Tool manipulation is vital for facilitating robots to complete challenging task goals. It requires reasoning about the desired effect of the task and, thus, properly grasping and manipulating the tool to achieve the task. Most work in robotics has focused on task-agnostic grasping, which optimizes for only grasp robustness without considering the subsequent manipulation tasks. In this article, we propose the Task-Oriented Grasping Network (TOG-Net) to jointly optimize both task-oriented grasping of a tool and the manipulation policy for that tool. The training process of the model is based on large-scale simulated self-supervision with procedurally generated tool objects. We perform both simulated and real-world experiments on two tool-based manipulation tasks: sweeping and hammering. Our model achieves overall 71.1% task success rate for sweeping and 80.0% task success rate for hammering.

中文翻译：

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从模拟自我监督中学习面向任务的工具操作抓取

工具操作对于促进机器人完成具有挑战性的任务目标至关重要。它需要对任务的预期效果进行推理，从而正确掌握和操纵工具来完成任务。机器人学中的大多数工作都集中在与任务无关的抓取上，它仅针对抓取鲁棒性进行优化，而不考虑后续的操作任务。在本文中，我们提出了面向任务的抓取网络（TOG-Net）来共同优化面向任务的工具抓取和该工具的操作策略。该模型的训练过程是基于程序生成的工具对象的大规模模拟自我监督。我们对两个基于工具的操作任务进行了模拟和现实世界的实验：扫掠和锤击。我们的模型总体达到了 71。