Autonomously developing organisms face several challenges when learning reaching movements. First, motor control is learned unsupervised or self-supervised. Second, knowledge of sensorimotor contingencies is acquired in contexts in which action consequences unfold in time. Third, motor redundancies must be resolved. To solve all 3 of these problems, the authors propose a sensorimotor, unsupervised, redundancy-resolving control architecture (SURE_REACH), based on the ideomotor principle. Given a 3-degrees-of-freedom arm in a 2-dimensional environment, SURE_REACH encodes 2 spatial arm representations with neural population codes: a hand end-point coordinate space and an angular arm posture space. A posture memory solves the inverse kinematics problem by associating hand end-point neurons with neurons in posture space. An inverse sensorimotor model associates posture neurons with each other action-dependently. Together, population encoding, redundant posture memory, and the inverse sensorimotor model enable SURE_REACH to learn and represent sensorimotor grounded distance measures and to use dynamic programming to reach goals efficiently. The architecture not only solves the redundancy problem but also increases goal reaching flexibility, accounting for additional task constraints or realizing obstacle avoidance. While the spatial population codes resemble neurophysiological structures, the simulations confirm the flexibility and plausibility of the model by mimicking previously published data in arm-reaching tasks.