Abstract
As robots begin to collaborate with humans in their daily work spaces, they need to have a deeper understanding of the tasks of using tools. In response to the problem of using tools in collaboration between humans and robots, we propose a modular system based on collaborative tasks. The first part of the system is designed to find task-related operating areas, and a multi-layer instance segmentation network is used to find the tools needed for the task, and classify the object itself based on the state of the robot in the collaborative task. Thus, we generate the state semantic region with the "leader-assistant" state. In the second part, in order to predict the optimal grasp and handover configuration, a multi-scale grasping network (MGR-Net) based on the mask of state semantic area is proposed, it can better adapt to the change of the receptive field caused by the state semantic region. Compared with the traditional method, our method has higher accuracy. The whole system also achieves good results on untrained real-world tool dataset we constructed. To further verify the effectiveness of our generated grasp representations, A robot platform based on Sawyer is used to prove the high performance of our system.