5 papers
FAVLA: A Force-Adaptive Fast-Slow VLA model for Contact-Rich Robotic Manipulation
Yao Li, Peiyuan Tang, Wuyang Zhang +7
Force/torque feedback can substantially improve Vision-Language-Action (VLA) models on contact-rich manipulation, but most existing approaches fuse all modalities at a single opera…
ROMAN: Reward-Orchestrated Multi-Head Attention Network for Autonomous Driving System Testing
Jianlei Chi, Yuzhen Wu, Jiaxuan Hou +7
Automated Driving System (ADS) acts as the brain of autonomous vehicles, responsible for their safety and efficiency. Safe deployment requires thorough testing in diverse real-worl…
The Safety Reminder: A Soft Prompt to Reactivate Delayed Safety Awareness in Vision-Language Models
Peiyuan Tang, Haojie Xin, Xiaodong Zhang +3
As Vision-Language Models (VLMs) demonstrate increasing capabilities across real-world applications such as code generation and chatbot assistance, ensuring their safety has become…
On-Demand Scenario Generation for Testing Automated Driving Systems
Songyang Yan, Xiaodong Zhang, Kunkun Hao +7
The safety and reliability of Automated Driving Systems (ADS) are paramount, necessitating rigorous testing methodologies to uncover potential failures before deployment. Tradition…
LitSim: A Conflict-aware Policy for Long-term Interactive Traffic Simulation
Haojie Xin, Xiaodong Zhang, Renzhi Tang +5
Simulation is pivotal in evaluating the performance of autonomous driving systems due to the advantages of high efficiency and low cost compared to on-road testing. Bridging the ga…