OGPO: One-Step Generative Policy Optimization for Real-Time Robot Control

Abstract:Real-time robot control demands fast action generation. Diffusion and flow matching policies for robot control require multi-step sampling, limiting their deployment in real-time scenarios. Natively reducing the sampling steps to one sacrifices representation quality and task performance, creating a trilemma among speed, fidelity, and performance. We present One-Step Generative Policy Optimization (OGPO), a systematic framework to resolve this trilemma. OGPO first pairs a lightweight architecture with the interval velocity principle for distillation-free one-step inference, while representation spreading prevents representation quality degradation. It then performs on-policy reinforcement learning (RL) fine-tuning on this fast, stable policy to break the imitation learning ceiling. Experiments on RoboMimic and OpenAI Gym benchmarks show that OGPO matches or exceeds multi-step baselines while achieving a 5-20 times inference speedup and over 120Hz control frequency. Physical deployment on a Franka-Emika-Panda robot validates real-world applicability. Project page: this https URL

Accepted at ACM MM 2026. 38 pages, including supplementary material. Project page: this https URL

Robotics (cs.RO)

arXiv:2601.20701 [cs.RO]

(or arXiv:2601.20701v2 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2601.20701 Focus to learn more arXiv-issued DOI via DataCite

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