StableVLA: Towards Robust Vision-Language-Action Models without Extra Data

Hugging Face Daily Papers Papers
vision-language-action robustness information-bottleneck adapter fine-tuning

Summary

This paper introduces an Information Bottleneck Adapter (IB-Adapter) for Vision-Language-Action (VLA) models to improve robustness against unseen visual disturbances without requiring extra data, achieving up to 30% improvement with minimal parameter overhead.

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.
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Paper page - StableVLA: Towards Robust Vision-Language-Action Models without Extra Data

Source: https://huggingface.co/papers/2605.18287 Published on May 18

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Submitted byhttps://huggingface.co/yfdeng10

yfdengon May 19

Abstract

Vision-Language-Action models exhibit degraded performance under unseen visual disturbances, but a lightweight information-theoretic adapter module significantly improves robustness with minimal parameter overhead.

It is infeasible to encompass all possible disturbances within the training dataset. This raises a critical question regarding therobustnessof Vision-Language-Action (VLA) models when encountering unseen real-worldvisual 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 whenvisual disturbancesabsent from the training data are introduced. To mitigate this issue, we propose a lightweightadapter modulegrounded ininformation theory, termed theInformation 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 achievesrobustnesscompetitive 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.

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