A Proposed Grasp Detection Based on Improved Dense Convolutional Neural Networks

The skill of grasping is one of fundamental and primary skills for a robot. In this work, we present a lightweight one-stage algorithm for generating robot grasping pose estimates, which outputs the grasping pose and grasping quality predictions on each pixel directly end-to-end through a backbone network. To address the problems of long detection time and large computational effort of the two-target detection algorithm, pixel-level learning is performed by combining an improved Dense-Attention module for deep feature extraction to achieve grasping pose estimation. Compared to other complex architectures of similar grasp networks our network has fewer parameters and detects quickly thus better meeting the requirements of real-time detection. Furthermore, we investigate the different input data impact in terms of performance of the algorithm. We find that input depth data and RGB data to the method can get the best performance and achieves the highest detection accuracy, despite reducing the computational speed to some extent.