13 datasets found

A dataset of whole-body human grasping of objects, containing 3D annotations of hand pose, object pose, and contact area.

The dataset used in this paper is a robot grasping dataset, where the robot learns to grasp objects in a simulated environment.

A benchmark for vision-based grasping.

A database of grasp synthesis algorithms.

A large-scale experimental study that increases the amount of data for learning to grasp, providing complete labeling in terms of whether an object can be grasped at a...

A dataset of successful, cylindrical precision robotic grasps using the V-REP simulator and object files provided by Kleinhans et al. on a simulated "picking" task.

A dataset of grasps with object contact and hand pose, containing 3D annotations of hand pose, object pose, and contact area.

The FPHA dataset is a real-world dataset for studying hand-object interaction.

The HO-3D and FPHA datasets are real-world datasets for studying hand-object interaction.

The Obman dataset is a synthetic dataset containing hand-object mesh pairs. The hands are generated by a non-learning based method GraspIt! and are parameterized by the MANO model.

The dataset used for training a Convolutional Neural Network to predict a grasp quality score over all grasp poses, given a depth image of an object.

PhyPartNet is a large-scale 3D-linguistic dataset for robotic grasping, featuring 195K object instances with varying physical properties and human preferences.

The DexYCB dataset is a large-scale dataset of hand-grasping postures captured using a synchronized setup of 8 cameras.