Dr Yanlong Huang

Profile

I am a university academic fellow at the school of computing, University of Leeds. My research mainly focuses on imitation learning, optimal control, reinforcement learning, motion planning and their applications to robotic systems. I received my Ph.D. degree from Institute of Automation, Chinese Academy of Sciences, Beijing, China. After that, I carried out my research as a postdoctoral researcher in Max-Planck Institute for Intelligent Systems and Italian Institute of Technology.

News & Updates

• New paper: 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. 
• New paper: 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), Las Vegas, USA, 2020. 
• New paper: Y. Huang and D. G. Caldwell. A linearly constrained nonparametric framework for imitation learning, IEEE International Conference on Robotics and Automation (ICRA), Paris, France, 2020.

Selected Publications

• 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]
• A. Duan, R. Camoriano, D. Ferigo, Y. Huang, D. Calandriello, L. Rosasco, D. Pucci. Learning to sequence multiple tasks with competing constraints, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2019.
• 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

I have been focusing on developing generic approaches for robot skill learning and optimization. Specifically, my research topics comprise imitation learning, optimal control, reinforcement learning and motion planning. Please see part of my research videos as follows.

<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/665-constrained-robot-skill-learning-from-few-examples">Constrained Robot Skill Learning From Few Examples</a></li>