Abstract: -At the light of control and learning theories, this paper addresses the question of controlling multi-joint system using sensory feedback. A generic Sensory-Motor Control Model (SMCM) is firstly presented that solves the inverse kinematics difficulty at a theoretical level. Computational implementations of SMCM requires the knowledge of sensory motor transforms that are directly dependent to the multi-joint structure that is to be controlled. To avoid the dependency of SMCM to the analytical knowledge of these transforms, a non parametric learning approach is developed to identify non linear mappings between sensory signals and motor commands involved in SMCM. The resulting adaptive SMCM (ASMCM) is intensively tested within the scope of hand-arm reaching movements. ASMCM shows to be very effective and robust at least for this task. Its generic properties and effectiveness allow to foresee wide area of application
