Dr. Yaxiong Wu (BRID: 00917.00.90205) is an Assistant Research Fellow at the Institute of Automation, Chinese Academy of Sciences (CAS), affiliated with the State Key Laboratory of Multimodal Artificial Intelligence Systems. He earned both his B.Eng. (2019) and Ph.D. (2024) in Mechanical Engineering from the University of Science and Technology Beijing, demonstrating a consistent academic excellence in robotics and control systems. Following his doctoral studies, he joined CAS as a Postdoctoral Fellow and later advanced to his current role as an Assistant Research Fellow. His professional experience spans across musculoskeletal robotics, biomechanical modeling, intelligent control, and human–machine interaction, with a strong interdisciplinary approach integrating mechanical design, neural control principles, and artificial intelligence. Dr. Wu’s research interests focus on bio-inspired musculoskeletal robotic systems, equilibrium-point control theory, compliant motion learning, and brain–machine fusion technologies, aiming to bridge biological mechanisms with robotic intelligence for humanoid applications. His research skills include advanced control algorithm development, multimodal data fusion, robotic system modeling, reinforcement learning, and experimental validation of human-like motion systems. As Principal Investigator of an NSFC Youth Science Fund Project and participant in several national R&D programs funded by the Ministry of Science and Technology and Ministry of Industry and Information Technology, Dr. Wu contributes to China’s major strategic initiatives in humanoid robotics and intelligent systems. His representative works, published in journals such as IEEE/ASME Transactions on Mechatronics, Neurocomputing, and Robotic Intelligence and Automation, highlight innovative methods for control robustness and morphology learning in tendon-driven robotic arms. He has also co-invented multiple patents on artificial muscle devices and musculoskeletal control systems. Dr. Wu’s excellence has earned him recognition within the robotics research community, reflecting his growing influence and scholarly impact, with 148 citations by , 14 Documents, and an h-index of 6.
Profiles: Google scholar | Scopus | ORCID | ResearchGate
Featured Publications
Qiao, H., Wu, Y., Zhong, S., Yin, P., & Chen, J. (2023). Brain-inspired intelligent robotics: Theoretical analysis and systematic application. Machine Intelligence Research, 20(1), 1–18.Citations: 81
Wu, Y., Chen, J., & Qiao, H. (2021). Anti-interference analysis of bio-inspired musculoskeletal robotic system. Neurocomputing, 436, 114–125.Citations: 31
Chen, J., Wu, Y., Yao, C., & Huang, X. (2024). Robust motion learning for musculoskeletal robots based on a recurrent neural network and muscle synergies. IEEE Transactions on Automation Science and Engineering, 22, 2405–2420.Citations: 18
Chen, J., Wu, Y., & Qiao, H. (2024). Memory, attention, and muscle synergies based reinforcement and transfer learning for musculoskeletal robots under imperfect observation. IEEE/ASME Transactions on Mechatronics.Citations: 14
Fan, Y., Yuan, J., Wu, Y., & Qiao, H. (2023). A feedforward compensation approach for cable-driven musculoskeletal systems. Robotica, 41(4), 1221–1230.Citations: 10