This paper presents an integrated model-based framework for generating and executing dynamic whole-body dance motions on humanoid robots. The framework operates in two stages: offline motion generation and online motion execution, both leveraging future state prediction to enable robust and dynamic dance motions in real-world environments. In the offline motion generation stage, human dance demonstrations are captured via a motion capture (MoCap) system, retargeted to the robot by solving a Quadratic Programming (QP) problem, and further refined using Trajectory Optimization (TO) to ensure dynamic feasibility. In the online motion execution stage, a centroidal dynamics-based Model Predictive Control (MPC) framework tracks the planned motions in real time and proactively adjusts swing foot placement to adapt to real world disturbances. We validate our framework on the full-size humanoid robot Kuavo 4Pro, demonstrating the dynamic dance motions both in simulation and in a four-minute live public performance with a team of four robots. Experimental results show that longer prediction horizons improve both motion expressiveness in planning and stability in execution.

翻译：本文提出了一种集成式基于模型的框架，用于在类人机器人上生成并执行动态全身舞蹈动作。该框架分为两个阶段：离线运动生成与在线运动执行，两者均利用未来状态预测以确保在真实环境中实现鲁棒且动态的舞蹈动作。在离线运动生成阶段，通过动作捕捉（MoCap）系统采集人类舞蹈演示数据，利用二次规划（QP）问题将其重定向至机器人，并进一步通过轨迹优化（TO）进行精炼以保证动态可行性。在线运动执行阶段，基于质心动力学的模型预测控制（MPC）框架实时追踪规划运动，并主动调整摆动脚落点以适应真实世界干扰。我们在全尺寸类人机器人Kuavo 4Pro上验证了该框架，通过仿真及四台机器人团队的四分钟现场公开表演展示了动态舞蹈动作。实验结果表明，更长的预测时域能同时提升规划中的动作表现力与执行稳定性。