CLAW: Composable Language-Annotated Whole-body Motion Generation

Abstract

Training language-conditioned whole-body controllers for humanoid robots requires large-scale datasets pairing motion trajectories with natural-language descriptions.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.Therefore, we present CLAW, an interactive web-based pipeline for scalable generation of language-annotated whole-body motion data for the Unitree G1 humanoid robot. CLAW treats the motion modes of a kinematic planner as composable building blocks, each 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 whole-body controller tracks the planner's kinematic references in MuJoCo simulation, producing physically grounded trajectories recorded at 50Hz. Simultaneously, a deterministic template-based annotation engine generates diverse natural-language descriptions at multiple stylistic registers for every segment and for the full trajectory. We release the system as open source to support scalable generation of language-motion paired data for humanoid robot learning.