Bring My Cup! Personalizing Vision-Language-Action Models with Visual Attentive Prompting
📰 ArXiv cs.AI
Learn to personalize vision-language-action models with visual attentive prompting for robot tasks like fetching specific objects
Action Steps
- Implement visual attentive prompting in a VLA model to focus on specific objects
- Train the model with few-shot reference images of user-specific objects
- Test the model's ability to identify and control personalized objects
- Apply the model to real-world robotics tasks like object fetching
- Compare the performance of the model with and without visual attentive prompting
Who Needs to Know This
Robotics and AI engineers can benefit from this research to improve personalized command execution in vision-language-action models
Key Insight
💡 Visual attentive prompting enables VLA models to identify and control specific objects with few reference images
Share This
🤖 Personalize vision-language-action models with visual attentive prompting for tasks like 'bring my cup'! #AI #Robotics
Key Takeaways
Learn to personalize vision-language-action models with visual attentive prompting for robot tasks like fetching specific objects
Full Article
Title: Bring My Cup! Personalizing Vision-Language-Action Models with Visual Attentive Prompting
Abstract:
arXiv:2512.20014v3 Announce Type: replace-cross Abstract: While Vision-Language-Action (VLA) models generalize well to generic instructions, they struggle with personalized commands such as "bring my cup," where the robot must act on one specific instance among visually similar objects. We study this setting of manipulating personal objects, in which a VLA must identify and control a user-specific object unseen during training using only a few reference images. To address this challenge, we prop
Abstract:
arXiv:2512.20014v3 Announce Type: replace-cross Abstract: While Vision-Language-Action (VLA) models generalize well to generic instructions, they struggle with personalized commands such as "bring my cup," where the robot must act on one specific instance among visually similar objects. We study this setting of manipulating personal objects, in which a VLA must identify and control a user-specific object unseen during training using only a few reference images. To address this challenge, we prop
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