What Can Be Learned from Human Reach-to-Grasp Movements for the Design of Robotic Hand-Eye Systems?

In the eld of visual control of robot motion, visual servoing has been proposed as the suitable control strategy to cope with imprecise models and calibration errors. Remaining problems such as the necessity of a high rate of visual feedback are deemed to be solvable by the development of real-time vision modules. However, human grasping, which still outshines its robotic counterparts especially with respect to robustness and exi-bility, deenitely requires only sparse, asynchronous visual feedback; hence, there might be something to be learned from the neuroscience for the design of robotic systems yet. We therefore examined current neurosci-entiic models for the control of human reach-to-grasp movements with the emphasis lying on the visual control strategy used. From this, we developped a generalized and extended control model that uniies the two robotic strategies look-then-move and visual servoing, thereby compensating the problems that each strategy shows when used alone.