Grasp Quality Evaluation with Whole Arm Kinematic Noise Propagation

Abstract

In this paper we propose a new approach to evaluate grasps that accounts for both the kinematic structure of the robot and the noise at its joints. Our starting observation is that with a redundant robot the same grasp can be implemented with different arm configurations, and these may display significant differences in terms of robustness to disturbances. Consequently, the grasp quality metric is seen as a random variable depending on the arm configuration. Starting from a first order approximation for the error, we introduce the high probability force closure region as a tool to evaluate the local robustness of an arm configuration, and we then introduce a new metric Qarm to rank different configurations according to the robustness to noise. By combining this method in an offline/online framework, we demonstrate through large scale simulations that this approach successfully captures aspects that were neglected in former literature regarding grasp evaluation, and can successfully be integrated into future grasp planners.