Learning task-oriented grasping for tool manipulation from simulated self-supervision. (March 2020)

• Record Type: Journal Article
• Authors: Fang, Kuan; Zhu, Yuke; Garg, Animesh; Kurenkov, Andrey; Mehta, Viraj; Fei-Fei, Li; Savarese, Silvio
• Is Part Of: International journal of robotics research. Volume 39:Number 2/3(2020)
• Page Start: 202
• Page End: 216
• Publication Date: 2020-03
• Subjects: Grasping -- manipulation -- learning and adaptive systems; Robots -- Periodicals; Robots, Industrial -- Periodicals; 629.89205
• DOI: 10.1177/0278364919872545
• Languages: English
• ISSNs: 0278-3649

Abstract

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.