Dr Yanlong Huang

Profile

I am a University Academic Fellow at the School of Computing, University of Leeds, with research topics mainly focusing on imitation learning, reinforcement learning, deep learning, and their applications to robotic systems.
I am an Associate Editor for IEEE Robotics and Automation Letters, an Associate Editor for IEEE International Conference on Intelligent Robots and Systems (2022, 2023), and an Associate Editor for Transactions of the Institute of Measurement and Control.
I received my Ph.D. degree from the Institute of Automation, Chinese Academy of Sciences. After that, I carried out my research as a postdoctoral researcher at Max-Planck Institute for Intelligent Systems and Italian Institute of Technology. I was a recipient of Marie Skłodowska-Curie Fellowship.

Prospective students

I am looking for highly self-motivated and passionate Ph.D. students who are interested in imitation learning or deep reinforcement learning. Please send me your CV, BSc, and MSc transcripts in your enquiry email. Visiting Ph.D. students are accepted.
You may come to visit our lab, but please make an appointment with one of the Ph.D. students.
An interesting block-stacking experiment carried out in our lab by Shaokang Wu and Qianyi Fu:

News & Updates

• New Paper: 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.
• New Paper: 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.
• New Paper: 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. 
• New Paper: 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. 
• New paper: Y. Huang, D. Xu, and M. Tan. On imitation learning of robot movement trajectories: A survey, Acta Automatica Sinica; 机器人运动轨迹的模仿学习综述, 自动化学报, 2022. 
• New paper: Y. Huang. EKMP: Generalized imitation learning with adaptation, nonlinear hard constraints and obstacle avoidance, arXiv:2103.00452, 2021.

Selected Publications

• 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

<h4>Research projects</h4> <p>Any research projects I'm currently working on will be listed below. Our list of all <a href="https://eps.leeds.ac.uk/dir/research-projects">research projects</a> allows you to view and search the full list of projects in the faculty.</p>

Student education

OCOM5205M (Robot Imitation Learning, 2022)

Current postgraduate researchers

• Dave Russell
• Shaokang Wu
• Qianyi Fu

<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>