Evaluating The Quality Of Non-Prehensile Balancing Grasps
2018 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA)(2018)
Abstract
Assessing grasp quality and, subsequently, predicting grasp success is useful for avoiding failures in many autonomous robotic applications. In addition, interest in non-prehensile grasping and manipulation has been growing as it offers the potential for a large increase in dexterity. However, while force-closure grasping has been the subject of intense study for many years, few existing works have considered quality metrics for non-prehensile grasps. Furthermore, no studies exist to validate them in practice. In this work we use a real-world data set of non-prehensile balancing grasps and use it to experimentally validate a wrench-based quality metric by means of its grasp success prediction capability. The overall accuracy of up to 84% is encouraging and in line with existing results for force-closure grasps.
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Key words
nonprehensile balancing grasps,wrench-based quality metric,force-closure grasps,autonomous robotic applications,manipulation,prediction capability,dexterity
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