DanceHAT: Generate Stable Dances for Humanoid Robots with Adversarial Training

Music to dance for humanoid robots is an interesting task. Robot dance generation is challenging when considering music pieces, human dancer motions, and robot stability simultaneously. Previous methods rely on human-designed motion library or stability constraints for robot postures. Hence, dance generation for humanoid robots requires expert design, which can be time-consuming across different humanoid platforms. In this work, we propose a novel method called DanceHAT, which generates stable humanoid dances by imitating human dancers with self-learning. DanceHAT is an adversarial training framework, which incorporates similarity loss and stability loss simultaneously. Furthermore, DanceHAT does not require human-designed features or robot model information. Experiments in the simulation environment and on the real robot demonstrate that our model can generate stable, diverse, and human-like dances for humanoid robots automatically. In addition, DanceHAT is a general training approach for robot imitation tasks with stability constraints, thus can be utilized in other humanoid tasks and will be researched in future works.