# EGAD! an Evolved Grasping Analysis Dataset for diversity and reproducibility in robotic manipulation

arXiv: https://arxiv.org/abs/2003.01314 · HTML: https://mlsanity.com/paper/2003.01314/

## Key facts

- Cited by 61 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Mar 2020.

## Built on by (most cited first, 30 of 61)

- [Objaverse: A Universe of Annotated 3D Objects](https://mlsanity.com/paper/2212.08051/) (2022)
- [Objaverse-XL: A Universe of 10M+ 3D Objects](https://mlsanity.com/paper/2307.05663/) (2023)
- [ManiSkill2: A Unified Benchmark for Generalizable Manipulation Skills](https://mlsanity.com/paper/2302.04659/) (2023)
- [A System for General In-Hand Object Re-Orientation](https://mlsanity.com/paper/2111.03043/) (2021)
- [HomeRobot: Open-Vocabulary Mobile Manipulation](https://mlsanity.com/paper/2306.11565/) (2023)
- [General In-Hand Object Rotation with Vision and Touch](https://mlsanity.com/paper/2309.09979/) (2023)
- [Advances in 3D Generation: A Survey](https://mlsanity.com/paper/2401.17807/) (2024)
- [DexDiffuser: Generating Dexterous Grasps with Diffusion Models](https://mlsanity.com/paper/2402.02989/) (2024)
- [A Survey of Embodied Learning for Object-Centric Robotic Manipulation](https://mlsanity.com/paper/2408.11537/) (2024)
- [Grasp-Anything: Large-scale Grasp Dataset from Foundation Models](https://mlsanity.com/paper/2309.09818/) (2023)
- [GraspGen: A Diffusion-based Framework for 6-DOF Grasping with On-Generator Training](https://mlsanity.com/paper/2507.13097/) (2025)
- [pyribs: A Bare-Bones Python Library for Quality Diversity Optimization](https://mlsanity.com/paper/2303.00191/) (2023)
- [DDGC: Generative Deep Dexterous Grasping in Clutter](https://mlsanity.com/paper/2103.04783/) (2021)
- [Language-driven Grasp Detection](https://mlsanity.com/paper/2406.09489/) (2024)
- [ShapeSplat: A Large-scale Dataset of Gaussian Splats and Their Self-Supervised Pretraining](https://mlsanity.com/paper/2408.10906/) (2024)
- [Proximal Policy Gradient Arborescence for Quality Diversity Reinforcement Learning](https://mlsanity.com/paper/2305.13795/) (2023)
- [Orienting Novel 3D Objects Using Self-Supervised Learning of Rotation Transforms](https://mlsanity.com/paper/2105.14246/) (2021)
- [Distributionally Robust Policy Learning via Adversarial Environment Generation](https://mlsanity.com/paper/2107.06353/) (2021)
- [LaGeM: A Large Geometry Model for 3D Representation Learning and Diffusion](https://mlsanity.com/paper/2410.01295/) (2024)
- [AnyDexGrasp: General Dexterous Grasping for Different Hands with Human-level Learning Efficiency](https://mlsanity.com/paper/2502.16420/) (2025)
- [Rapid Motor Adaptation for Robotic Manipulator Arms](https://mlsanity.com/paper/2312.04670/) (2023)
- [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)
- [DA2 Dataset: Toward Dexterity-Aware Dual-Arm Grasping](https://mlsanity.com/paper/2208.00408/) (2022)
- [ArrayBot: Reinforcement Learning for Generalizable Distributed Manipulation through Touch](https://mlsanity.com/paper/2306.16857/) (2023)
- [Generalizing 6-DoF Grasp Detection via Domain Prior Knowledge](https://mlsanity.com/paper/2404.01727/) (2024)
- [Training Diverse High-Dimensional Controllers by Scaling Covariance Matrix Adaptation MAP-Annealing](https://mlsanity.com/paper/2210.02622/) (2022)
- [Recent Advances of Deep Robotic Affordance Learning: A Reinforcement Learning Perspective](https://mlsanity.com/paper/2303.05344/) (2023)
- [The Quality-Diversity Transformer: Generating Behavior-Conditioned Trajectories with Decision Transformers](https://mlsanity.com/paper/2303.16207/) (2023)
- [Reproducibility of Machine Learning: Terminology, Recommendations and Open Issues](https://mlsanity.com/paper/2302.12691/) (2023)

## Abstract (arXiv)

We present the Evolved Grasping Analysis Dataset (EGAD), comprising over 2000 generated objects aimed at training and evaluating robotic visual grasp detection algorithms. The objects in EGAD are geometrically diverse, filling a space ranging from simple to complex shapes and from easy to difficult to grasp, compared to other datasets for robotic grasping, which may be limited in size or contain only a small number of object classes. Additionally, we specify a set of 49 diverse 3D-printable evaluation objects to encourage reproducible testing of robotic grasping systems across a range of complexity and difficulty. The dataset, code and videos can be found at https://dougsm.github.io/egad/