6 citations · 6 across the 4 of their papers we have counts for
5 papers
Quantile Head for Vision-Language-Action Models
Xuan Wang, Yinan Wu, Haoran Duan +1
Vision-Language-Action (VLA) models integrate pretrained Vision-Language Models (VLMs) with action heads for robot control. Common action heads have distinct limitations: point reg…
PhaseLoRA: Control-Regime-Conditioned Low-Rank Adaptation for Continuous-Action Vision-Language-Action Policies
Yufei Guo, Yinan Wu, Haoran Duan +2
Parameter-efficient fine-tuning (PEFT) is a natural way to adapt pretrained vision-language-action (VLA) policies, but most adapter designs apply temporally static updates througho…
CofactVLA: Deconfounding Vision-Language-Action Models via Counterfactual Intervention
Yan Zhang, Yinan Wu, Haoran Duan +1
Vision-Language-Action (VLA) models have driven significant progress in robotic manipulation, yet they fundamentally struggle with the vision-override phenomenon. Driven by the sev…
QuoVLA: Quotient Space for Vision-Language-Action Models
Xuan Wang, Yinan Wu, Haoran Duan +1
Vision-Language-Action (VLA) models commonly adapt pretrained Vision-Language Models (VLMs) to robot control by mapping visual observations and language instructions to continuous…
Adversarial Counterfactual Environment Model Learning
Xiong-Hui Chen, Yang Yu, Zheng-Mao Zhu +8
A good model for action-effect prediction, named environment model, is important to achieve sample-efficient decision-making policy learning in many domains like robot control, rec…