RobustVLA: 視覚言語行動モデルのためのロバスト性を考慮した強化学習ポストトレーニング
RobustVLA: Robustness-Aware Reinforcement Post-Training for Vision-Language-Action Models
事前学習済みVLAモデルに対し、ヤコビアン正則化と平滑化正則化を組み込んだオンライン強化学習ポストトレーニングを行い、観測ノイズや行動摂動に対するロバスト性を高める手法を提案した。
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著者: Hongyin Zhang, Shuo Zhang, Junxi Jin, Qixin Zeng, Runze Li, Donglin Wang
分類: cs.RO, cs.LG
原文アブストラクト
Vision-Language-Action (VLA) models have recently emerged as powerful general-purpose policies for robotic manipulation, benefiting from large-scale multi-modal pre-training. However, they often fail to generalize reliably in out-of-distribution deployments, where unavoidable disturbances such as observation noise, sensor errors, or actuation perturbations become prevalent. While recent Reinforcement Learning (RL)-based post-training provides a practical means to adapt pre-trained VLA models, existing methods mainly emphasize reward maximization and overlook robustness to environmental uncertainty. In this work, we introduce RobustVLA, a lightweight online RL post-training method designed to explicitly enhance the resilience of VLA models. Through a systematic robustness analysis, we identify two key regularizations: Jacobian regularization, which mitigates sensitivity to observation noise, and smoothness regularization, which stabilizes policies under action perturbations. Extensive experiments across diverse robotic environments demonstrate that RobustVLA significantly outperforms prior state-of-the-art methods in robustness and reliability. Our results highlight the importance of principled robustness-aware RL post-training as a key step toward improving the reliability and robustness of VLA models.
