Dr Ning Wang PhD, FBCS, FHEA
Associate Professor in Robotics and AI, Transforming Lives Fellow
Summary
Dr Ning Wang is an Associate Professor in Robotics and AI, and a Transforming Lives Fellow at the Department of Computing. She received the Ph.D. degree in Electronics Engineering at The Chinese University of Hong Kong in 2011.
Dr Wang’s research centres on robotics and intelligent learning, with a primary focus on human-machine interaction (HMI), embodied AI, and data analytics, aiming to develop systems that enable seamless collaboration between humans and machines, emphasizing adaptability, autonomy, and effective decision-making in dynamic environments, with applications on healthcare, autonomous driving, smart manufacturing, etc. She has been key member of EU FP7 Project ROBOT-ERA, EU Regional Development Funded Project ASTUTE2020 and industrial projects with UK companies, and has been awarded several awards including best application paper award of ICAC’24, best paper award of DISA'23, best student paper award of ICAC'23, IET premium award for best paper 2022, best paper award of ICIRA'15, best student paper award nomination of ISCSLP'10, and award of merit of 2008 IEEE Signal Processing Postgraduate Forum, etc.
Dr Wang is a Fellow of British Computer Society and a Fellow of the Higher Education Academy.
About
I am currently an Associate Professor in Robotics and AI, at 香蕉视频. Before that, I was a Senior Lecturer and interim leader of teleoperation group at the Bristol Robotics Laboratory. After getting my PhD, I worked as a research fellow on machine learning and big data at the Chinese University of Hong Kong. Then, I worked on elderly-robot interaction with the Centre for Robotics and Neural Systems, University of Plymouth, under the support of EU FP7 Project Robot-Era. I have been key member of EU Regional Development Funded Project ASTUTE 2020 and industrial projects with UK companies at Swansea University. I have built an academic career in robotics and machine intelligence, with a track record of two books and 80+ peer-reviewed papers published in flagship conferences and prestigious journals. I am serving as Associate Editor for the Elsevier International Journal of Advanced Robotic Systems and Frontiers in Computer Science. I was invited speakers to ROMAN’20 and EECR’23, in organizing committee of ICAC’22, ICIT’24 and EECR’25. I am regular grant reviewer for UKRI and NWO (Dutch 香蕉视频 Council).
My research seeks to address critical challenges at the intersection of robotics and intelligent systems, with applications in health & social care, industrial automation, and smart environments, etc. by offering solutions with human-like cognitive, social and physical capabilities in a machine. My research has been recognised in the field of human-robot interaction (HRI) and machine learning. The pioneering work of robot speech features for speaker recognition (Wang 2010, Wang 2011) contributing to social HRI technologies in adverse scenarios got nominated as best student paper award in ISCSLP’10. The innovative work on human feature extraction and data mining (Wang 2013, Wang 2015) funded by Hong Kong RGC General 香蕉视频 Fund presents a significant milestone in developing new tools and methodologies for healthcare, especially those with neurological diseases. The Electromyography (EMG) enhanced impedance and force control and muscle fatigue estimation for robot operation (Wang 2014, Wang 2018) suggests the application of human observations into robot control and physical HRI. The multi-modal user interface developed for service robots in large multidisciplinary collaborative project EU FP7 Robot-ERA has successfully delivered innovative robotic services for elderly people in the Europe (Di Nuovo 2018, Wang 2019). Collaborative work of human-robot sign language gesture interaction framework for social robots (Li 2023) attempts to break communication barrier for deaf-mute and hearing communities. The hybrid robotic force and motion learning research funded by the NSFC has attracted attention in the HRI research community (Wang 2021).
In my recent work, I have explored embodied AI in robotics, utilizing advanced techniques in deep neural networks learning and foundation models, e.g., large language models and vision-language models, to tackle robotic manipulation and navigation problems in real-world. These efforts have contributed to enhancing user experience of an AI-driven systems and improving their learning efficiency and performance. The work on imitation learning with transformer detection for end-to-end autonomous driving has shown leading driving performance in the world-renowned Carla Leader board, comparing to classification-based models (Chen 2023). This work has got the best paper award in DISA’23. My research team has investigated and successfully developed a deep multimodal imitation learning framework able to significantly improve the success rate of autonomous ultrasound scanning from 75% to 90% (Si 2025). Our work on integrating vision and learning-based method for dexterous grasping of soft fruits (Sampath 2024) has been awarded best application paper award of ICAC’24.
