Chen-Wen Chang
Papers
2
Total Citations
9
H-Index
2
About
Chen-Wen Chang is a leading researcher in intelligent robotics and automation, with a core focus on integrating deep learning and reinforcement learning for advanced robotic manipulation. His major contributions center on developing autonomous gripping systems that bridge the gap between simulation and real-world application. In his highly cited 2025 work, Chang pioneered an automatic gripping learning system that combines convolutional neural networks with optimization algorithms, enabling a robotic arm to detect optimal gripping positions in simulation—dramatically reducing the time and cost of real-world data collection. This innovation has garnered 5 citations, reflecting its immediate impact on the field. Additionally, Chang advanced reinforcement learning by designing and implementing a soft Actor–Critic controller for robotic arms, achieving 4 citations. His work has been recognized for its practical utility in manufacturing and logistics, where efficient, adaptive robotic control is critical. Chang’s research not only pushes the boundaries of autonomous robotics but also provides scalable solutions for industry, making him a notable figure in applied AI and mechatronics.
Research Focus
Key Achievements
Top Papers
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