StableVLA: Towards Robust Vision-Language-Action Models without Extra Data
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866911694059470848 |
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| author | Fu, Yiyang Zhang, Chubin Gong, Shukai Deng, Yufan Sun, Kaiwei Min, Qiyang Hou, Qibin Tang, Yansong Wang, Jianan Zhou, Daquan |
| author_facet | Fu, Yiyang Zhang, Chubin Gong, Shukai Deng, Yufan Sun, Kaiwei Min, Qiyang Hou, Qibin Tang, Yansong Wang, Jianan Zhou, Daquan |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2605_18287 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | StableVLA: Towards Robust Vision-Language-Action Models without Extra Data Fu, Yiyang Zhang, Chubin Gong, Shukai Deng, Yufan Sun, Kaiwei Min, Qiyang Hou, Qibin Tang, Yansong Wang, Jianan Zhou, Daquan Computer Vision and Pattern Recognition Robotics 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. |
| title | StableVLA: Towards Robust Vision-Language-Action Models without Extra Data |
| topic | Computer Vision and Pattern Recognition Robotics |
| url | https://arxiv.org/abs/2605.18287 |