Learning Humanoid Standing-up Control across Diverse Postures
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arXiv
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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866917991611891712 |
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| author | Huang, Tao Ren, Junli Wang, Huayi Wang, Zirui Ben, Qingwei Wen, Muning Chen, Xiao Li, Jianan Pang, Jiangmiao |
| author_facet | Huang, Tao Ren, Junli Wang, Huayi Wang, Zirui Ben, Qingwei Wen, Muning Chen, Xiao Li, Jianan Pang, Jiangmiao |
| contents | 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 real-world 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. Videos and code are available at https://taohuang13.github.io/humanoid-standingup.github.io/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_08378 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning Humanoid Standing-up Control across Diverse Postures Huang, Tao Ren, Junli Wang, Huayi Wang, Zirui Ben, Qingwei Wen, Muning Chen, Xiao Li, Jianan Pang, Jiangmiao Robotics Artificial Intelligence Machine Learning 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 real-world 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. Videos and code are available at https://taohuang13.github.io/humanoid-standingup.github.io/. |
| title | Learning Humanoid Standing-up Control across Diverse Postures |
| topic | Robotics Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2502.08378 |