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I n the field of robot motion control, visual servoing has been proposed as the suitable 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 b y the development of real-time vision modules. However, human grasping, which still outshines its robotic counterparts especially with respect to robustness and flexibility, definitely requires only sparse, asynchronous visual feedback. W e therefore examined current neuroscientific models for the control of human reach-to-grasp movements with the emphasis lying on the visual control strategy used. From this, we developed a control model that unifies the two robotic strategies look-then-move and visual servoing, thereby compensating the problems that each strategy shows when used alone.

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