In this work, we introduce PokeRRT, a novel motion planning algorithm that demonstrates poking as an effective non-prehensile manipulation skill to enable fast manipulation of objects and increase the size of a robot's reachable workspace. We showcase poking as a failure recovery tactic used synergistically with pick-and-place for resiliency in cases where pick-and-place initially fails or is unachievable. Our experiments demonstrate the efficiency of the proposed framework in planning object trajectories using poking manipulation in uncluttered and cluttered environments. In addition to quantitatively and qualitatively demonstrating the adaptability of PokeRRT to different scenarios in both simulation and real-world settings, our results show the advantages of poking over pushing and grasping in terms of success rate and task time.

翻译：在本工作中，我们提出了PokeRRT——一种新颖的运动规划算法，该算法展示了戳动作为一种有效的非抓取操作技能，能够实现物体的快速操控并扩大机器人的可达工作空间。我们展示了戳动作为故障恢复策略，可与抓放操作协同使用，从而在抓放操作初始失败或无法实现时提升系统鲁棒性。实验证明，所提框架在无障碍与有障碍环境中利用戳动操作规划物体轨迹具有高效性。除了通过定量与定性方法展示PokeRRT在仿真和真实场景中对不同情况的适应性外，我们的结果还表明，在成功率和任务耗时方面，戳动操作相较于推动和抓取具有明显优势。