Evaluating the efficacy of grasp metrics for utilization in a Gaussian Process-based grasp predictor

Abstract

With the goal of advancing the state of automatic robotic grasping, we present a novel approach that combines machine learning techniques and rigorous validation on a physical robotic platform in order to develop an algorithm that predicts the quality of a robotic grasp before execution. After collecting a large grasp sample set (522 grasps), we first conduct a thorough statistical analysis of the ability of grasp metrics that are commonly used in the robotics literature to discriminate between good and bad grasps. We then apply Principal Component Analysis and Gaussian Process algorithms on the discriminative grasp metrics to build a classifier that predicts grasp quality. The key findings are as follows: (i) several of the grasp metrics in the literature are weak predictors of grasp quality when implemented on a physical robotic platform; (ii) the Gaussian Process-based classifier significantly improves grasp prediction techniques by providing an absolute grasp quality prediction score from combining multiple grasp metrics. Specifically, the GP classifier showed a 66% percent improvement in the True Positive classification rate at a low False Positive rate of 5% when compared with classification based on thresholding of individual grasp metrics.