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| Format: | Preprint |
| Veröffentlicht: |
2026
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| Online-Zugang: | https://arxiv.org/abs/2604.11251 |
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| _version_ | 1866911600359768064 |
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| author | Cao, Jianuo Chen, Yuxin Tomizuka, Masayoshi |
| author_facet | Cao, Jianuo Chen, Yuxin Tomizuka, Masayoshi |
| contents | Training language-conditioned whole-body controllers for humanoid robots demands large-scale motion-language datasets. Existing approaches based on motion capture are costly and limited in diversity, while text-to-motion generative models produce purely kinematic outputs that are not guaranteed to be physically feasible. We present CLAW, a pipeline for scalable generation of language-annotated whole-body motion data for the Unitree G1 humanoid robot. CLAW composes motion primitives from a kinematic planner, parameterized by movement, heading, speed, pelvis height, and duration, and provides two browser-based interfaces--a real-time keyboard mode and a timeline-based sequence editor--for exploratory and batch data collection. A low-level controller tracks these references in MuJoCo simulation, yielding physically grounded trajectories. In parallel, a template-based engine generates diverse natural-language annotations at both segment and trajectory levels. To support scalable generation of motion-language paired data for humanoid robot learning, we make our system publicly available at: https://github.com/JianuoCao/CLAW |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_11251 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | CLAW: Composable Language-Annotated Whole-body Motion Generation Cao, Jianuo Chen, Yuxin Tomizuka, Masayoshi Robotics Training language-conditioned whole-body controllers for humanoid robots demands large-scale motion-language datasets. Existing approaches based on motion capture are costly and limited in diversity, while text-to-motion generative models produce purely kinematic outputs that are not guaranteed to be physically feasible. We present CLAW, a pipeline for scalable generation of language-annotated whole-body motion data for the Unitree G1 humanoid robot. CLAW composes motion primitives from a kinematic planner, parameterized by movement, heading, speed, pelvis height, and duration, and provides two browser-based interfaces--a real-time keyboard mode and a timeline-based sequence editor--for exploratory and batch data collection. A low-level controller tracks these references in MuJoCo simulation, yielding physically grounded trajectories. In parallel, a template-based engine generates diverse natural-language annotations at both segment and trajectory levels. To support scalable generation of motion-language paired data for humanoid robot learning, we make our system publicly available at: https://github.com/JianuoCao/CLAW |
| title | CLAW: Composable Language-Annotated Whole-body Motion Generation |
| topic | Robotics |
| url | https://arxiv.org/abs/2604.11251 |