A key issue in motor control is to understand how the motor system chooses a solution from the multiple solutions that exist to achieve any particular task goal. One hypothesis is that redundancy may be resolved by minimizing movement-related costs. However, testing this prediction in motor learning has been problematic in simple laboratory tasks, like reaching, because the motor system already has extensive prior knowledge about redundancy in these tasks. Here, we used a novel task where healthy human participants performed finger movements to guide a computer cursor to different targets on the screen. Through training, all participants learned to perform successful goal-directed movements. Our findings showed that subjects did not develop a single inverse map from target to hand posture. Instead, they learned to use distinct hand postures to get to a single target, using a strategy in which the final hand posture at the target depended on the starting hand posture. Furthermore, postures chosen also depended upon the information content of visual feedback, with precise visual feedback resulting in postures that minimized movement-related costs. These results reinforce the idea that redundancy is exploited to minimize movement-related costs and that feedback plays a critical role in modulating this ability to effectively take advantage of the abundance of degrees of freedom.