الملخص
Recent advances in Vision-Language-Action (VLA) models point toward general-purpose robotic intelligence by unifying perception, instruction, and control. Despite impressive progress, existing VLA models often adapt poorly due to \emph{tri-modal misalignment} among vision, language, and action, which weakens action grounding and hurts generalization and fine-tuning efficiency. In this work, we present Alignment-Guided Flow Transformer (AGFT), a novel framework that explicitly enforces tri-modal alignment through a dedicated alignment loss, bridging the representational gap across modalities and enhancing task adaptation. While prior research has predominantly emphasized bi-modal vision--language alignment, we systematically formalize and study tri-modal alignment in VLA models, and provide both ablations and analysis to isolate its role in improving adaptation and robustness. To further accelerate deployment, we adopt a flow-matching objective, enabling substantially fewer inference steps than diffusion-based policies while maintaining accuracy. Theoretically, we establish a quantitative connection between the tri-modal alignment gap and the optimization tightness of flow matching; empirically, experiments on the extensive benchmark show that AGFT achieves superior success rates and lower inference latency compared to SOTA baselines, underscoring tri-modal alignment as a key ingredient for scaling robust VLA manipulation.
الكلمات المفتاحية
الموضوع
بيانات النشر
- المجلة
- غير متاح
- وصول مفتوح
- وصول مفتوح أخضر
اقتبس هذه المقالة
APA 7
Hu, S., Wang, P., Zhou, Q., Zheng, G., Huang, Y., Shen, L., Zhang, Y., & Tao, D. (2026). Alignment-Guided Flow Transformer for Efficient Vision-Language-Action Policy Learning. https://omanscience.com/ar/articles/alignment-guided-flow-transformer-for-efficient-vision-language-action-policy-learning
MLA 9
Hu, Shengchao, et al. "Alignment-Guided Flow Transformer for Efficient Vision-Language-Action Policy Learning." https://omanscience.com/ar/articles/alignment-guided-flow-transformer-for-efficient-vision-language-action-policy-learning.
شيكاغو (المؤلف–التاريخ)
Hu, Shengchao, Peng Wang, Qiyang Zhou, Guodong Zheng, Yuqi Huang, Li Shen, Ya Zhang, and Dacheng Tao. 2026. "Alignment-Guided Flow Transformer for Efficient Vision-Language-Action Policy Learning." https://omanscience.com/ar/articles/alignment-guided-flow-transformer-for-efficient-vision-language-action-policy-learning.
هارفارد
Hu, S., Wang, P., Zhou, Q., Zheng, G., Huang, Y., Shen, L., Zhang, Y. and Tao, D. (2026) 'Alignment-Guided Flow Transformer for Efficient Vision-Language-Action Policy Learning', Available at: https://omanscience.com/ar/articles/alignment-guided-flow-transformer-for-efficient-vision-language-action-policy-learning.
فانكوفر
Hu S, Wang P, Zhou Q, Zheng G, Huang Y, Shen L, et al. Alignment-Guided Flow Transformer for Efficient Vision-Language-Action Policy Learning. https://omanscience.com/ar/articles/alignment-guided-flow-transformer-for-efficient-vision-language-action-policy-learning
IEEE
S. Hu, P. Wang, Q. Zhou, G. Zheng, Y. Huang, L. Shen, Y. Zhang, and D. Tao, "Alignment-Guided Flow Transformer for Efficient Vision-Language-Action Policy Learning," https://omanscience.com/ar/articles/alignment-guided-flow-transformer-for-efficient-vision-language-action-policy-learning.