RobustVLA: Robustness-Aware Reinforcement Post-Training for Vision-Language-Action Models

• URL: http://arxiv.org/abs/2511.01331v1
• Date: Mon, 03 Nov 2025 08:30:48 GMT
• Title: RobustVLA: Robustness-Aware Reinforcement Post-Training for Vision-Language-Action Models
• Authors: Hongyin Zhang, Shuo Zhang, Junxi Jin, Qixin Zeng, Runze Li, Donglin Wang,
• Abstract summary: Vision-Language-Action (VLA) models fail to generalize reliably in out-of-distribution deployments.<n>We introduce RobustVLA, a lightweight online RL post-training method designed to explicitly enhance the resilience of VLA models.<n>Our results highlight the importance of robustness-aware RL post-training as a key step toward improving the principled reliability and robustness of VLA models.
• Score: 33.503927352666096
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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