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arXiv Machine Learning By Perry Dong, Kuo-Han Hung, Dorsa Sadigh, Chelsea Finn

Reinforcement Learning for Real-Time Vision-Language-Action Policies

Read the original on arXiv Machine Learning →

The paper presents Real‑Time EXPO‑FT, a reinforcement learning framework that fine‑tunes large Vision‑Language‑Action models for real‑time robotic control. It separates slow, expressive action generation from fast, reactive edits, allowing a lightweight policy to adjust actions based on the latest observation. Experiments on the Kinetix benchmark and four dynamic real‑world tasks show that Real‑Time EXPO‑FT achieves superior performance, improving policy success rates from 42% to 97% with only ten minutes of online data and no human intervention.

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