TY - GEN
T1 - BGRN
T2 - 23rd International Conference on Industrial Informatics, INDIN 2025
AU - Li, Shixiang
AU - Dong, Jiabao
AU - Kong, Yusheng
AU - Wang, Haiteng
AU - Jia, Zidi
AU - Ren, Lei
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - Grasping tasks are crucial in industrial manufacturing, where precise and efficient object grasping ensures smooth assembly processes and stable operations. Robotic arms, deployed in industrial environments, require timely and accurate computations to perform these tasks. This paper introduces BGRN, an RGB-D fusion-based binary grasp prediction network designed for lightweight grasp pose prediction in such settings. We propose a binary grasp pose prediction framework that significantly reduces memory usage by quantizing both weights and activations to 1 bit. Additionally, an interaction fusion module improves the integration of RGB and depth images, while an information recovery connection helps mitigate feature loss caused by binarization. Experimental results show that BGRN achieves competitive accuracy and notable reductions in memory usage and computational load compared to full-precision models.
AB - Grasping tasks are crucial in industrial manufacturing, where precise and efficient object grasping ensures smooth assembly processes and stable operations. Robotic arms, deployed in industrial environments, require timely and accurate computations to perform these tasks. This paper introduces BGRN, an RGB-D fusion-based binary grasp prediction network designed for lightweight grasp pose prediction in such settings. We propose a binary grasp pose prediction framework that significantly reduces memory usage by quantizing both weights and activations to 1 bit. Additionally, an interaction fusion module improves the integration of RGB and depth images, while an information recovery connection helps mitigate feature loss caused by binarization. Experimental results show that BGRN achieves competitive accuracy and notable reductions in memory usage and computational load compared to full-precision models.
KW - Binary Neural Network
KW - Grasping
KW - Industrial Edge Intelligence
UR - https://www.scopus.com/pages/publications/105032698709
U2 - 10.1109/INDIN64977.2025.11279214
DO - 10.1109/INDIN64977.2025.11279214
M3 - 会议稿件
AN - SCOPUS:105032698709
T3 - IEEE International Conference on Industrial Informatics (INDIN)
BT - 2025 IEEE 23rd International Conference on Industrial Informatics, INDIN 2025
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 12 July 2025 through 15 July 2025
ER -