NarrativeFlow: Flow-Based Vision-Language-Action Model Using Robot Velocity Fields

Abstract:We focus on language-conditioned flow-based manipulation, where robot flows (robot velocity fields) serve as embodiment-agnostic, motion-centric representations for leveraging data collected from multiple robot platforms. This task is crucial because language-conditioned manipulation is essential for practical robotic systems, yet scaling robot foundation models remains limited by the labor-intensive collection of embodiment-specific data. Existing methods either coarsely approximate robot flows with sparse keypoint displacements, or cannot handle language-conditioned manipulation. To address this limitation, we propose NarrativeFlow, which models robot flows as continuous velocity fields using a flow-matching formulation conditioned on language. Accordingly, NarrativeFlow generates robot flows that are physically consistent with real-world manipulation. To validate NarrativeFlow, we have conducted experiments on standard datasets for language-conditioned manipulation. The experimental results show that NarrativeFlow outperforms representative baseline methods on standard evaluation metrics. Furthermore, through real-world experiments, we show that NarrativeFlow achieves higher success rates than baseline methods across multiple manipulation tasks. The project page is available at this https URL

Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)

arXiv:2610.00981 [cs.RO]

(or arXiv:2610.00981v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2610.00981 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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