VLA-GAUGE: Systematically Evaluating Generalization and Robustness in Vision-Language-Action Models
Overview
VLA-GAUGE evaluates Vision-Language-Action models, finding poor zero-shot performance (22.3%) and robustness (R-Scores 9.3). Fine-tuning improves results, highlighting the need for targeted evaluation and refinement in robotics systems.
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- arXiv Robotics — research abstractsVLA-GAUGE: Systematically Evaluating Generalization and Robustness in Vision-Language-Action Models
This research presents VLA-GA, a framework for evaluating the generalization and robustness of Vision-Language-Action models. The study finds that models perform poorly on zero-shot tasks and combinatorial perturbations, with mean Zero-Shot Scores of 22.3% and R-Scores of 9.3. Diagnosis-driven fine-tuning significantly improves performance, showing the importance of targeted evaluation and refinement.
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