# Robotic Grasp Detection using Deep Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1611.08036 · HTML: https://mlsanity.com/paper/1611.08036/

## Key facts

- Cited by 47 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Nov 2016.

## Built on by (most cited first, 30 of 47)

- [Open-World Object Manipulation using Pre-trained Vision-Language Models](https://mlsanity.com/paper/2303.00905/) (2023)
- [Graspness Discovery in Clutters for Fast and Accurate Grasp Detection](https://mlsanity.com/paper/2406.11142/) (2024)
- [RGB Matters: Learning 7-DoF Grasp Poses on Monocular RGBD Images](https://mlsanity.com/paper/2103.02184/) (2021)
- [A Survey of Embodied Learning for Object-Centric Robotic Manipulation](https://mlsanity.com/paper/2408.11537/) (2024)
- [MonoGraspNet: 6-DoF Grasping with a Single RGB Image](https://mlsanity.com/paper/2209.13036/) (2022)
- [A Joint Network for Grasp Detection Conditioned on Natural Language Commands](https://mlsanity.com/paper/2104.00492/) (2021)
- [Open-sourced Data Ecosystem in Autonomous Driving: the Present and Future](https://mlsanity.com/paper/2312.03408/) (2023)
- [Exploring Visual Pre-training for Robot Manipulation: Datasets, Models and Methods](https://mlsanity.com/paper/2308.03620/) (2023)
- [Simultaneous Semantic and Collision Learning for 6-DoF Grasp Pose Estimation](https://mlsanity.com/paper/2108.02425/) (2021)
- [When Transformer Meets Robotic Grasping: Exploits Context for Efficient Grasp Detection](https://mlsanity.com/paper/2202.11911/) (2022)
- [REGRAD: A Large-Scale Relational Grasp Dataset for Safe and Object-Specific Robotic Grasping in Clutter](https://mlsanity.com/paper/2104.14118/) (2021)
- [DA2 Dataset: Toward Dexterity-Aware Dual-Arm Grasping](https://mlsanity.com/paper/2208.00408/) (2022)
- [RGB-D Grasp Detection via Depth Guided Learning with Cross-modal Attention](https://mlsanity.com/paper/2302.14264/) (2023)
- [Lightweight Language-driven Grasp Detection using Conditional Consistency Model](https://mlsanity.com/paper/2407.17967/) (2024)
- [GKNet: grasp keypoint network for grasp candidates detection](https://mlsanity.com/paper/2106.08497/) (2021)
- [A Hybrid Approach for Learning to Shift and Grasp with Elaborate Motion Primitives](https://mlsanity.com/paper/2111.01510/) (2021)
- [Environment Optimization for Multi-Agent Navigation](https://mlsanity.com/paper/2209.11279/) (2022)
- [Constrained Environment Optimization for Prioritized Multi-Agent Navigation](https://mlsanity.com/paper/2305.11260/) (2023)
- [Learning suction graspability considering grasp quality and robot reachability for bin-picking](https://mlsanity.com/paper/2111.02571/) (2021)
- [I Know What You Draw: Learning Grasp Detection Conditioned on a Few Freehand Sketches](https://mlsanity.com/paper/2205.04026/) (2022)
- [Hybrid Physical Metric For 6-DoF Grasp Pose Detection](https://mlsanity.com/paper/2206.11141/) (2022)
- [GraNet: A Multi-Level Graph Network for 6-DoF Grasp Pose Generation in Cluttered Scenes](https://mlsanity.com/paper/2312.03345/) (2023)
- [Depth-aware Object Segmentation and Grasp Detection for Robotic Picking Tasks](https://mlsanity.com/paper/2111.11114/) (2021)
- [Open Arms: Open-Source Arms, Hands & Control](https://mlsanity.com/paper/2205.12992/) (2022)
- [NBMOD: Find It and Grasp It in Noisy Background](https://mlsanity.com/paper/2306.10265/) (2023)
- [Improving the Successful Robotic Grasp Detection Using Convolutional Neural Networks](https://mlsanity.com/paper/2403.05211/) (2024)
- [Double-Dot Network for Antipodal Grasp Detection](https://mlsanity.com/paper/2108.01527/) (2021)
- [A Robotic Visual Grasping Design: Rethinking Convolution Neural Network with High-Resolutions](https://mlsanity.com/paper/2209.07459/) (2022)
- [MotorFactory: A Blender Add-on for Large Dataset Generation of Small Electric Motors](https://mlsanity.com/paper/2301.05028/) (2023)
- [HMT-Grasp: A Hybrid Mamba-Transformer Approach for Robot Grasping in Cluttered Environments](https://mlsanity.com/paper/2410.03522/) (2024)

## Abstract (arXiv)

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.