Hybrid learning model and MMSVM classification for on-line visual imitation of a human with 3-D motions

In this paper, the on-line visual imitation of humanoid robot (HR) for human's 3-D motions is developed. At the beginning, the 3-D motional sequences of a human is captured by a stereo vision system (SVS), which skeleton algorithm can capture and estimate the 3-D coordinates of fifteen main joints. Since the dynamic balance of the HR is not considered, the proposed on-line visual imitation is divided into two parts, lower body (LB) and upper body (UB). Eleven stable motions of LB with the developed feature vector based on the 3-D coordinates of head, left and right feet are classified by the proposed modified multi-class support vector machine (MMSVM). To confirm the effectiveness of MMSVM, it is also compared with the SVM based on error correcting output codes (ECOC). The imitation of UB is based on the inverse kinematics (IK) of two pairs of (hand, elbow). To enhance one-to-one mapping and to reduce the modeling complexity of IK, two arms of UB are partitioned into eight subwork spaces, and each one is approximated by a pre-trained hybrid learning model. The comparisons between hybrid learning model based and ordinary IKs are also made. Combining the classified motion of LB with the operated IK motion of UB accomplishes the task of imitating the 3-D motions of a human. Finally, the corresponding experiments are presented to validate the effectiveness and practicality of the proposed method. On-line visual imitation of the 3-D motions of a human.Visual imitation is designed by the combined motions of upper body and lower body.The motions of lower body are classified as eleven stable motions.The motions of upper body are imitated by that of two pairs of (hand, elbow).The trained hybrid learning model are designed to reduce the complex computation, to improve robust accuracy, to obtain more complex imitation.