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

Alex K. Goins, Ryan Carpenter, Weng-Keen Wong, Ravi Balasubramanian
2014 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems  
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
more » ... 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.
doi:10.1109/iros.2014.6943029 dblp:conf/iros/GoinsCWB14 fatcat:fy5gnrqcynfwxfr4quppqqoini