香蕉视频
- Communication and Computing 香蕉视频 Centre
- Industry and Innovation 香蕉视频 Institute
Royal Society International Exchanges grant (IES\R2\232111, with Hong Kong), " Motion Learning for Microrobotic Control", as Co-I, Royal Society, £29,301.80, 2023 - 2025.
Key Publications:
- Z. Lu, Z. Zhao, T. Yue, X. Zhu, N. Wang, “A bio-inspired multi-functional tendon-driven tactile sensor and application in obstacle avoidance using reinforcement learning,” IEEE Transactions on Cognitive and Developmental Systems, Vol. 16, No. 2, pp. 407-415, 2024.
- J. Li, J. Zhong, N. Wang, “A multimodal human-robot sign language interaction framework applied in social robots,” Frontiers in Neuroscience, No. 17, Article 1168888, 2023.
- Z. Lu, N. Wang, “Biomimetic force and impedance adaptation based on broad learning system in stable and unstable tasks: Creating an incremental and explainable neural network with functional linkage,” IEEE Robotics & Automation Magazine, Vol. 29, No. 4, pp. 66-77, 2022.
- N. Wang, C. Chen, A. Di Nuovo, “A framework of hybrid force/motion skills learning for robots”, IEEE Transactions on Cognitive and Developmental Systems, Vol. 13, No. 1, pp. 162-170, 2021.
- N. Wang, C. Chen, C. Yang, “Robot learning framework based on adaptive admittance control and generalizable motion modeling with neural network controller,” Neurocomputing, Vol. 390, pp. 260-267, 2020.
- N. Wang, A. D. Nuovo, A. Cangelosi, and R. Jones, “Temporal patterns in multi-modal social interaction between elderly users and service,” Interaction Studies, Vol. 20, No. 1, pp. 4-24, 2019.
- N. Wang and M. R. Lyu, “Extracting and selecting distinctive EEG features for efficient epileptic seizure prediction,” IEEE Journal of Biomedical and Health Informatics, Vol. 19, No. 5, pp. 1648 - 1659, 2015.
- N. Wang, P. C. Ching, N. Zheng and T. Lee, “Robust speaker recognition using denoised vocal source and vocal tract features,” IEEE Transactions on Audio, Speech and Language Processing, Vol. 19, No. 1, pp. 196-205, 2011.
Books:
- C. Yang, Z. Lu, and N. Wang, Robot Dexterous Manipulation: from Teleoperation to Autonomous Learning and Control, Springer, ISBN: 978-3-031-78500-9, 2025.
- C. Yang, J. Luo, and N. Wang, Human-in-the-loop Learning and Control for Robot Teleoperation, Elsevier, ISBN: 9780323951432, 2023.
Publications
Journal articles
Kadalagere Sampath, S., Wang, N., Yang, C., Wu, H., Liu, C., & Pearson, M. (2025). . Applied Sciences, 15 (5).
Lu, Z., Si, W., Wang, N., & Yang, C. (2024). Dynamic Movement Primitives-Based Human Action Prediction and Shared Control for Bilateral Robot Teleoperation. IEEE Transactions on Industrial Electronics, 71 (12), 16654-16663.
Lu, Z., Wang, N., Si, W., & Yang, C. (2024). Distributed Observer-Based Prescribed Performance Control for Multi-Robot Deformable Object Cooperative Teleoperation. IEEE Transactions on Automation Science and Engineering, 21 (3), 4143-4154.
Lu, Z., Zhao, Z., Yue, T., Zhu, X., & Wang, N. (2024). A Bioinspired Multifunctional Tendon-Driven Tactile Sensor and Application in Obstacle Avoidance Using Reinforcement Learning. IEEE Transactions on Cognitive and Developmental Systems, 16 (2), 407-415.
Lu, Z., Wang, N., & Yang, C. (2024). . IEEE Transactions on Automation Science and Engineering, 22, 1748-1763.
Liu, H., Sampath, S.K., Wang, N., & Yang, C. (2024). . IEEE/ASME Transactions on Mechatronics, 29 (5), 3522-3533.
Si, W., Wang, N., & Yang, C. (2024). . IEEE Transactions on Automation Science and Engineering, 22, 317-327.
Lu, Z., & Wang, N. (2022). Biomimetic Force and Impedance Adaptation Based on Broad Learning System in Stable and Unstable Tasks: Creating an Incremental and Explainable Neural Network With Functional Linkage. IEEE Robotics & Automation Magazine, 29 (4), 66-77.
