Simple linear readouts from generic neural microcircuit models consisting of spiking neurons and dynamic synapses can be trained to generate and control basic movements, for example, reaching with an arm to various target points. After suitable training of these readouts on a small number of target points; reaching movements to other target points can also be generated. Sensory or proprioceptive feedback turns out to be essential for such movement control, even if it is noisy and substantially delayed. Such feedback turns out to optimally improve the performance of the neural microcircuit model if it arrives with a biologically realistic delay of 100 to 200 ms. Furthermore, additional feedbacks of ``prediction of sensory variables'' are shown to improve the performance significantly. The proposed model also provides a new approach for movement control in robotics. Existing control methods in robotics that take the particular dynamics of the sensors and actuators into account (``embodiment of robot control'') are taken one step further by this approach, which provides methods for also using the ``embodiment of computation'', i.e. the inherent dynamics and spatial structure of neural circuits, for the design of robot movement controllers.