Opening the Sim-to-Real Door for Humanoid Pixel-to-Action Policy Transfer
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arXiv
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| Autores principales: | , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
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| _version_ | 1866912739971039232 |
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| author | Xue, Haoru He, Tairan Wang, Zi Ben, Qingwei Xiao, Wenli Luo, Zhengyi Da, Xingye Castañeda, Fernando Shi, Guanya Sastry, Shankar Fan, Linxi "Jim" Zhu, Yuke |
| author_facet | Xue, Haoru He, Tairan Wang, Zi Ben, Qingwei Xiao, Wenli Luo, Zhengyi Da, Xingye Castañeda, Fernando Shi, Guanya Sastry, Shankar Fan, Linxi "Jim" Zhu, Yuke |
| contents | Recent progress in GPU-accelerated, photorealistic simulation has opened a scalable data-generation path for robot learning, where massive physics and visual randomization allow policies to generalize beyond curated environments. Building on these advances, we develop a teacher-student-bootstrap learning framework for vision-based humanoid loco-manipulation, using articulated-object interaction as a representative high-difficulty benchmark. Our approach introduces a staged-reset exploration strategy that stabilizes long-horizon privileged-policy training, and a GRPO-based fine-tuning procedure that mitigates partial observability and improves closed-loop consistency in sim-to-real RL. Trained entirely on simulation data, the resulting policy achieves robust zero-shot performance across diverse door types and outperforms human teleoperators by up to 31.7% in task completion time under the same whole-body control stack. This represents the first humanoid sim-to-real policy capable of diverse articulated loco-manipulation using pure RGB perception. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_01061 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Opening the Sim-to-Real Door for Humanoid Pixel-to-Action Policy Transfer Xue, Haoru He, Tairan Wang, Zi Ben, Qingwei Xiao, Wenli Luo, Zhengyi Da, Xingye Castañeda, Fernando Shi, Guanya Sastry, Shankar Fan, Linxi "Jim" Zhu, Yuke Robotics Computer Vision and Pattern Recognition Recent progress in GPU-accelerated, photorealistic simulation has opened a scalable data-generation path for robot learning, where massive physics and visual randomization allow policies to generalize beyond curated environments. Building on these advances, we develop a teacher-student-bootstrap learning framework for vision-based humanoid loco-manipulation, using articulated-object interaction as a representative high-difficulty benchmark. Our approach introduces a staged-reset exploration strategy that stabilizes long-horizon privileged-policy training, and a GRPO-based fine-tuning procedure that mitigates partial observability and improves closed-loop consistency in sim-to-real RL. Trained entirely on simulation data, the resulting policy achieves robust zero-shot performance across diverse door types and outperforms human teleoperators by up to 31.7% in task completion time under the same whole-body control stack. This represents the first humanoid sim-to-real policy capable of diverse articulated loco-manipulation using pure RGB perception. |
| title | Opening the Sim-to-Real Door for Humanoid Pixel-to-Action Policy Transfer |
| topic | Robotics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.01061 |