Efficient human motor control is characterised by an extensive use of joint impedance modulation, which to a large extent is achieved by co-contracting antagonistic muscle pairs in a way that is beneficial to the specific task. Studies in single and multi joint limb reaching movements revealed that joint impedance is increased with faster movements [1] as well as with higher positional accuracy demands [2]. A large body of experimental work has investigated the motor learning processes in tasks with changing dynamics conditions (e.g., [3]) and it has been shown that subjects generously make use of impedance control to counteract destabilising external force fields (FF). In the early stage of dynamics learning humans tend to increase co-contraction. As learning progresses in consecutive reaching trials, a reduction in co-contraction with a parallel reduction of the reaching errors made can be observed. While there is much experimental evidence available for the use of impedance control in the CNS, no generally-valid computational model of impedance control derived from first principles have been proposed so far. Many of the proposed computational models have either focused on the biomechanical aspects of impedance control [4] or have proposed simple low level mechanisms to try to account for observed human co-activation patterns [3]. However these models are of a rather descriptive nature and do not provide us with a general and principled theory of impedance control in the nervous system.