This paper presents an efficient method of learning motion control for autonomous animated characters. The method uses a non parametric learning approach which identifies non linear mappings between sensory signals and motor control. The learning phase is handled through a General Regression Neural Network model simulated by using near neighbors search algorithms (kd-tree). The resulting adaptive model (ASMM) is suitable for the expressive animation of an anthropomorphic hand-arm system involved in reaching or tracking tasks.