Robotic Grasp Detection using Deep Convolutional Neural Networks

TL;DR

This paper introduces a deep learning-based system for robotic grasp detection that predicts optimal grasping poses from RGB-D images, achieving high accuracy and real-time performance, advancing robotic manipulation capabilities.

Contribution

It presents a novel multi-modal deep convolutional neural network architecture for robotic grasp detection, achieving state-of-the-art accuracy and real-time operation.

Findings

Achieved 89.21% accuracy on Cornell Grasp Dataset.

Operates at real-time speeds.

Redefines the state-of-the-art in robotic grasp detection.

Abstract

Deep learning has significantly advanced computer vision and natural language processing. While there have been some successes in robotics using deep learning, it has not been widely adopted. In this paper, we present a novel robotic grasp detection system that predicts the best grasping pose of a parallel-plate robotic gripper for novel objects using the RGB-D image of the scene. The proposed model uses a deep convolutional neural network to extract features from the scene and then uses a shallow convolutional neural network to predict the grasp configuration for the object of interest. Our multi-modal model achieved an accuracy of 89.21% on the standard Cornell Grasp Dataset and runs at real-time speeds. This redefines the state-of-the-art for robotic grasp detection.