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Modern approaches to grasp planning often involve deep learning. However,there are only a few large datasets of labelled grasping examples on physicalrobots, and available datasets involve relatively simple planar grasps withtwo-fingered grippers. Here we present: 1) a new human grasp demonstrationmethod that facilitates rapid collection of naturalistic grasp examples, withfull six-degree-of-freedom gripper positioning; and 2) a dataset of roughlyforty thousand successful grasps on 109 different rigid objects with theRightHand Robotics three-fingered ReFlex gripper.

A dataset of 40K naturalistic 6-degree-of-freedom robotic grasp demonstrations