Dr Yanlong Huang

Position: Associate Professor
Areas of expertise: Imitation learning; Reinforcement learning.
Email: Y.L.Huang@leeds.ac.uk
Phone: +44(0)113 343 3505
Location: 2.21C Sir William Henry Bragg Building
Website: GitHub | Googlescholar | Researchgate

Profile

I am an Associate Professor at the School of Computer Science. I am an Associate Editor for

IEEE Robotics and Automation Letters
IEEE Transactions on Automation Science and Engineering
IEEE International Conference on Intelligent Robots and Systems (2022 - 2024)

Prospective students

I am looking for highly self-motivated and passionate PhD students who are interested in the following topics: imitation learning, deep reinforcement learning, computer vision, object detection and manipulation, task and motion planning, optimal control.
Application: send me your CV, BSc, and MSc transcripts in your enquiry email. You may visit our lab, but please make an appointment with one of my PhD students.

Selected Publications

S. Wu, Y. Wang, and Y. Huang. Auto-LfD: closing the loop for learning from demonstrations, IEEE Transactions on Automation Science and Engineering (T-ASE), 2025. [video]
J. Ding, C. Santina, T. Lam, J. Pang, X. Xiao, N. Tsagarakis, and Y. Huang. Robust humanoid locomotion via sequential stepping and angular momentum optimization, IEEE Transactions on Industrial Electronics (T-IE), 2024.
J. Silvério and Y. Huang. A non-parametric skill representation with soft null space projectors for fast generalization, IEEE International Conference on Robotics and Automation (ICRA), 2023.
P. Xu, L. Ding, Z. Li, J. Shi, H. Gao, G. Liu, and Y. Huang. A closed-loop shared control framework for legged robots, IEEE Transactions on Mechatronics (T-MECH), 2023.
P. Xu, L. Ding, Z. Li, H. Yang, Z. Wang, H. Gao, R. Zhou, Y. Su, Z. Deng, and Y. Huang. Learning physical characteristics like animals for legged robots, National Science Review (NSR), 2023.
J. Ding, T. Lam, L. Ge, J. Pang, and Y. Huang. Safe and adaptive 3D locomotion via constrained task space imitation learning, IEEE Transactions on Mechatronics (T-MECH), 2023.
Y. Huang, D. Xu, and M. Tan. On imitation learning of robot movement trajectories: A survey, Acta Automatica Sinica, 2022.
F. J. Abu-Dakka, Y. Huang, J. Silvério, and V. Kyrki. A probabilistic framework for learning geometry-based robot manipulation skills, Robotics and Autonomous Systems (RAS), 2021. [video]
Y. Huang, F. J. Abu-Dakka, J. Silvério, and D. G. Caldwell. Towards orientation learning and adaptation in Cartesian space, IEEE Transactions on Robotics (T-RO), 2020. [page]
J. Ding, X. Xiao, N. Tsagarakis, and Y. Huang. Robust gait synthesis combining constrained optimization and imitation learning, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020.
Y. Huang and D. G. Caldwell. A linearly constrained nonparametric framework for imitation learning, IEEE International Conference on Robotics and Automation (ICRA), 2020. [video]
J. Silvério, Y. Huang, F. J. Abu-Dakka, L. Rozo, D. G. Caldwell. Uncertainty aware imitation learning using kernelized movement primitives, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2019. [video][code]
Y. Huang, L. Rozo, J. Silvério, and D. G. Caldwell. Kernelized movement primitives, The International Journal of Robotics Research (IJRR), 2019. [video][code]
Y. Huang, F. J. Abu-Dakka, J. Silvério, and D. G. Caldwell. Generalized orientation learning in robot task space, IEEE International Conference on Robotics and Automation (ICRA), 2019. [video][code]
Y. Huang, L. Rozo, J. Silvério, and D. G. Caldwell. Non-parametric imitation learning of robot motor skills, IEEE International Conference on Robotics and Automation (ICRA), 2019. [code]
Y. Huang, J. Silvério, and D. G. Caldwell. Towards minimal intervention control with competing constraints, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2018.
J. Silvério, Y. Huang, L. Rozo, and D. G. Caldwell. An uncertainty-aware minimal intervention control strategy learned from demonstrations, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2018. [video]
J. Silvério, Y. Huang, L. Rozo, S. Calinon, and D. G. Caldwell. Probabilistic learning of torque controllers from kinematic and force constraints, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2018. [video]
Y. Huang, J. Silvério, L. Rozo, and D. G. Caldwell. Generalized task-parameterized skill learning, IEEE International Conference on Robotics and Automation (ICRA), 2018. [video]
Y. Huang, J. Silvério, L. Rozo, and D. G. Caldwell. Hybrid probabilistic trajectory optimization using null-space exploration, IEEE International Conference on Robotics and Automation (ICRA), 2018. [video]
Y. Huang, D. Buchler, O. Koc, B. Scholkopf, and J. Peters. Jointly learning trajectory generation and hitting point prediction in robot table tennis, IEEE International Conference on Humanoid Robots (HUMANOIDS), 2016. [Video]
Y. Huang, B. Scholkopf, and J. Peters. Learning optimal striking points for a ping-pong playing robot, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2015.
Y. Huang, D. Xu, M. Tan, and H. Su. Adding active learning to LWR for ping-pong playing robot, IEEE Transactions on Control Systems Technology (TCST), 2013. [video]
Y. Huang, D. Xu, M. Tan, and H. Su. Trajectory prediction of spinning ball for ping-pong player robot, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2011.

Research interests

Student education

COMP5611M Machine Learning (Module Leader)
COMP3631 Intelligent Systems and Robotics (Module Leader)
COMP3611 Machine Learning
COMP5200M MSc Project
COMP3931/3932 Individual Project
MECH 3895 Individual Project

Current postgraduate researchers

<h4>Postgraduate research opportunities</h4> <p>We welcome enquiries from motivated and qualified applicants from all around the world who are interested in PhD study. Our <a href="https://phd.leeds.ac.uk">research opportunities</a> allow you to search for projects and scholarships.</p>

Projects

<li><a href="//phd.leeds.ac.uk/project/1633-meta-learning-from-watching-videos">Meta learning from watching videos</a></li> <li><a href="//phd.leeds.ac.uk/project/1087-robot-skill-learning-using-deep-reinforcement-learning">Robot Skill Learning Using Deep Reinforcement Learning</a></li>