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

A Billion Ways to Grasp: An Evaluation of Grasp Sampling Schemes on a Dense, Physics-based Grasp Data Set