用可组合的运动基元生成带语言标注的仿人机器人动作数据
CLAW: Composable Language-Annotated Whole-body Motion Generation
- 基于运动基元构建可参数化的动作生成流程
- 在模拟中生成物理可行的动作轨迹并配以自然语言描述
- 支持实时键盘与时间线编辑，适合机器人动作数据集构建
为仿人机器人训练语言控制的全身动作控制器需要大规模动作-语言数据集。现有基于动捕的方法成本高且多样性有限，而文本到动作生成模型输出的仅为运动学结果，无法保证物理可行性。我们提出CLAW，一个面向Unitree G1仿人机器人的可扩展语言标注全身动作数据生成管道。CLAW从运动规划器生成运动基元，通过移动方向、速度、骨盆高度、持续时间等参数化，并提供两种浏览器界面——实时键盘模式和基于时间线的序列编辑器，支持探索式与批量数据采集。低层控制器在MuJoCo仿真中跟踪这些参考轨迹，生成物理合理的运动路径。同时，基于模板的引擎在片段与轨迹层面生成多样化的自然语言标注。为支持仿人机器人学习的规模化动作-语言数据生成，系统已开源：https://github.com/JianuoCao/CLAW
原文摘要 · Abstract (English)
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
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