## 1 Introduction
In [26], the author presented a quantitative metric of the similarity between a human pose and a robot pose based on the latent spaces of the human and the robot; the poses were represented by joint angles, which also provide an indirect representation of posture. With regard to metrics that are not specifically aimed at imitation systems, [PERSON] et al. evaluated the human-like behavior of the Nadine robot by calculating the Pearson product-moment correlation coefficients of key points captured by a depth camera between the humanoid robot and the human demonstrator [47]. [PERSON] et al. constructed a quantitative evaluation method for pose and motion similarity in accordance with human perception [48]. The weights of the body regions were optimized to ensure the highest correlation coefficients between the quantitative similarities and average human intuition. The quantitative evaluation was based on the positions and angles of the body regions, which do not directly represent the posture and do not consider the different effects of different joints on the posture. To improve the evaluation of imitation similarity, two quantitative vector-based similarity evaluation criteria are proposed in this paper. Overall, the constraints of the previous research on robotic imitation are as follows:  
* Most imitation systems that can achieve stable whole-body motions are based on numerical IK methods for posture mapping. However, joint angles generated using numerical methods are not as accurate as those obtained using analytical methods and incur higher computation costs. * Most analytical methods of joint angle mapping used in imitation systems are simple and rough, and they cannot indicate the orientations of human body links and lack sufficient consideration of the robot's joint structures. This will affect the accuracy of the joint angles for mapping and limit the imitation similarity. * Many approaches cannot achieve stable doubly supported and singly supported imitation. * Most researchers have not considered the imitation of head motions, hand motions or locomotion, which are essential for task execution using an imitation system. Some systems partially accomplish these functions but require additional postures or audio instructions or require ancillary handheld or wearable devices, which are inconvenient. * Most similarity criteria are based on the positions of key points, i.e., the positions of end effectors or skeleton points, or are based on joint angles and consequently cannot directly reflect the posture similarity. To solve the problems identified above and improve the performance of real-time imitation systems, a novel stable, complete and unrestricted whole-body imitation system is proposed and developed in this paper. In addition, vector-set-based and multi-focused methods of imitation evaluation are proposed and employed to evaluate our real-time imitation system. The main contributions of this paper can be summarized as follows:* A novel comprehensive and unrestricted whole-body imitation system is proposed and developed. In addition, an imitation learning algorithm is presented and developed based on it. To the best of our knowledge, it is the most complete and free whole-body imitation system developed to date, enabling the imitation of head motions, arm motions, lower-limb motions, hand motions and locomotion and not requiring any ancillary handheld or wearable devices or any additional audio or gesture-based instructions. The system includes a double-support mode, a single-support mode and a walking mode. The balance is controlled in each mode, and the stability of the system enables the robot to execute some complicated tasks in real time. * A novel analytical method called GA-LVVJ is proposed to map the human motions to a robot based on the observed human data. Link vectors are constructed according to the captured skeleton points, and the virtual joints are set according to the link vectors and the robot joints.