Papers
FAST: Efficient Action Tokenization for Vision-Language-Action Models
Karl Pertsch, Kyle Stachowicz, Brian Ichter et al.
VQ-VLA: Improving Vision-Language-Action Models via Scaling Vector-Quantized Action Tokenizers
Yating Wang, Haoyi Zhu, Mingyu Liu et al.
CoT-VLA: Visual Chain-of-Thought Reasoning for Vision-Language-Action Models
Qingqing Zhao, Yao Lu, Moo Jin Kim et al.
MoManipVLA: Transferring Vision-language-action Models for General Mobile Manipulation
Zhenyu Wu, Yuheng Zhou, Xiuwei Xu et al.
Spec-VLA: Speculative Decoding for Vision-Language-Action Models with Relaxed Acceptance
Songsheng Wang, Rucheng Yu, Zhihang Yuan et al.
Do What? Teaching Vision-Language-Action Models to Reject the Impossible
Wen-Han Hsieh, Elvis Hsieh, Dantong Niu et al.
CoA-VLA: Improving Vision-Language-Action Models via Visual-Text Chain-of-Affordance
Jinming Li, Yichen Zhu, Zhibin Tang et al.
Exploring the Adversarial Vulnerabilities of Vision-Language-Action Models in Robotics
Taowen Wang, Cheng Han, James Liang et al.
Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success
Moo Jin Kim, Chelsea Finn, Percy Liang