Parametric Primitives for Motor Representation and Control

The use of motor primitives for the generation of complex movements is a relatively new and interesting idea for dimensionality reduction in robot control. We propose a framework in which adaptive primitives learn and represent synergetic arm movements. A simple and fixed set of postural and oscillatory primitives form the substrate through which all control is elicited. Higher level adaptive primitives interact and control the primitive substrate in order to handle complex movement sequences. We implemented this model on a simulated 20 DOF humanoid character with dynamics. We present results of the experiments involving the presentation and learning of synergetic arm movements.