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Evaluating the efficacy of grasp metrics for utilization in a Gaussian Process-based grasp predictor
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
doi:10.1109/iros.2014.6943029
dblp:conf/iros/GoinsCWB14
fatcat:fy5gnrqcynfwxfr4quppqqoini