# CLAW: Composable Language-Annotated Whole-body Motion Generation

arXiv: https://arxiv.org/abs/2604.11251 · HTML: https://mlsanity.com/paper/2604.11251/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Apr 2026.

## Built on by (most cited first, 1 of 1)

- [TEXEDO : Test Time Scaling for Controller-aware Language-conditioned Humanoid Motion Generation](https://mlsanity.com/paper/2606.22998/) (2026)

## Abstract (arXiv)

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