RobotDancing: Residual-Action Reinforcement Learning Enables Robust Long-Horizon Humanoid Motion Tracking

Abstract-- Long-horizon, high-dynamic motion tracking on humanoids remains brittle because absolute joint commands cannot compensate model-plant mismatch, leading to error accumulation. We propose RobotDancing, a simple, scalable framework that predicts residual joint targets to explicitly correct dynamics discrepancies. The pipeline is end-to-end--training, sim-to-sim validation, and zero-shot sim-to-real--and uses a single-stage reinforcement learning (RL) setup with a unified observation, reward, and hyperparameter configuration. RobotDancing can track multi-minute, high-energy behaviors (jumps, spins, cartwheels) and deploys zero-shot to hardware with high motion tracking quality. I. INTRODUCTION Humanoid robots are increasingly expected to execute long-horizon, highly dynamic behaviors such as dance, where small tracking errors compound rapidly and destabilize control. A principal source of such drift is the mismatch between idealized reference trajectories and the robot's true physics (actuation limits, friction, inertia, latency).