BGRN: A Binarized Multimodal Fusion Grasp Prediction Network with Information Recovery Connection

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.