SOURCE-LINKED INTELLIGENCE
MaskVLA: Visual Masking Against Trajectory Overfitting of Vision-Language-Action Model
Vision-Language-Action (VLA) models integrate vision-language understanding with executable robot actions, enabling end-to-end learning for robot control. However, our empirical analysis reveals that existing models exhibit severe trajectory overfitting when finetuned on limited datasets. To guide the model in effectively utilizing wrist camera information, we propose MaskVLA, a masking-based fine-tuning strategy. By randomly masking a small portion of the main camera's visual information, the model is guided to autonomously learn more fine-grained, task-relevant, and effective visual features
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-20T11:31:46.000Z
First collected: 2026-09-23T09:51:33.063Z. This is not the publication date.