Developing a Framework for Natural Human Movement Mimicry of Low-Dynamic Motions in Mobile-Based Humanoids

In this work, we propose a framework that facil- itates natural whole-body movements for a mobile-based hu- manoid. The framework takes bipedal human motion animation as input. After re-targeting the motion to the robot rig, the joint space data is applied to a physics-enabled robot model for balance confirmation and then deployed one-shot to the real robot. This method is beneficial for: 1) mapping expressive and low-dynamic whole-body motions, such as walking, to mobile-based robots; and 2) serving as a basis for training more complex control policies for more dynamic motions. Experiments were conducted by deploying human walking animations to the robot, assessing its ability to mirror the movements, and evaluating the subjective feelings of humans observing the robot performing the motions generated by both the proposed method and the traditional method. The results indicated that the proposed method is effective for mimicking human movements and consistently delivered a better overall impression in the natural appearance of the motion, the human- like factor, and friendliness.