Flow as Flow: Modeling Robot Velocity Fields as Probability Velocity Fields for Flow-Based Object Manipulation

Abstract:Cross-embodiment data have become central to training robotic foundation models. To leverage such heterogeneous data, we focus on flow-based object manipulation, where robot flows (robot velocity fields) serve as embodiment-agnostic motion representations. Previous studies do not formulate robot flows as dense velocity fields, but as displacements of sparse keypoints, even though dense velocity fields better match the continuous-time nature of motions. To address this, we propose Flow as Flow, a framework that models robot flows as probability flows based on a flow matching formulation. By naturally modeling such velocity fields within this formulation, our method achieves efficient and high-quality robot flow generation. Across standard benchmarks, our method outperforms representative baseline methods on standard metrics, while achieving approximately 24$\times$ faster generation than standard flow matching. Furthermore, through real-world experiments evaluating 9 methods with 260 trials per method across 13 manipulation tasks, we show that our method achieves a higher average success rate than the baseline methods.

Robotics (cs.RO)

arXiv:2606.23090 [cs.RO]

(or arXiv:2606.23090v3 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2606.23090 Focus to learn more arXiv-issued DOI via DataCite

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