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. Task-agnostic
grasping optimizes for grasp robustness while ignoring crucial task-specific
constraints. In this paper, 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.
Supplementary material is available at: bit.ly/task-oriented-grasp

本文提出了一个任务导向抓取网络 (TOG-Net)，通过大规模模拟自我监督与程序生成的工具对象，同时优化工具的任务导向抓取和操纵策略。模型在打扫和敲打两个任务中表现出较高的成功率，并取得了显著的效果。