learning (RL) agents for singulation. Simulation experiments demonstrate that this insight increases performance. Finally, our results show that the RL-based policy implicitly learns something similar to one of the used heuristics in terms of decision making.

翻译：非致命推力行动有可能将目标对象从周围的杂乱中挑选出来,以便利机器人掌握目标。为了解决这一问题,我们使用了一种超自然规则,将目标对象移到工作空间的空空空间,并表明这种简单的超自然规则可以实现单向。此外,我们将这种超自然规则纳入奖励,以便更有效地训练强化学习(RL)剂进行制成。模拟实验表明,这种洞察力可以提高性能。最后,我们的结果显示,基于RL的政策在决策方面隐含了类似于用过的超自然学的东西。