Lu, Z., Wang, N., & Shi, D. (2022). DMPs-based skill learning for redundant dual-arm robotic synchronized cooperative manipulation. Complex & Intelligent Systems, 8 (4), 2873-2882.
Si, W., Guan, Y., & Wang, N. (2022). Adaptive Compliant Skill Learning for Contact-Rich Manipulation With Human in the Loop. IEEE Robotics and Automation Letters, 7 (3), 5834-5841.
Lu, Z., Wang, N., Li, M., & Yang, C. (2022). Incremental Motor Skill Learning and Generalization From Human Dynamic Reactions Based on Dynamic Movement Primitives and Fuzzy Logic System. IEEE Transactions on Fuzzy Systems, 30 (6), 1506-1515.
Lu, Z., Guan, Y., & Wang, N. (2022). An Adaptive Fuzzy Control for Human-in-the-Loop Operations With Varying Communication Time Delays. IEEE Robotics and Automation Letters, 7 (2), 5599-5606.
Zeng, C., Li, Y., Guo, J., Huang, Z., Wang, N., & Yang, C. (2022). A Unified Parametric Representation for Robotic Compliant Skills With Adaptation of Impedance and Force. IEEE/ASME Transactions on Mechatronics, 27 (2), 623-633.
Xue, X., Zuo, L., & Wang, N. (2022). A Robot Human鈥怢ike Learning Framework Applied to Unknown Environment Interaction. Complexity, 2022 (1).
Lu, Z., Wang, N., & Yang, C. (2021). A Constrained DMPs Framework for Robot Skills Learning and Generalization From Human Demonstrations. IEEE/ASME Transactions on Mechatronics, 26 (6), 3265-3275.
Chen, X., Wang, N., Cheng, H., & Yang, C. (2020). Neural Learning Enhanced Variable Admittance Control for Human鈥揜obot Collaboration. IEEE Access, 8, 25727-25737.
Wang, N., Chen, C., & Di Nuovo, A. (2020). . IEEE Transactions on Cognitive and Developmental Systems, 1.
Wang, N., Di Nuovo, A., Cangelosi, A., & Jones, R. (2019). . Interaction Studies, 4-24.
Yang, C., Chen, C., Wang, N., Ju, Z., Fu, J., & Wang, M. (2019). Biologically Inspired Motion Modeling and Neural Control for Robot Learning From Demonstrations. IEEE Transactions on Cognitive and Developmental Systems, 11 (2), 281-291.
Luo, J., Yang, C., Wang, N., & Wang, M. (2019). Enhanced teleoperation performance using hybrid control and virtual fixture. International Journal of Systems Science, 50 (3), 451-462.
Yang, C., Zeng, C., Cong, Y., Wang, N., & Wang, M. (2019). A Learning Framework of Adaptive Manipulative Skills From Human to Robot. IEEE Transactions on Industrial Informatics, 15 (2), 1153-1161.
Wang, N., Xu, Y., Ma, H., & Liu, X. (2018). Exploration of Muscle Fatigue Effects in Bioinspired Robot Learning from sEMG Signals. Complexity, 2018 (1).
Di Nuovo, A., Broz, F., Wang, N., Belpaeme, T., Cangelosi, A., Jones, R., ... Dario, P. (2017). . Intelligent Service Robotics, 11 (1), 109-126.
Conference papers
Di Nuovo, A., Wang, N., Broz, F., Belpaeme, T., Jones, R., & Cangelosi, A. (2016). . In Alboul, L., Damien, D., & Aitken, J.M. (Eds.) Towards autonomous robotic systems, 17th Annual Conference, TAROS 2016, Sheffield, UK, June 26--July 1, 2016, Proceedings, (pp. 87-98). Springer International Publishing:
Other activities
Associate Editor:
International Journal of Advanced Robotic Systems (Elsevier), 2021 - Present
Frontiers in Computer Science (Frontiers), 2023 - Present
Session chair: ICSPCC 2012, ICAC 2022, ICM 2023, ICRA 2023, ICAC 2023.
Organising committee:
Local Chair ICAC 2022, Award Chair ICIT 2024, Publicity Chair EECR 2025.
Invited talk: ROMAN 2020, EECR 2023.
Grant proposal reviewer: UKRI, Dutch 香蕉视频 Council.
Paper reviewer: Nature Communications (Nature), Scientific Reports (Nature).
Postgraduate supervision
Had 1 PhD and 30+ master projects completed.