Grasp Pose Detection from a Single RGB Image

Cheng, Hu1; Wang, Yingying1; Meng, Max Q-H2,3

10.1109/IROS51168.2021.9636511

2021

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

2153-0858

978-1-6654-1715-0

2021 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS)

4686-4691

SEP 27-OCT 01, 2021

null,null,ELECTR NETWORK

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

Grasp pose detection generates the position and orientation of the robot end-effector to grasp objects from the RGB or RGB-D image. In this paper, we propose a novel grasp pose detection network that generates 3-DOF grasp poses using the RGB image. The network follows the anchor-based object detection pipeline and incorporates the angle detection unit. Furthermore, we redesign the grasp angle predictor with a classification unit to increase the accuracy of grasp pose rotation estimation. Our method classifies the prediction angle densely in contrast with the previous regression method or sparse classification method. Moreover, an angle smooth label is designed to avoid the sudden change of the angle regression loss caused by the periodic property of the angle. We validate our algorithm on Cornell Grasp Dataset and obtain a higher detection accuracy than the state-of-the-art method. The real scenario experiment also proves the effectiveness of our method. The robot equipped with the parallel gripper achieves a 96:4% grasp success rate.

Pipelines Estimation Object detection Prediction algorithms End effectors Classification algorithms Grippers

通讯

英语

CPCI-S ; EI

Shenzhen Key Laboratory of Robotics Perception and Intelligence[ZDSYS-20200810171800001]

Automation & Control Systems ; Computer Science ; Engineering ; Robotics

Automation & Control Systems ; Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic ; Robotics

WOS:000755125503105

20220711624141

End effectors ; Gesture recognition ; Intelligent robots ; Object detection ; Robotics

Data Processing and Image Processing:723.2 ; Robotics:731.5 ; Robot Applications:731.6 ; Mathematical Statistics:922.2

Web of Science

1.Chinese Univ Hong Kong, Dept Elect Engn, Robot Percept & Artificial Intelligence Lab, Hong Kong, Peoples R China
2.Southern Univ Sci & Technol, Dept Elect & Elect Engn, Shenzhen, Peoples R China
3.Chinese Univ Hong Kong, Shenzhen Res Inst, Shenzhen, Peoples R China

Cheng, Hu,Wang, Yingying,Meng, Max Q-H. Grasp Pose Detection from a Single RGB Image[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2021:4686-4691.