HuBE: Cross-Embodiment Human-like Behavior Execution for Humanoid Robots
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912555455217664 |
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| author | Lyu, Shipeng Wang, Fangyuan Lin, Weiwei Zhu, Luhao Navarro-Alarcon, David Guo, Guodong |
| author_facet | Lyu, Shipeng Wang, Fangyuan Lin, Weiwei Zhu, Luhao Navarro-Alarcon, David Guo, Guodong |
| contents | Achieving both behavioral similarity and appropriateness in human-like motion generation for humanoid robot remains an open challenge, further compounded by the lack of cross-embodiment adaptability. To address this problem, we propose HuBE, a bi-level closed-loop framework that integrates robot state, goal poses, and contextual situations to generate human-like behaviors, ensuring both behavioral similarity and appropriateness, and eliminating structural mismatches between motion generation and execution. To support this framework, we construct HPose, a context-enriched dataset featuring fine-grained situational annotations. Furthermore, we introduce a bone scaling-based data augmentation strategy that ensures millimeter-level compatibility across heterogeneous humanoid robots. Comprehensive evaluations on multiple commercial platforms demonstrate that HuBE significantly improves motion similarity, behavioral appropriateness, and computational efficiency over state-of-the-art baselines, establishing a solid foundation for transferable and human-like behavior execution across diverse humanoid robots. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2508_19002 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | HuBE: Cross-Embodiment Human-like Behavior Execution for Humanoid Robots Lyu, Shipeng Wang, Fangyuan Lin, Weiwei Zhu, Luhao Navarro-Alarcon, David Guo, Guodong Robotics Achieving both behavioral similarity and appropriateness in human-like motion generation for humanoid robot remains an open challenge, further compounded by the lack of cross-embodiment adaptability. To address this problem, we propose HuBE, a bi-level closed-loop framework that integrates robot state, goal poses, and contextual situations to generate human-like behaviors, ensuring both behavioral similarity and appropriateness, and eliminating structural mismatches between motion generation and execution. To support this framework, we construct HPose, a context-enriched dataset featuring fine-grained situational annotations. Furthermore, we introduce a bone scaling-based data augmentation strategy that ensures millimeter-level compatibility across heterogeneous humanoid robots. Comprehensive evaluations on multiple commercial platforms demonstrate that HuBE significantly improves motion similarity, behavioral appropriateness, and computational efficiency over state-of-the-art baselines, establishing a solid foundation for transferable and human-like behavior execution across diverse humanoid robots. |
| title | HuBE: Cross-Embodiment Human-like Behavior Execution for Humanoid Robots |
| topic | Robotics |
| url | https://arxiv.org/abs/2508.19002 |