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This paper describes a redundant robot arm that is capable of learning to reach for targets in space while avoiding obstacles in a self-organized fashion. Self-generated movement commands that activate correlated visual, spatial and motor information are used to learn forward and inverse kinematic control models while moving in obstacle free space using the direction-to-rotation transform (DIRECT). The DIRECT based kinematic controller is fault tolerant and can handle a wide range of perturbations such as joint locking and the use of tools despite not experiencing them during learning. We have developed a DIRECT-based reactive obstacle avoidance controller (DIRECT-ROAC) that enables the redundant robot arm to avoid obstacles in environments with simple obstacle configurations. However, certain configurations of obstacles in the environment prevent the robot from reaching the target with purely reactive obstacle avoidance. To address this complexity, we model a self-organized process of mental rehearsals of movements inspired by human and animal experiments on reaching to generate plans for movement execution using DIRECT-ROAC in complex environments. These mental rehearsals or plans are self generated by utilizing perceptual information in the form of via-points extracted from attentional shrouds around obstacles in its environment. Computer simulations show that the proposed novel controller is successful in avoiding obstacles in environments with complex obstacle configurations.