让四足机器人像人一样动态跳舞，靠预测与实时调整实现稳定表演
Dynamic Whole-Body Dancing with Humanoid Robots -- A Model-Based Control Approach
- 用动作捕捉+优化算法生成机器人可执行的舞蹈动作
- 预测越长，动作越流畅，执行越稳定
- 适合对机器人动态表演感兴趣的研究者和开发者
本文提出一种基于模型的集成框架，用于在类人机器人上生成并执行动态全身舞蹈动作。该框架分为离线动作生成与在线动作执行两个阶段，均利用未来状态预测，实现在真实环境中的鲁棒动态舞蹈。离线阶段通过动作捕捉系统采集人类舞蹈示范，经二次规划（QP）重定向至机器人，并通过轨迹优化（TO）确保动力学可行性；在线阶段采用基于质心动力学的模型预测控制（MPC）实时跟踪规划动作，并主动调整摆动脚位置以适应实际扰动。我们在全尺寸类人机器人Kuavo 4Pro上验证了该框架，展示了仿真与四台机器人协同完成的四分钟现场公开表演。实验结果表明，更长的预测时域能同时提升规划阶段的动作表现力与执行阶段的稳定性。
原文摘要 · Abstract (English)
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.
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