Anomaly Detection in UR5 Robots Using Reinforcement Learning for Smart Manufacturing
Li, Jianzhi
dc.title: Anomaly Detection in UR5 Robots Using Reinforcement Learning for Smart Manufacturing
dc.description.abstract: Traditional anomaly detection methods in industrial robotics often rely on supervised
learning, which struggles with dynamic environments and requires extensive labeled data. In this study, we propose a reinforcement learning (RL) framework for real-time, unsupervised anomaly detection in a UR5 robotic arm. Using high-dimensional data for different payloads and operational cond