EXPO-FT: 視覚言語行動モデルのサンプル効率的強化学習ファインチューニング
EXPO-FT: Sample-Efficient Reinforcement Learning Finetuning for Vision-Language-Action Models
事前学習済みの視覚言語行動(VLA)モデルを強化学習で安定かつサンプル効率的にファインチューニングするシステムEXPO-FTを提案し、高精度・動的動作を要する複数の操作タスクで完全成功率を達成した。
詳しい要約
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著者: Perry Dong, Kuo-Han Hung, Tian Gao, Dorsa Sadigh, Chelsea Finn
分類: cs.RO, cs.AI
原文アブストラクト
The ability to efficiently and reliably learn new tasks has been a foundational challenge in robotics. Vision-Language-Action (VLA) models have demonstrated strong generalization across diverse manipulation tasks, yet pretrained policies consistently fall short of the reliability required for real-world deployment. Reinforcement learning (RL) fine-tuning offers a promising path to bridge this gap, but existing approaches either train from scratch without fully leveraging pretrained priors, or fine-tune VLAs without achieving the sample efficiency and success rates that practical deployment demands. We present EXPO-FT, a system for stable, sample-efficient RL finetuning of pretrained VLA policies that closes this gap. Our system solves a suite of challenging manipulation tasks, including routing string lights and inserting the plug to light it up, striking a pool ball into a pocket, and inserting a flower into a wine bottle, each requiring combinations of high precision, dynamic actions, and robustness to varied initial states. Our system achieves perfect task performance (30/30 successes) across all evaluated tasks within an average of 19.1 minutes of online robot data, outperforming both prior RL-from-scratch and VLA finetuning approaches. We release an open-source codebase with the aim of facilitating broader adoption of RL finetuning of VLA models in robotics.
