吳飛揚 (Wú Fēi Yáng), "woo-FAY-yahng" 🔊
ML PhD student at Georgia Tech
My research interests lie in the intersection of optimization, reinforcement learning, and robotics. I develop algorithms to efficiently train robots, ideally with theoretical guarantee.
I am co-advised by Prof. Anqi Wu and Prof. Ye Zhao at Georgia Tech. Additionally, I had the privilege to collaborate with Prof. George Lan on stochastic optimization & RL.
Previously, I contributed to the development team of optimization solvers for large-scale linear programming problems at Chinese University of Hong Kong, Shenzhen (CUHKSZ), where I spent my undergrad years in computer science and engineering.
[Sep 2025] Our work on IRL with reward distributions is released on arxiv.
[Sep 2025] Two co authored papers submitted to ICRA.
[Aug 2025] Our paper L2T got accepted at RA-L. Check out here.
[May 2025] I passed my qualifying exam.
[May 2025] One paper accpeted to ICML 2025. Congrats Jingyang!
[April 2025] One paper submitted to RA-L! Checkout here: https://lidar-learn-to-teach.github.io.
[Sep 2024] Visitng student Xavier Nal finished his master degree project in our lab. See our results here.
Official submission coming soon. Initial version available online.
[May 2024] I interned at Georgia Tech Research Institute on generalizing diffusion policies on robotics manipulation tasks.
[Dec 2023] Our paper on using Inverse Reinforcement Learning accepted in ICRA 2024.
[Oct 2023] Our paper on average reward accepted as poster in Neurips 2023.
Distributional Inverse Reinforcement Learning. 2025. preprint
Feiyang Wu, Ye Zhao, Anqi Wu
RL-augmented Adaptive Model Predictive Control for Bipedal Locomotion over Challenging Terrain. 2025. preprint
Junnosuke Kamohara, Feiyang Wu, Chinmayee Wamorkar, Seth Hutchinson, Ye Zhao
SEEC: Stable End-Effector Control with Model-Enhanced Residual Learning for Humanoid Loco-Manipulation. 2025. preprint
Jaehwi Jang, Zhuoheng Wang, Ziyi Zhou, Feiyang Wu, Ye Zhao
Jingyang Ke, Feiyang Wu, Jiyi Wang, Zhaoyuan Gu, Jeffrey Markowitz, Anqi Wu
Learn to Teach: Improve Sample Efficiency in Teacher-student Learning for Sim-to-Real Transfer. 2025. RA-L.
Feiyang Wu, Xavier Nal, Zhaoyuan Gu, Ye Zhao, Anqi Wu
Stochastic first-order methods for average-reward Markov decision processes, 2024, Mathematics of Operation Research.
Tianjiao Li, Feiyang Wu, Guanghui Lan
Infer and Adapt: Bipedal Locomotion Reward Learning from Demonstrations via Inverse Reinforcement Learning. Sept 2023, ICRA 2024.
Feiyang Wu, Zhaoyuan Gu, Hanran Wu, Anqi Wu, Ye Zhao
Inverse Reinforcement Learning with the Average Reward Criterion, NeurIPS 2023
Feiyang Wu, Jingyang Ke, Anqi Wu
SAniHead: Sketching Animal-Like 3D Character Heads Using a View-Surface Collaborative Mesh Generative Network, 2020, IEEE Transactions on Visualization and Computer Graphics (TVCG).
Dong Du, Xiaoguang Han, Hongbo Fu, Feiyang Wu, Yizhou Yu, Shuguang Cui, Ligang Liu