RobotDancing: Residual-Action Reinforcement Learning Enables Robust Long-Horizon Humanoid Motion Tracking
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912604156329984 |
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| author | Sun, Zhenguo Peng, Yibo Meng, Yuan Li, Xukun Huang, Bo-Sheng Bing, Zhenshan Wang, Xinlong Knoll, Alois |
| author_facet | Sun, Zhenguo Peng, Yibo Meng, Yuan Li, Xukun Huang, Bo-Sheng Bing, Zhenshan Wang, Xinlong Knoll, Alois |
| contents | 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. We evaluate primarily on Unitree G1 with retargeted LAFAN1 dance sequences and validate transfer on H1/H1-2. RobotDancing can track multi-minute, high-energy behaviors (jumps, spins, cartwheels) and deploys zero-shot to hardware with high motion tracking quality. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_20717 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | RobotDancing: Residual-Action Reinforcement Learning Enables Robust Long-Horizon Humanoid Motion Tracking Sun, Zhenguo Peng, Yibo Meng, Yuan Li, Xukun Huang, Bo-Sheng Bing, Zhenshan Wang, Xinlong Knoll, Alois Robotics Artificial Intelligence 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. We evaluate primarily on Unitree G1 with retargeted LAFAN1 dance sequences and validate transfer on H1/H1-2. RobotDancing can track multi-minute, high-energy behaviors (jumps, spins, cartwheels) and deploys zero-shot to hardware with high motion tracking quality. |
| title | RobotDancing: Residual-Action Reinforcement Learning Enables Robust Long-Horizon Humanoid Motion Tracking |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2509.20717 |