不加数据也能让视觉语言动作模型更抗干扰，只用轻量模块提升鲁棒性。
StableVLA: Towards Robust Vision-Language-Action Models without Extra Data
- 用信息瓶颈原理设计轻量适配模块，过滤视觉输入中的噪声。
- 无需额外数据，平均性能提升30%，参数增加少于1000万。
- 小模型（0.5B）也能媲美70亿参数大模型，适合资源受限场景。
现有视觉-语言-动作（VLA）模型在遭遇训练中未见的视觉干扰时，性能显著下降。本文系统评估了主流VLA模型的鲁棒性，发现其在真实世界复杂视觉条件下的表现受限。为此，提出基于信息论的轻量级适配器IB-Adapter，可选择性过滤视觉输入中的潜在噪声。该方法无需额外数据或增强策略，在仅增加少于1000万参数的前提下，使基线模型平均性能提升30%。即使使用14倍缩小的骨干网络（0.5B参数），且未在Open X-Embodiment上预训练，所提出的StableVLA仍达到与70亿参数级先进VLA相当的鲁棒性。在长程任务中保持高精度，并在合成与物理视觉扰动下优于OpenPi。
原文摘要 · Abstract (English)
It is infeasible to encompass all possible disturbances within the training dataset. This raises a critical question regarding the robustness of Vision-Language-Action (VLA) models when encountering unseen real-world visual disturbances, particularly under imperfect visual conditions. In this work, we conduct a systematic study based on recent state-of-the-art VLA models and reveal a significant performance drop when visual disturbances absent from the training data are introduced. To mitigate this issue, we propose a lightweight adapter module grounded in information theory, termed the Information Bottleneck Adapter (IB-Adapter), which selectively filters potential noise from visual inputs. Without requiring any extra data or augmentation strategies, IB-Adapter consistently improves over the baseline by an average of 30%, while adding fewer than 10M parameters, demonstrating notable efficiency and effectiveness. Furthermore, even with a 14x smaller backbone (0.5B parameters) and no pre-training on the Open X-Embodiment dataset, our model StableVLA achieves robustness competitive with 7B-scale state-of-the-art VLAs. With negligible parameter overhead (<10M), our approach maintains accuracy on long-horizon tasks and surpasses OpenPi under both synthetic and physical visual corruptions.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。