Grasping of extrafoveal targets: A robotic model

Abstract

We present a computational model of grasping of non-fixated (extrafoveal) target objects which is implemented on a robot setup, consisting of a robot arm with cameras and gripper. This model is based on the premotor theory of attention ( Rizzolatti et al., 1994) which states that spatial attention is a consequence of the preparation of goal-directed, spatially coded movements (especially saccadic eye movements). In our model, we add the hypothesis that saccade planning is accompanied by the prediction of the retinal images after the saccade. The foveal region of these predicted images can be used to determine the orientation and shape of objects at the target location of the attention shift. This information is necessary for precise grasping. Our model consists of a saccade controller for target fixation, a visual forward model for the prediction of retinal images, and an arm controller which generates arm postures for grasping. We compare the precision of the robotic model in different task conditions, among them grasping (1) towards fixated target objects using the actual retinal images, (2) towards non-fixated target objects using visual prediction, and (3) towards non-fixated target objects without visual prediction. The first and second setting result in good grasping performance, while the third setting causes considerable errors of the gripper orientation, demonstrating that visual prediction might be an important component of eye–hand coordination. Finally, based on the present study we argue that the use of robots is a valuable research methodology within psychology.