Deep Learning Reactive Robotic Grasping With a Versatile Vacuum Gripper

Hui Zhang; Jef Peeters; Eric DemeesterKarel Kellens

首页> 外文期刊>IEEE Transactions on Robotics: A publication of the IEEE Robotics and Automation Society >Deep Learning Reactive Robotic Grasping With a Versatile Vacuum Gripper

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Deep Learning Reactive Robotic Grasping With a Versatile Vacuum Gripper

机译：Deep Learning Reactive Robotic Grasping With a Versatile Vacuum Gripper

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In this article, a six-step approach is proposed to simulate the grasp and evaluate the grasp quality for a versatile vacuum gripper by tracking the deformation and force-torque wrench of the gripping pad. Over 100 K synthetic grasps are generated for neural network training. Furthermore, a gripping attention convolutional neural network (GA-CNN) is developed to predict the grasp quality for real-world grasp, running by 15 Hz closed-loop control with the real-time robotic observation and force-torque feedback. Various experiments in both the simulation and physical grasps indicate that our GA-CNN can focus on the crucial region of the soft gripping pad to predict grasp qualities and perform a lower average error compared with a same-scale traditional CNN. In addition, the complexity of grasping clutters is defined from Level 1 to Level 9. The proposed grasping method achieves an average success rate of 90.2 for static clutters at Level 1 to Level 8 and an average success rate of >80.0 for dynamic grasping at Level 1 to Level 7, which outperforms state-of-the-art grasping methods.

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《IEEE Transactions on Robotics: A publication of the IEEE Robotics and Automation Society》 |2023年第2期|1244-1259|共16页
作者
Hui Zhang; Jef Peeters; Eric DemeesterKarel Kellens;
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作者单位

ACRO Research Group, Department of Mechanical Engineering, Diepenbeek, Belgium|Flanders Make ROB Core Lab@ KU Leuven, Leuven, Belgium;

LCE Research Group, Department of Mechanical Engineering, Heverlee, Belgium|Flanders Make VCCM Core Lab@ KU Leuven, Leuven, Belgium;

ACRO Research Group, Department of Mechanical Engineering, Diepenbeek, Belgium;

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原文格式 PDF
正文语种英语
中图分类机器人技术;
关键词
Grippers; Grasping; Neural networks; Solid modeling; Robot sensing systems; Robot kinematics; Deep learning;

Deep Learning Reactive Robotic Grasping With a Versatile Vacuum Gripper

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