论文标题
学习针对目标和面向目标任务的双功能推动协同策略
Learning Bifunctional Push-grasping Synergistic Strategy for Goal-agnostic and Goal-oriented Tasks
论文作者
论文摘要
目标不合时宜的任务和面向目标的任务都具有机器人掌握的实用价值：目标 - 敏锐的任务针对工作空间中的所有对象，而面向目标的任务旨在掌握预分配预分配的目标对象。但是，当前大多数抓握方法只能在应对一项任务方面更好。在这项工作中，我们提出了针对目标和面向目标的握把任务的双功能推动协同策略。我们的方法集成了推动以及抓握，以根据任务要求拾取具有高动作效率的所有对象或预分配的目标对象。我们介绍了一个双功能网络，该网络会吸收视觉观察，并输出Q值的密集像素图，以推动和掌握原始动作，以增加动作空间中的可用样本。然后，我们提出了一个分层增强学习框架，以通过将目标 - 反应任务作为多个面向目标任务的组合来协调这两个任务。为了减少分层框架的训练难度，我们设计了一种两阶段的训练方法，可以分别训练两种类型的任务。我们在模拟中对模型进行预训练，然后将学习的模型转移到现实世界中，而无需任何其他现实世界进行微调。实验结果表明，所提出的方法在任务完成率方面的表现优于现有方法，并掌握了运动数量较小的成功率。补充材料可从https：//github.com/dafaren/learning_biftunctional_push-grasping_synergistrate_strategy_for_goal-agnostic_and_and_goal-goal-oriented_tasks获得
Both goal-agnostic and goal-oriented tasks have practical value for robotic grasping: goal-agnostic tasks target all objects in the workspace, while goal-oriented tasks aim at grasping pre-assigned goal objects. However, most current grasping methods are only better at coping with one task. In this work, we propose a bifunctional push-grasping synergistic strategy for goal-agnostic and goal-oriented grasping tasks. Our method integrates pushing along with grasping to pick up all objects or pre-assigned goal objects with high action efficiency depending on the task requirement. We introduce a bifunctional network, which takes in visual observations and outputs dense pixel-wise maps of Q values for pushing and grasping primitive actions, to increase the available samples in the action space. Then we propose a hierarchical reinforcement learning framework to coordinate the two tasks by considering the goal-agnostic task as a combination of multiple goal-oriented tasks. To reduce the training difficulty of the hierarchical framework, we design a two-stage training method to train the two types of tasks separately. We perform pre-training of the model in simulation, and then transfer the learned model to the real world without any additional real-world fine-tuning. Experimental results show that the proposed approach outperforms existing methods in task completion rate and grasp success rate with less motion number. Supplementary material is available at https: //github.com/DafaRen/Learning_Bifunctional_Push-grasping_Synergistic_Strategy_for_Goal-agnostic_and_Goal-oriented_Tasks
