In complex scenarios where typical pick-and-place techniques are insufficient, often non-prehensile manipulation can ensure that a robot is able to fulfill its task. However, non-prehensile manipulation is challenging due to its underactuated nature with hybrid-dynamics, where a robot needs to reason about an object's long-term behavior and contact-switching, while being robust to contact uncertainty. The presence of clutter in the workspace further complicates this task, introducing the need to include more advanced spatial analysis to avoid unwanted collisions. Building upon prior work on reinforcement learning with multimodal categorical exploration for planar pushing, we propose to incorporate location-based attention to enable robust manipulation in cluttered scenes. Unlike previous approaches addressing this obstacle avoiding pushing task, our framework requires no predefined global paths and considers the desired target orientation of the manipulated object. Experimental results in simulation as well as with a real KUKA iiwa robot arm demonstrate that our learned policy manipulates objects successfully while avoiding collisions through complex obstacle configurations, including dynamic obstacles, to reach the desired target pose.

翻译：在典型抓取放置技术不足的复杂场景中，非抓取式操作通常能确保机器人完成任务。然而，由于具有混合动力学的欠驱动特性，非抓取式操作具有挑战性：机器人需要推理物体的长期行为和接触切换，同时对接触不确定性保持鲁棒性。工作空间中杂物的存在进一步复杂化了该任务，这需要引入更先进的空间分析以避免非预期碰撞。基于先前关于平面推动中多模态分类探索强化学习的研究，我们提出融入基于位置的注意力机制，以实现杂乱场景中的鲁棒操作。与以往解决避障推动任务的方法不同，我们的框架无需预定义全局路径，并考虑了被操作物体的期望目标朝向。仿真实验以及真实KUKA iiwa机械臂的实验结果表明，我们学习到的策略能够成功操作物体，在包括动态障碍物在内的复杂障碍物配置中实现避碰，最终达到期望的目标位姿。