6 papers
Safe-RULE: Safe Reinforcement UnLEarning
Shixiong Jiang, Taozheng Zhu, Fanxin Kong
Offline safe reinforcement learning (Safe RL) enables policy learning without online interactions, making it suitable for safety-critical systems such as robotics systems. However,…
SafeVLA-Bench: A Benchmark for the Success-Safety Gap in Vision-Language-Action Models
Jialiang Fan, Weizhe Xu, Oleg Sokolsky +2
Vision-language-action (VLA) benchmarks measure whether a policy completes a requested manipulation task, but binary success can hide safety-relevant trajectory behavior: reaching…
Digital Guardians: The Past and The Future of Cyber-Physical Resilience
Saurabh Bagchi, Hyunseung Kim, Tarek Abdelzaher +20
Resilience in cyber-physical systems (CPS) is the fundamental ability to maintain safety and critical functionality despite adverse "perturbations," which includes security attacks…
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)…
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…
Vulnerability Analysis of Safe Reinforcement Learning via Inverse Constrained Reinforcement Learning
Jialiang Fan, Shixiong Jiang, Mengyu Liu +1
Safe reinforcement learning (Safe RL) aims to ensure policy performance while satisfying safety constraints. However, most existing Safe RL methods assume benign environments, maki…