Densely Supervised Grasp Detector (DSGD)

TL;DR

This paper introduces DSGD, a deep learning framework that combines multi-level feature fusion to detect stable grasps at various image hierarchy levels, achieving high accuracy and real-time performance.

Contribution

The novel aspect is the hierarchical, densely supervised approach that integrates global, region, and pixel-level grasp predictions for improved detection.

Findings

Achieves 97% grasp detection accuracy on Cornell dataset.

Attains 90% success rate in real-world robotic grasping.

Operates in real-time with high stability on unseen objects.

Abstract

This paper presents Densely Supervised Grasp Detector (DSGD), a deep learning framework which combines CNN structures with layer-wise feature fusion and produces grasps and their confidence scores at different levels of the image hierarchy (i.e., global-, region-, and pixel-levels). % Specifically, at the global-level, DSGD uses the entire image information to predict a grasp. At the region-level, DSGD uses a region proposal network to identify salient regions in the image and predicts a grasp for each salient region. At the pixel-level, DSGD uses a fully convolutional network and predicts a grasp and its confidence at every pixel. % During inference, DSGD selects the most confident grasp as the output. This selection from hierarchically generated grasp candidates overcomes limitations of the individual models. % DSGD outperforms state-of-the-art methods on the Cornell grasp dataset in terms of grasp accuracy. % Evaluation on a multi-object dataset and real-world robotic grasping experiments show that DSGD produces highly stable grasps on a set of unseen objects in new environments. It achieves 97% grasp detection accuracy and 90% robotic grasping success rate with real-time inference speed.