Grasp Generation

GRAB is a dataset of full-body motions interacting and grasping 3D objects. It contains accurate finger and facial motions as well as the contact between the objects and body. It contains 5 male and 5 female participants and 4 different motion intents. The GRAB dataset also contains binary contact maps between the body and objects. Source: https://github.com/otaheri/GRAB Image Source: https://github.com/otaheri/GRAB

Robot grasping is often formulated as a learning problem. With the increasing speed and quality of physics simulations, generating large-scale grasping data sets that feed learning algorithms is becoming more and more popular. An often overlooked question is how to generate the grasps that make up these data sets. In this paper, we review, classify, and compare different grasp sampling strategies. Our evaluation is based on a fine-grained discretization of SE(3) and uses physics-based simulation to evaluate the quality and robustness of the corresponding parallel-jaw grasps. Specifically, we consider more than 1 billion grasps for each of the 21 objects from the YCB data set. This dense data set lets us evaluate existing sampling schemes w.r.t. their bias and efficiency. Our experiments show that some popular sampling schemes contain significant bias and do not cover all possible ways an object can be grasped.

https://syncandshare.lrz.de/getlink/fi9EZb33KiSAJ5rLHAkhg7/ffhnet-data.zip

Includes several sets of synthetic stereo images labelled with grasp rectangles representing parallel-jaw grasps (Cornell-like format). The set was introduced in the range of the ICAR paper "Automatic generation of realistic training data for learning parallel-jaw grasping from synthetic stereo images", please refer to it if you are using the data.