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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.14308 |
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| _version_ | 1866915864305991680 |
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| author | Fu, Lequn Zhong, Yijun Li, Xiao Liu, Yibin Xu, Zhiyuan Tang, Jian Li, Shiqi |
| author_facet | Fu, Lequn Zhong, Yijun Li, Xiao Liu, Yibin Xu, Zhiyuan Tang, Jian Li, Shiqi |
| contents | Humanoid robots deployed in industrial environments are required to perform load-carrying transportation tasks that tightly couple locomotion and manipulation. However, achieving stable and robust locomotion under varying payloads and upper-body motions is challenging due to dynamic coupling and partial observability. This paper presents a load-aware locomotion framework for industrial humanoids based on a decoupled yet coordinated loco-manipulation architecture. Lower-body locomotion is controlled via a reinforcement learning policy producing residual joint actions on kinematically derived nominal configurations. A kinematics-based locomotion reference with a height-conditioned joint-space offset guides learning, while a history-based state estimator infers base linear velocity and height and encodes residual load- and manipulation-induced disturbances in a compact latent representation. The framework is trained entirely in simulation and deployed on a full-size humanoid robot without fine-tuning. Simulation and real-world experiments demonstrate faster training, accurate height tracking, and stable loco-manipulation. Project page: https://lequn-f.github.io/LALO/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_14308 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Load-Aware Locomotion Control for Humanoid Robots in Industrial Transportation Tasks Fu, Lequn Zhong, Yijun Li, Xiao Liu, Yibin Xu, Zhiyuan Tang, Jian Li, Shiqi Robotics Humanoid robots deployed in industrial environments are required to perform load-carrying transportation tasks that tightly couple locomotion and manipulation. However, achieving stable and robust locomotion under varying payloads and upper-body motions is challenging due to dynamic coupling and partial observability. This paper presents a load-aware locomotion framework for industrial humanoids based on a decoupled yet coordinated loco-manipulation architecture. Lower-body locomotion is controlled via a reinforcement learning policy producing residual joint actions on kinematically derived nominal configurations. A kinematics-based locomotion reference with a height-conditioned joint-space offset guides learning, while a history-based state estimator infers base linear velocity and height and encodes residual load- and manipulation-induced disturbances in a compact latent representation. The framework is trained entirely in simulation and deployed on a full-size humanoid robot without fine-tuning. Simulation and real-world experiments demonstrate faster training, accurate height tracking, and stable loco-manipulation. Project page: https://lequn-f.github.io/LALO/ |
| title | Load-Aware Locomotion Control for Humanoid Robots in Industrial Transportation Tasks |
| topic | Robotics |
| url | https://arxiv.org/abs/2603.14308 |