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ARXIV:2603.24021 · ROBOTICS MOTION GENERATION · SUBMITTED 26 MAR · 20:30 UTC · FRESHNESS STALE
ARXIV:2603.24021ROBOTICS MOTION GENERATIONSUBMITTED 26 MAR · 20:30 UTCFRESHNESS STALELi Gao · Fuzhi Yang · Jianhui Chen · Liu Liu · Yao Zheng · Yang Cai · +1 at arXiv
A foundational dataset and unified framework for text-driven quadruped motion generation and control, enabling real-time, expressive robot movements.
Opportunity summary
Pain A foundational dataset and unified framework for text-driven quadruped motion generation and control, enabling real-time, expressive robot movements.
Evidence 0 refs | 0 sources | 50% coverage
Blocker Evidence unverified
A foundational dataset and unified framework for text-driven quadruped motion generation and control, enabling real-time, expressive robot movements. Current quadruped motion datasets are limited to a few mocap primitives (e.g., walk, trot, sit) and…
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…
ScienceToStartup currently rates this 8.0/10 on the public viability pass. 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,…
Robotics Motion Generation moved forward this cycle; last verified April 2026. Public score 8.0/10. Implementation evidence is present through a linked repository.
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A foundational dataset and unified framework for text-driven quadruped motion generation and control, enabling real-time, expressive robot movements.
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10.48550/arXiv.2603.24021A foundational dataset and unified framework for text-driven quadruped motion generation and control, enabling real-time, expressive robot movements.
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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PROBLEM
A foundational dataset and unified framework for text-driven quadruped motion generation and control, enabling real-time, expressive robot movements. Current quadruped motion datasets are limited to a few mocap primitives (e.g., walk, trot, sit) and lack diverse behaviors with r...
METHOD
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. Cu...
RESULT
ScienceToStartup currently rates this 8.0/10 on the public viability pass. 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 lab...
WHY NOW
Robotics Motion Generation moved forward this cycle; last verified April 2026. Public score 8.0/10. Implementation evidence is present through a linked repository.
Abstract-backed public claims while anchored extraction refreshes.
A foundational dataset and unified framework for text-driven quadruped motion generation and control, enabling real-time, expressive robot movements. Current quadruped motion datasets are limited to a few mocap primitives (e.g., walk, trot, sit) and lack diverse behaviors with rich language grounding.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 8.0/10 on the public viability pass. 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. A public repository is linked, so build verification can inspect implementation evidence instead of treating the paper as PDF-only.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Robotics Motion Generation moved forward this cycle; last verified April 2026. Public score 8.0/10. Implementation evidence is present through a linked repository.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A foundational dataset and unified framework for text-driven quadruped motion generation and control, enabling real-time, expressive robot movements.
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Robotics Motion Generation
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