EGAD - Evolved Grasping Analysis Dataset

The Evolved Grasping Analysis Dataset (EGAD), a collection of generated shapes for training and benchmarking robotic grasping and manipulatoin tasks. 

Diverse and extensive training data are critical to training modern robotic systems to grasp, and yet many systems are trained on small or non-diverse datasets repurposed from other domains. We used evoluationary algorithms to create a set of objects which uniformly span the object space of simple to complex, and easy to difficult to grasp, with a focus on geometric diversity. The objects are all easily 3D-printable, making 1:1 sim-to-real transfer possible.

Full details, code and videos can be found on the project website: 

A preprint version of the paper can be found at:

进化抓取分析数据集（Evolved Grasping Analysis Dataset，EGAD）是一类用于机器人抓取与操作任务训练及性能基准测试的生成形状集合。

多样化且大规模的训练数据是训练现代机器人抓取系统的核心要素，但当前多数系统均使用从其他领域复用而来的小规模或多样性不足的数据集进行训练。

本研究采用进化算法生成了一组物体，其均匀覆盖了从简单到复杂、易抓取到难抓取的物体空间，核心聚焦于几何多样性。所有物体均可轻松实现3D打印，从而支持1:1的仿真到现实迁移。

完整的细节信息、代码及演示视频可在项目官网获取：

该论文的预印本版本可在以下地址获取：