A Keyframe-driven Whole-body Motion Control Framework for Humanoid Robots

Learning-based algorithms remain a challenge in the field of humanoid robot motion control. Due to the diverse range of humanoid robot motions and the complexity of control, existing motion control methods often require the development of distinct control frameworks or using different network architectures. This paper proposes a keyframe-driven whole-body motion control framework for humanoid robots that allows the robot to perform a variety of motions through manually designed actions or video demonstrations. We introduce a humanoid robot motion classification method to enable the robot to learn various types of action. Moreover, we design a Dual Actor Single Critic Network(DASCN) network for whole-body control of the humanoid robot. Experimental results demonstrate that our method enables the humanoid robot H1 to execute a range of movements based solely on pre-designed motions or demonstrated videos.