# Jacquard: A Large Scale Dataset for Robotic Grasp Detection

arXiv: https://arxiv.org/abs/1803.11469 · HTML: https://mlsanity.com/paper/1803.11469/

## Key facts

- Cited by 87 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Mar 2018.

## Built on by (most cited first, 30 of 87)

- [Aligning Cyber Space with Physical World: A Comprehensive Survey on Embodied AI](https://mlsanity.com/paper/2407.06886/) (2024)
- [ContactOpt: Optimizing Contact to Improve Grasps](https://mlsanity.com/paper/2104.07267/) (2021)
- [On Bringing Robots Home](https://mlsanity.com/paper/2311.16098/) (2023)
- [Language Embedded Radiance Fields for Zero-Shot Task-Oriented Grasping](https://mlsanity.com/paper/2309.07970/) (2023)
- [In-Context Imitation Learning via Next-Token Prediction](https://mlsanity.com/paper/2408.15980/) (2024)
- [ContactGen: Generative Contact Modeling for Grasp Generation](https://mlsanity.com/paper/2310.03740/) (2023)
- [End-to-end Trainable Deep Neural Network for Robotic Grasp Detection and Semantic Segmentation from RGB](https://mlsanity.com/paper/2107.05287/) (2021)
- [Robot Utility Models: General Policies for Zero-Shot Deployment in New Environments](https://mlsanity.com/paper/2409.05865/) (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)
- [Towards Robust and Secure Embodied AI: A Survey on Vulnerabilities and Attacks](https://mlsanity.com/paper/2502.13175/) (2025)
- [Grasp-Anything: Large-scale Grasp Dataset from Foundation Models](https://mlsanity.com/paper/2309.09818/) (2023)
- [Language-driven Grasp Detection](https://mlsanity.com/paper/2406.09489/) (2024)
- [Humanoid Everyday: A Comprehensive Robotic Dataset for Open-World Humanoid Manipulation](https://mlsanity.com/paper/2510.08807/) (2025)
- [Learning High-DOF Reaching-and-Grasping via Dynamic Representation of Gripper-Object Interaction](https://mlsanity.com/paper/2204.13998/) (2022)
- [MonoGraspNet: 6-DoF Grasping with a Single RGB Image](https://mlsanity.com/paper/2209.13036/) (2022)
- [Language-guided Robot Grasping: CLIP-based Referring Grasp Synthesis in Clutter](https://mlsanity.com/paper/2311.05779/) (2023)
- [Towards Scale Balanced 6-DoF Grasp Detection in Cluttered Scenes](https://mlsanity.com/paper/2212.05275/) (2022)
- [Learning 6-DoF Fine-grained Grasp Detection Based on Part Affordance Grounding](https://mlsanity.com/paper/2301.11564/) (2023)
- [Towards Open-World Grasping with Large Vision-Language Models](https://mlsanity.com/paper/2406.18722/) (2024)
- [Simultaneous Semantic and Collision Learning for 6-DoF Grasp Pose Estimation](https://mlsanity.com/paper/2108.02425/) (2021)
- [MetaGraspNet: A Large-Scale Benchmark Dataset for Scene-Aware Ambidextrous Bin Picking via Physics-based Metaverse Synthesis](https://mlsanity.com/paper/2208.03963/) (2022)
- [ZeroGrasp: Zero-Shot Shape Reconstruction Enabled Robotic Grasping](https://mlsanity.com/paper/2504.10857/) (2025)
- [When Transformer Meets Robotic Grasping: Exploits Context for Efficient Grasp Detection](https://mlsanity.com/paper/2202.11911/) (2022)
- [Visuo-Haptic Object Perception for Robots: An Overview](https://mlsanity.com/paper/2203.11544/) (2022)
- [On Robot Grasp Learning Using Equivariant Models](https://mlsanity.com/paper/2306.06489/) (2023)
- [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)
- [Generalizing 6-DoF Grasp Detection via Domain Prior Knowledge](https://mlsanity.com/paper/2404.01727/) (2024)
- [TO-Scene: A Large-scale Dataset for Understanding 3D Tabletop Scenes](https://mlsanity.com/paper/2203.09440/) (2022)

## Abstract (arXiv)

Grasping skill is a major ability that a wide number of real-life applications require for robotisation. State-of-the-art robotic grasping methods perform prediction of object grasp locations based on deep neural networks. However, such networks require huge amount of labeled data for training making this approach often impracticable in robotics. In this paper, we propose a method to generate a large scale synthetic dataset with ground truth, which we refer to as the Jacquard grasping dataset. Jacquard is built on a subset of ShapeNet, a large CAD models dataset, and contains both RGB-D images and annotations of successful grasping positions based on grasp attempts performed in a simulated environment. We carried out experiments using an off-the-shelf CNN, with three different evaluation metrics, including real grasping robot trials. The results show that Jacquard enables much better generalization skills than a human labeled dataset thanks to its diversity of objects and grasping positions. For the purpose of reproducible research in robotics, we are releasing along with the Jacquard dataset a web interface for researchers to evaluate the successfulness of their grasping position detections using our dataset.