提升视觉语言动作模型在扰动下的可靠性，通过强化学习显式增强鲁棒性。
RobustVLA: Robustness-Aware Reinforcement Post-Training for Vision-Language-Action Models
- 引入雅可比与平滑正则化，降低对观测噪声和执行扰动的敏感度。
- 在多种机器人环境中，鲁棒性显著优于现有最先进方法。
- 适合关注实际部署中模型可靠性的机器人研究者与工程师。
视觉语言动作（VLA）模型近年来成为机器人操作的强大通用策略，得益于大规模多模态预训练。然而，在分布外部署中，不可避免的干扰如观测噪声、传感器误差或执行扰动普遍存在时，其泛化能力往往不可靠。尽管基于强化学习（RL）的后训练提供了一种实用的适应方式，但现有方法主要聚焦于奖励最大化，忽视了对环境不确定性的鲁棒性。本文提出RobustVLA，一种轻量级在线强化学习后训练方法，旨在显式增强VLA模型的抗扰能力。通过系统性鲁棒性分析，我们识别出两项关键正则化：雅可比正则化（缓解观测噪声敏感性）与平滑性正则化（稳定动作扰动下的策略）。在多样机器人环境中的大量实验表明，RobustVLA在鲁棒性和可靠性方面显著优于先前最先进方法。结果强调了有原则的鲁棒性感知强化学习后训练对于提升VLA模型可靠性和鲁棒性的关键作用。
原文摘要 · Abstract (English)
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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