5 papers
SLoSa: Small, Low-Power, and Safe Foundation Models for Robotic Task Planning
Weizhe Xu, Jialiang Fan, Essien Taylor +3
Safe robotic task planning requires planners that can reliably achieve task goals while satisfying safety constraints. Classical planners search over explicit symbolic task represe…
SafeVLA-Bench: A Benchmark for the Success-Safety Gap in Vision-Language-Action Models
Jialiang Fan, Weizhe Xu, Oleg Sokolsky +3
Vision-language-action (VLA) benchmarks measure whether a policy completes a requested manipulation task, but binary success can hide safety violations along the trajectory: a poli…
SafePilot: A Framework for Assuring LLM-enabled Cyber-Physical Systems
Weizhe Xu, Mengyu Liu, Fanxin Kong
Large Language Models (LLMs), deep learning architectures with typically over 10 billion parameters, have recently begun to be integrated into various cyber-physical systems (CPS)…
SafeGen-LLM: Enhancing Safety Generalization in Task Planning for Robotic Systems
Jialiang Fan, Weizhe Xu, Mengyu Liu +3
Safety-critical task planning in robotic systems remains challenging: classical planners suffer from poor scalability, Reinforcement Learning (RL)-based methods generalize poorly,…
Enhancing LLM-Based Test Generation by Eliminating Covered Code
WeiZhe Xu, Mengyu Liu, Fanxin Kong
Automated test generation is essential for software quality assurance, with coverage rate serving as a key metric to ensure thorough testing. Recent advancements in Large Language…