Run-time Observation Interventions Make Vision-Language-Action Models More Visually Robust

• URL: http://arxiv.org/abs/2410.01971v1
• Date: Wed, 2 Oct 2024 19:29:24 GMT
• Title: Run-time Observation Interventions Make Vision-Language-Action Models More Visually Robust
• Authors: Asher J. Hancock, Allen Z. Ren, Anirudha Majumdar,
• Abstract summary: Vision-language-action (VLA) models trained on large-scale internet data and robot demonstrations have the potential to serve as generalist robot policies. We introduce Bring Your Own VLA (BYOVLA): a run-time intervention scheme that dynamically identifies regions of the input image that the model is sensitive to. We show that BYOVLA enables state-of-the-art VLA models to nearly retain their nominal performance in the presence of distractor objects and backgrounds.
• Score: 9.647148940880381
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Vision-language-action (VLA) models trained on large-scale internet data and robot demonstrations have the potential to serve as generalist robot policies. However, despite their large-scale training, VLAs are often brittle to task-irrelevant visual details such as distractor objects or background colors. We introduce Bring Your Own VLA (BYOVLA): a run-time intervention scheme that (1) dynamically identifies regions of the input image that the model is sensitive to, and (2) minimally alters task-irrelevant regions to reduce the model's sensitivity using automated image editing tools. Our approach is compatible with any off the shelf VLA without model fine-tuning or access to the model's weights. Hardware experiments on language-instructed manipulation tasks demonstrate that BYOVLA enables state-of-the-art VLA models to nearly retain their nominal performance in the presence of distractor objects and backgrounds, which otherwise degrade task success rates by up to 40%. Website with additional information, videos, and code: https://aasherh.github.io/byovla/ .

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