Dynamic Whole-Body Dancing with Humanoid Robots -- A Model-Based Control Approach

2026-04-05

AI Summary

This study addresses the challenge of enabling humanoid robots to perform full-body dance motions that simultaneously exhibit dynamism, stability, and artistic expressiveness. The authors propose a model-based two-stage framework: in the offline phase, motion capture data retargeting combined with trajectory optimization yields dynamically feasible dance sequences; in the online phase, a centroidal dynamics-based model predictive control (MPC) scheme employs long-horizon prediction to adjust footstep placements in real time for robust disturbance rejection. The approach is validated through the first public demonstration of robust, synchronized, four-minute dynamic dancing performed by multiple full-sized Kuavo 4Pro humanoid robots, showcasing its effectiveness in dynamic execution, disturbance resilience, and artistic fidelity.

Abstract

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.

Problem

• humanoid robots
• dynamic whole-body dancing
• motion generation
• motion execution
• real-world stability

Innovation

• Model Predictive Control
• Trajectory Optimization
• Humanoid Robotics
• Whole-Body Motion
• Dynamic Dance Generation