Robotics: Science and Systems XXI
Learning Humanoid Standing-up Control across Diverse Postures
Tao Huang, Junli Ren, Huayi Wang, Zirui Wang, Qingwei Ben, Muning Wen, Xiao Chen, Jianan Li, Jiangmiao PangAbstract:
Standing-up control is crucial for humanoid robots, with the potential for integration into current locomotion and loco-manipulation systems, such as fall recovery. Existing approaches are either limited to simulations that overlook hardware constraints or rely on predefined ground-specific motion trajectories, failing to enable standing up across postures in realworld scenes. To bridge this gap, we present HoST (Humanoid Standing-up Control), a reinforcement learning framework that learns standing-up control from scratch, enabling robust sim-to-real transfer across diverse postures. HoST effectively learns posture-adaptive motions by leveraging a multi-critic architecture and curriculum-based training on diverse simulated terrains. To ensure successful real-world deployment, we constrain the motion with smoothness regularization and implicit motion speed bound to alleviate oscillatory and violent motions on physical hardware, respectively. After simulation-based training, the learned control policies are directly deployed on the Unitree G1 humanoid robot. Our experimental results demonstrate that the controllers achieve smooth, stable, and robust standing-up motions across a wide range of laboratory and outdoor environments (Fig. 1). Videos and code are available on [our project page](https://taohuang13.github.io/humanoid-standingup.github.io/).
Bibtex:
@INPROCEEDINGS{HuangT-RSS-25, AUTHOR = {Tao Huang AND Junli Ren AND Huayi Wang AND Zirui Wang AND Qingwei Ben AND Muning Wen AND Xiao Chen AND Jianan Li AND Jiangmiao Pang}, TITLE = {{Learning Humanoid Standing-up Control across Diverse Postures}}, BOOKTITLE = {Proceedings of Robotics: Science and Systems}, YEAR = {2025}, ADDRESS = {LosAngeles, CA, USA}, MONTH = {June}, DOI = {10.15607/RSS.2025.XXI.064} }