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Self-Adaptive VLA for Robust Robot Deployment

arXiv · AI, language, vision and robotics · article · Sep 24, 2026 · UTC

While Vision-Language-Action (VLA) models demonstrate impressive capabilities in robotic manipulation, their memoryless nature renders them brittle to test-time environment shifts, particularly hardware shifts caused by wear or imperfect calibration. Enabling these models to self-adapt during deployment without requiring continuous on-site recalibration remains a critical bottleneck for real-world scalability. In this work, we introduce Self-Adaptive VLA, a novel post-training recipe that enables the policy to iteratively adapt to deployment-time hardware shifts leveraging its own rollouts as

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Evidence & attribution

First collected: 2026-09-25T06:12:46.948Z. This is not the publication date.