Learning Task-Oriented Grasping for Tool Manipulation

from Simulated Self-Supervision

Kuan Fang, Yuke Zhu, Animesh Garg, Andrey Kurenkov, Viraj Mehta, Li Fei-Fei, Silvio Savarese


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. 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.



title={Learning Task-Oriented Grasping for Tool Manipulation from Simulated Self-Supervision},

author={Kuan Fang and Yuke Zhu and Animesh Garg and Andrey Kuryenkov and Viraj Mehta and Li Fei-Fei and Silvio Savarese},

journal={Robotics: Science and Systems (RSS)},



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