QuadFM: Foundational Text-Driven Quadruped Motion Dataset for Generation and Control

• URL: http://arxiv.org/abs/2603.24021v1
• Date: Wed, 25 Mar 2026 07:30:25 GMT
• Title: QuadFM: Foundational Text-Driven Quadruped Motion Dataset for Generation and Control
• Abstract summary: We introduce Quadruped Foundational Motion (QuadFM), the first large-scale, ultra-high-fidelity dataset for text-to-motion generation and general motion control.<n>QuadFM contains 11,784 curated motion clips spanning locomotion, interactive, and emotion-expressive behaviors.<n>We propose Gen2Control RL, a unified framework that jointly trains a general motion controller and a text-to-motion generator.
• Score: 18.78068897227934
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite significant advances in quadrupedal robotics, a critical gap persists in foundational motion resources that holistically integrate diverse locomotion, emotionally expressive behaviors, and rich language semantics-essential for agile, intuitive human-robot interaction. Current quadruped motion datasets are limited to a few mocap primitives (e.g., walk, trot, sit) and lack diverse behaviors with rich language grounding. To bridge this gap, we introduce Quadruped Foundational Motion (QuadFM) , the first large-scale, ultra-high-fidelity dataset designed for text-to-motion generation and general motion control. QuadFM contains 11,784 curated motion clips spanning locomotion, interactive, and emotion-expressive behaviors (e.g., dancing, stretching, peeing), each with three-layer annotation-fine-grained action labels, interaction scenarios, and natural language commands-totaling 35,352 descriptions to support language-conditioned understanding and command execution. We further propose Gen2Control RL, a unified framework that jointly trains a general motion controller and a text-to-motion generator, enabling efficient end-to-end inference on edge hardware. On a real quadruped robot with an NVIDIA Orin, our system achieves real-time motion synthesis (<500 ms latency). Simulation and real-world results show realistic, diverse motions while maintaining robust physical interaction. The dataset will be released at https://github.com/GaoLii/QuadFM.

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