论文标题
通过预测稳健，精确和以任务为导向的掌握机器人组装
Towards Robotic Assembly by Predicting Robust, Precise and Task-oriented Grasps
论文作者
论文摘要
强大的以任务为导向的GRASP计划对于自动机器人精确装配任务至关重要。在确定正确的掌握时，应纳入对象的几何形状和目标任务的先决条件的知识。但是，有几个因素导致了实现这些抓取的挑战，例如控制机器人时噪声，未知对象属性以及对复杂对象对象相互作用进行建模的困难。我们提出了一种分解此问题的方法，并通过学习三个级联网络来优化掌握鲁棒性，精度和任务性能。我们在模拟三个常见的组装任务中评估了我们的方法：将齿轮插入钉子上，将括号对准在角落中，然后将形状插入插槽中。我们的策略是使用基于大规模自我监督的掌握模拟的课程来培训的，并使用程序生成的对象进行了培训。最后，我们使用真正的机器人评估了前两个任务的性能，在该机器人中，我们的方法在插入括号插入的4.28mm误差和齿轮插入的1.44mm误差。
Robust task-oriented grasp planning is vital for autonomous robotic precision assembly tasks. Knowledge of the objects' geometry and preconditions of the target task should be incorporated when determining the proper grasp to execute. However, several factors contribute to the challenges of realizing these grasps such as noise when controlling the robot, unknown object properties, and difficulties modeling complex object-object interactions. We propose a method that decomposes this problem and optimizes for grasp robustness, precision, and task performance by learning three cascaded networks. We evaluate our method in simulation on three common assembly tasks: inserting gears onto pegs, aligning brackets into corners, and inserting shapes into slots. Our policies are trained using a curriculum based on large-scale self-supervised grasp simulations with procedurally generated objects. Finally, we evaluate the performance of the first two tasks with a real robot where our method achieves 4.28mm error for bracket insertion and 1.44mm error for gear insertion.
