12 citations · 27 across the 7 of their papers we have counts for
7 papers
LADEV: A Language-Driven Testing and Evaluation Platform for Vision-Language-Action Models in Robotic Manipulation
Zhijie Wang, Zhehua Zhou, Jiayang Song +3
Building on the advancements of Large Language Models (LLMs) and Vision Language Models (VLMs), recent research has introduced Vision-Language-Action (VLA) models as an integrated…
VLATest: Testing and Evaluating Vision-Language-Action Models for Robotic Manipulation
Zhijie Wang, Zhehua Zhou, Jiayang Song +3
The rapid advancement of generative AI and multi-modal foundation models has shown significant potential in advancing robotic manipulation. Vision-language-action (VLA) models, in…
MORTAR: A Model-based Runtime Action Repair Framework for AI-enabled Cyber-Physical Systems
Renzhi Wang, Zhehua Zhou, Jiayang Song +3
Cyber-Physical Systems (CPSs) are increasingly prevalent across various industrial and daily-life domains, with applications ranging from robotic operations to autonomous driving.…
Multilingual Blending: LLM Safety Alignment Evaluation with Language Mixture
Jiayang Song, Yuheng Huang, Zhehua Zhou +1
As safety remains a crucial concern throughout the development lifecycle of Large Language Models (LLMs), researchers and industrial practitioners have increasingly focused on safe…
GenSafe: A Generalizable Safety Enhancer for Safe Reinforcement Learning Algorithms Based on Reduced Order Markov Decision Process Model
Zhehua Zhou, Xuan Xie, Jiayang Song +2
Safe Reinforcement Learning (SRL) aims to realize a safe learning process for Deep Reinforcement Learning (DRL) algorithms by incorporating safety constraints. However, the efficac…
Online Safety Analysis for LLMs: a Benchmark, an Assessment, and a Path Forward
Xuan Xie, Jiayang Song, Zhehua Zhou +3
While Large Language Models (LLMs) have seen widespread applications across numerous fields, their limited interpretability poses concerns regarding their safe operations from mult…