HAFO: A Force-Adaptive Control Framework for Humanoid Robots in Intense Interaction Environments
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866908798889754624 |
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| author | Dong, Chenhui Xu, Haozhe Feng, Wenhao Wang, Zhipeng Zhou, Yanmin Zhao, Yifei He, Bin |
| author_facet | Dong, Chenhui Xu, Haozhe Feng, Wenhao Wang, Zhipeng Zhou, Yanmin Zhao, Yifei He, Bin |
| contents | Reinforcement learning (RL) controllers have made impressive progress in humanoid locomotion and light-weight object manipulation. However, achieving robust and precise motion control with intense force interaction remains a significant challenge. To address these limitations, this paper proposes HAFO, a dual-agent reinforcement learning framework that concurrently optimizes both a robust locomotion strategy and a precise upper-body manipulation strategy via coupled training. We employ a constrained residual action space to improve dual-agent training stability and sample efficiency. The external tension disturbances are explicitly modeled using a spring-damper system, allowing for fine-grained force control through manipulation of the virtual spring. In this process, the reinforcement learning policy autonomously generates a disturbance-rejection response by utilizing environmental feedback. The experimental results demonstrate that HAFO achieves whole-body control for humanoid robots across diverse force-interaction environments using a single dual-agent policy, delivering outstanding performance under load-bearing and thrust-disturbance conditions, while maintaining stable operation even under rope suspension state. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_20275 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | HAFO: A Force-Adaptive Control Framework for Humanoid Robots in Intense Interaction Environments Dong, Chenhui Xu, Haozhe Feng, Wenhao Wang, Zhipeng Zhou, Yanmin Zhao, Yifei He, Bin Robotics Reinforcement learning (RL) controllers have made impressive progress in humanoid locomotion and light-weight object manipulation. However, achieving robust and precise motion control with intense force interaction remains a significant challenge. To address these limitations, this paper proposes HAFO, a dual-agent reinforcement learning framework that concurrently optimizes both a robust locomotion strategy and a precise upper-body manipulation strategy via coupled training. We employ a constrained residual action space to improve dual-agent training stability and sample efficiency. The external tension disturbances are explicitly modeled using a spring-damper system, allowing for fine-grained force control through manipulation of the virtual spring. In this process, the reinforcement learning policy autonomously generates a disturbance-rejection response by utilizing environmental feedback. The experimental results demonstrate that HAFO achieves whole-body control for humanoid robots across diverse force-interaction environments using a single dual-agent policy, delivering outstanding performance under load-bearing and thrust-disturbance conditions, while maintaining stable operation even under rope suspension state. |
| title | HAFO: A Force-Adaptive Control Framework for Humanoid Robots in Intense Interaction Environments |
| topic | Robotics |
| url | https://arxiv.org/abs/2511.20275 |