arXiv AI June 19, 2026 By Sangoh Lee, Sangwoo Mo, Wook-Shin Han

Bring My Cup! Personalizing Vision-Language-Action Models with Visual Attentive Prompting

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Jun 16

Action with Visual Primitives

arXiv:2605. 22183v3 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models have emerged as a promising paradigm for generalist robotic manipulation.

By Weilong Guo, Yuchen Wang, Renping Zhou, Yunfeng Zhang, Rui Fang, Yuyang Pang, Wenda Xu, Gao Huang

robotics multimodal

arXiv AI

Jul 3

Restoring Linguistic Grounding in VLA Models via Train-Free Attention Recalibration

arXiv:2603. 06001v2 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models enable robots to perform manipulation tasks directly from natural language instructions and are increasingly viewed as a foundation for generalist robotic policies.

By Ninghao Zhang, Bin Zhu, Shijie Zhou, Jingjing Chen

robotics multimodal benchmarks safety

arXiv Computer Vision

3d ago

Correcting WHERE, Preserving HOW: Compositional Generalization for Vision-Language-Action Models via Referential Guidance

arXiv:2609.38616v1 Announce Type: cross Abstract: While Vision-Language-Action (VLA) models enable flexible action generation, their generalization across diverse environmental elements, including ma...

By Yanyan Zhang, Disheng Liu, Xinpeng Li, Chaoda Song, Mohsen Hariri, Debargha Ganguly, Wang Yang, Kai Ye, Bryce Grant, Vipin Chaudhary, Yu Yin

robotics multimodal

arXiv Machine Learning

Jun 16

AVA-VLA: Improving Vision-Language-Action models with Active Visual Attention

arXiv:2511. 18960v4 Announce Type: replace Abstract: Vision-Language-Action (VLA) models have shown remarkable progress in embodied tasks recently, but most methods process visual observations independently at each timestep.

By Lei Xiao, Jifeng Li, Juntao Gao, Feiyang Ye, Yan Jin, Jingjing Qian, Jing Zhang, Yong Wu, Xiaoyuan Yu

agents reinforcement-learning robotics multimodal benchmarks

arXiv Computer Vision

Sep 3

Towards Zero-Shot Transfer Across Embodiments For Driving VLAs

The paper investigates how Vision‑Language‑Action (VLA) models can generalise across different driving environments and camera setups. It introduces a multi‑dataset training strategy and an auxiliary objective called BEV‑Forcing, which injects bird‑eye‑view spatial information into the VLA backbone to improve both in‑distribution and out‑of‑distribution performance on a limited number of camera rigs. The authors observe that while BEV‑Forcing helps when training data is scarce, its advantage diminishes as the number of training embodiments grows, suggesting that scaling diversity may reduce the impact of such auxiliary tasks.

By Caio Azevedo, Stefano Sabatini, Sascha Hornauer, Fabien Moutarde

agents robotics fine-tuning multimodal

arXiv Machine Learning

Jun 11

Learning What to Say to Your VLA: Mostly Harmless Vision Language Action Model Steering

arXiv:2606. 12299v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models provide a natural language interface to robot control, but the mapping from language to behavior is often brittle and unintuitive: semantically similar instructions can induce drastically different behaviors, while some capabilities may not be elicitable through prompting alone.

By Hyun Joe Jeong, Gokul Swamy, Andrea Bajcsy

llms robotics fine-tuning multimodal