2018/06/25 by Kuan Fang, Yuke Zhu, Fang, Kuan +11 · 6 citations
Engineering · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Muscle activation and electromyography studies #Robot Manipulation and Learning #Robotics (cs.RO) #Soft Robotics and Applications
paper · pdf · doi:10.48550/arxiv.1806.09266
openalex created_date 2018/06/13 · openalex publication_date 2018/06/25 · openalex updated_date 2026/07/28
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