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Using Crowdsourcing to Generate Surrogate Training Data for Robotic Grasp Prediction


 
 As an alternative to the laborious process of collecting training data from physical robotic platforms for learning robotic grasp quality prediction, we explore the use of surrogate training data from crowd-sourced evaluations of images of robotic grasps. We show that in certain regions of the grasp feature space, grasp predictors trained with this surrogate data were almost as accurate as predictors built using data from physical testing with robots.
 
