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About

Dawn An is a robotics and artificial intelligence researcher whose work bridges intelligent control, anomaly detection, and fault diagnosis for advanced robotic systems. Her primary research areas include reinforcement learning-based control, AI-driven posture management, and deep learning for industrial robot safety. An’s most cited work, “Reinforcement learning and neural network-based artificial intelligence control algorithm for self-balancing quadruped robot” (2021, 28 citations), introduces a novel control framework that enables dynamic stability in legged robots, a critical challenge in autonomous locomotion. She further advanced robot manipulation with her 2022 study on AI-based posture control for 7-DOF manipulators, addressing the growing complexity of redundant robotic systems. In safety-critical applications, An has made notable contributions to predictive maintenance: her 2024 paper on anomaly detection using graph convolutional network–variational autoencoder models (11 citations) leverages time-series vibration and current data to identify failures in industrial robot modules. Additionally, her 2023 work on fault diagnosis for human coexistence robots (5 citations) employs convolutional neural networks with time-series data generation and image encoding, proactively preventing safety hazards in collaborative environments. An’s research is distinguished by its practical integration of AI with real-world industrial constraints, earning her recognition for enhancing both robot autonomy and operational reliability.