Real-Time Grasp Detection Using Convolutional Neural Networks

2014/12/09 by Joseph Redmon, Anelia Angelova, Redmon, Joseph +1 · 11 citations
Computer Science · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Robotics (cs.RO) #cs.CV #cs.RO

paper · pdf · doi:10.48550/arxiv.1412.3128

Accepted to ICRA 2015

arxiv created 2015/02/28 · arxiv updated 2015/03/03

Abstract

We present an accurate, real-time approach to robotic grasp detection based on convolutional neural networks. Our network performs single-stage regression to graspable bounding boxes without using standard sliding window or region proposal techniques. The model outperforms state-of-the-art approaches by 14 percentage points and runs at 13 frames per second on a GPU. Our network can simultaneously perform classification so that in a single step it recognizes the object and finds a good grasp rectangle. A modification to this model predicts multiple grasps per object by using a locally constrained prediction mechanism. The locally constrained model performs significantly better, especially on objects that can be grasped in a variety of ways.

Cited by

• A Hyperspectral Imaging Guided Robotic Grasping System
• Deep Learning Architectures for Code-Modulated Visual Evoked Potentials Detection
• ActiveGrasp: Information-Guided Active Grasping with Calibrated Energy-based Model
• XGrasp: Gripper-Aware Grasp Detection with Multi-Gripper Data Generation
• VCoT-Grasp: Grasp Foundation Models with Visual Chain-of-Thought Reasoning for Language-driven Grasp Generation
• First Plan Then Evaluate: Use a Vectorized Motion Planner for Grasping
• GraspMAS: Zero-Shot Language-driven Grasp Detection with Multi-Agent System
• Designing Pin-pression Gripper and Learning its Dexterous Grasping with Online In-hand Adjustment
• An Overview of Arithmetic Adaptations for Inference of Convolutional Neural Networks on Re-configurable Hardware
• Visual Affordance Prediction: Survey and Reproducibility
• Prompt-responsive Object Retrieval with Memory-augmented Student-Teacher Learning

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