Active Semi-supervised Grasp Pose Detection with Geometric Consistency

Bai，Fan1; Zhu，Delong1; Cheng，Hu1; Xu，Peng1; Meng，Max Q.H.2,3,4

10.1109/ROBIO54168.2021.9739616

2021

978-1-6654-0536-2

2021 IEEE International Conference on Robotics and Biomimetics, ROBIO 2021

1402-1408

27-31 Dec. 2021

Sanya, China

Learning-based pose detection in robotic grasping has been widely studied because of its generalization ability to deal with unknown objects. However, collecting a large labeled dataset is of great difficulty. There are countless objects in our lives, and there may be a considerable number of grasp poses for each object. Thus, it is impossible to label all the grasp poses. In this paper, we propose an active semi-supervised grasp pose detection strategy, in which we use the feature of grasp geometric consistency for data selection and training. Our method can select the most valuable samples to annotate based on the geometric consistency. As far as we know, this is the first work that leverages active learning and semi-supervised learning to solve the problem of grasp data. We experimentally verify that our method, which uses 66% selected data, outperforms random selection, which uses all labeled data, and achieves the best performance compared with the baseline and other well-known methods.

Training Costs Manuals Grasping Semisupervised learning Feature extraction Labeling

其他

英语

EI

20221611977341

Gesture recognition ; Large dataset ; Robotics

Data Processing and Image Processing:723.2 ; Robotics:731.5 ; Mathematics:921

2-s2.0-85128187656

Scopus

1.Chinese University of Hong Kong,Faculty of Engineering,Department of Electronic Engineering,Hong Kong,Hong Kong
2.Southern University of Science and Technology,Department of Electronic and Electrical Engineering,Shenzhen,China
3.Department of Electronic and Electrical Engineering,Chinese University of Hong Kong,Hong Kong,Hong Kong
4.Shenzhen Research Institute,Chinese University of Hong Kong,Shenzhen,China

Bai，Fan,Zhu，Delong,Cheng，Hu,et al. Active Semi-supervised Grasp Pose Detection with Geometric Consistency[C],2021:1402-1408.