Cybernetic-Graphic Model for Robot Imitation Learning Based on Non-contact Observation

The cybernetic graphic model (CGM), a new model of behavioral representation and reproduction, based on non-contact observation for robot imitation learning is proposed. The human-robots relationship is built for imitating the behaviors from demonstration of human, and the pre-condition of imitation learning is analyzed to be that differential motions of end-effector of system are used as the behavioral primitives. Architecture of CGM and the learning method based on visual observation sequences are proposed. The segmenting method of sequences based on accumulating and instantaneous correlation function for generation of graphic structure of CGM and the learning method of behavioral primitive target based on RBF (radial basis function) networks are proposed. The brush drawing and object grasping experiments are performed with different types and degrees of freedom of robots. The results show that the proposed CGM based on visual observation can represent and reproduce different levels and types of behaviors, and is powerful in generalization, generality and utility of imitation learning.