StructRL: フローベースVLAのための構造化アクション空間探索
StructRL: Structured Action-Space Exploration for Flow-Based VLAs
フローベースの視覚言語行動モデル(VLA)のオンライン強化学習において、ノイズをアクション空間に直接注入する構造化探索手法StructRLを提案し、シミュレーションと実世界タスクで性能を向上させた。
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著者: Jiarui Yang, Bin Zhu, Jingjing Chen, Na Zou, Yanwei Fu, Jianggang Zhu, Yu-Gang Jiang
分類: cs.RO
原文アブストラクト
Flow-based Vision-Language-Action (VLA) models are now widely used for continuous robotic manipulation, and online reinforcement learning (RL) is emerging as a key technique for adapting them to new tasks. Existing RL methods typically inject stochasticity inside the denoising chain, often through isotropic or temporally independent noise. However, effective robot exploration calls for structured noise: temporally smooth and scaled differently across action groups. We show that simply switching the in-chain noise to a structured form does not suffice: noise added at an intermediate flow time can be weakened by the remaining denoising steps before execution, a phenomenon we call \emph{Structured Noise Dilution}. We propose \textbf{StructRL}, which avoids dilution by relocating policy stochasticity to the action space via three coupled choices: (i) a deterministic ODE decoder, (ii) structured noise injected directly in the action space, and (iii) last-step replay, where policy-gradient updates avoid assigning likelihoods to intermediate denoising states. This keeps structured exploration tied to the executed action while providing a tractable training signal for the flow decoder. Across three flow-based VLA models on multiple simulated manipulation benchmarks and two real-world tasks, StructRL improves exploration efficiency and OOD performance over prior in-chain baselines, demonstrating the effectiveness of structured action-space exploration for adapting flow-based VLA with RL. \textbf{Project page:} https://flyfaerss.github.io/structrl/