Human reach-to-grasp generalization strategies: a Bayesian approach

Abstract

In this work we present a general structure of an initial Bayesian framework to describe the mechanisms underlying the human strategies that define the appropriate characteristics of the reach-to-grasp movements to specific contexts, objects and how these strategies can be extended and replicated to other contexts and objects. The Bayesian framework uses information extracted from data about the pose of the hand, fingers and head acquired by a magnetic tracker device, finger flexure data acquired by a data glove, as well as, data about eye gaze and saccade movements of the subject.