Learning Task-Oriented Grasping for Tool Manipulation from Simulated Self-Supervision

Autor:	Fang, Kuan, Zhu, Yuke, Garg, Animesh, Kurenkov, Andrey, Mehta, Viraj, Fei-Fei, Li, Savarese, Silvio
Rok vydání:	2018
Předmět:	Computer Science - Robotics Computer Science - Computer Vision and Pattern Recognition Computer Science - Machine Learning Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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 Comment: RSS 2018
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1806.09266 Zobrazit plný text záznamu View this record from Arxiv