• 2012-2016: South China University of Technology, Computer Science and Technology, Bachelor Degree
• 2012-2016: Simon Fraser University, Computer Science, Doctoral Degree

Work Experience：

• 2020-2022: Cheriton School of Computer Science - University of Waterloo, PostDoc
• 2020-2022: Vector Institute, Researcher (Joint)
• 2022-now: School of Data Science, Chinese University of Hong Kong, Shenzhen, Assistant Professor
• 2025-now: Shenzhen Loop Area Institute, Assistant Professor (Joint Appointment with CUHKSZ)

Dr. Guiliang Liu is an Assistant Professor at the School of Data Science, The Chinese University of Hong Kong, Shenzhen. He earned his Ph.D. in Computing Science from Simon Fraser University, Canada, and later conducted postdoctoral research at both the University of Waterloo and the Vector Institute in Canada. Dr. Liu’s research centers on reinforcement learning and embodied intelligent decision-making. He has pioneered the development of data engines for embodied intelligence, facilitating the generation and deployment of robotic manipulation skills through sim-to-real generalization. He has also introduced inverse constrained reinforcement learning models to improve the safety and stability of reinforcement learning control systems. In addition to his academic roles, Dr. Liu serves as the Scientist for Reinforcement Learning at DexForce, the Director of the Embodied Decision Making (Edem) lab, as well as a Research Fellow at the Shenzhen Loop Area Institute. He has authored over 30 papers in leading international machine learning conferences and journals, including NeurIPS, ICML, and ICLR. Dr. Liu is currently an Area Chair for NeurIPS and ICLR and has been recognized with several prestigious accolades.

1. Qiao, G., Lin, S., Zuo, R., Wu, Z., Jia, K., & Liu, Guiliang*. (2026). SignBot: Learning Human-to-Humanoid Sign Language Interaction. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA).
2. Zhao, R., Xu, S., Jin, R., Deng, Y., Tai, Y., Jia, K., & Liu, Guiliang*. (2026). Sim2Real VLA: Zero-Shot Generalization of Synthesized Skills to Realistic Manipulation. In Proceedings of the International Conference on Learning Representations (ICLR).
3. Lin, S., Qiao, G., Tai, Y., Li, A., Jia, K., & Liu, Guiliang*. (2026). HWC-Loco: A Hierarchical Whole-Body Control Approach to Robust Humanoid Locomotion. In Proceedings of the International Conference on Learning Representations (ICLR).
4. Miao, R., Xu, S., Zhao, R., Chan, W. K. V., & Liu, Guiliang*. (2025). Uncertainty-Aware Preference Alignment for Diffusion Policies. In Proceedings of the Conference on Neural Information Processing Systems (NeurIPS).
5. Liu, Guiliang#, Deng, Y.#, Zhao, R., Zhou, H., Chen, J., Chen, J., Xu, R., Tai, Y., & Jia, K. (2025). DexScale: Automating Data Scaling for Sim2Real Generalizable Robot Control. In Proceedings of the International Conference on Machine Learning (ICML).
6. Yue, B., Li, J., & Liu, Guiliang*. (2025). Provably Efficient Exploration in Inverse Constrained Reinforcement Learning. In Proceedings of the International Conference on Machine Learning (ICML).
7. Ruan, Q., Lei, J., Yuan, W., Zhang, Y., Lu, D., Liu, Guiliang, & Jia, K.. (2025). Prof. Robot: Differentiable Robot Rendering Without Static and Self-Collisions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
8. Xu, S., Yue, B., Zha, H., & Liu, Guiliang*. (2025). A Distributional Approach to Uncertainty-Aware Preference Alignment Using Offline Demonstrations. In Proceedings of the International Conference on Learning Representations (ICLR).
9. Zhao, R., Xu, S., Yue, B., & Liu, Guiliang*. (2025). Toward Exploratory Inverse Constraint Inference with Generative Diffusion Verifiers. In Proceedings of the International Conference on Learning Representations (ICLR).
10. Yue, B., Wang, S., Gaurav, A., Li, J., Poupart, P., & Liu, Guiliang*. (2025). Understanding Constraint Inference in Safety-Critical Inverse Reinforcement Learning. In Proceedings of the International Conference on Learning Representations (ICLR).
11. Qiao, G., Quan, G., Qu, R., & Liu, Guiliang*. (2024). Modelling Competitive Behaviors in Autonomous Driving Under Generative World Model. In Proceedings of the European Conference on Computer Vision (ECCV).
12. Quan, G., Xu, Z., & Liu, Guiliang*. (2024). Learning Constraints from Offline Demonstrations via Superior Distribution Correction Estimation. In Proceedings of the International Conference on Machine Learning (ICML).
13. Xu, S., & Liu, Guiliang*. (2024). Robust Inverse Constrained Reinforcement Learning under Model Misspecification. In Proceedings of the International Conference on Machine Learning (ICML).
14. Xu, S., & Liu, Guiliang*. (2024). Uncertainty-Aware Constraint Inference in Inverse Constrained Reinforcement Learning. In Proceedings of the International Conference on Learning Representations (ICLR).
15. Qiao, G., Liu, Guiliang*, Poupart, P., & Xu, Z. (2023). Multi-Modal Inverse Constrained Reinforcement Learning from a Mixture of Demonstrations. In Proceedings of the Conference on Neural Information Processing Systems (NeurIPS).
16. Liu, Guiliang, Luo, Y., Gaurav, A., Rezaee, K., & Poupart, P. (2023). Benchmarking Constraint Inference in Inverse Reinforcement Learning. In Proceedings of the International Conference on Learning Representations (ICLR).
17. Liu, Guiliang, Luo, Y., Schulte, O., & Poupart, P. (2022). Uncertainty-Aware Reinforcement Learning for Risk-Sensitive Player Evaluation in Sports Game. In Proceedings of the Conference on Neural Information Processing Systems (NeurIPS).
18. Liu, Guiliang, Adhikari, A., Farahmand, A.-M., & Poupart, P. (2022). Learning Object-Oriented Dynamics for Planning from Text. In Proceedings of the International Conference on Learning Representations (ICLR).
19. Luo, Y., Liu, Guiliang, Duan, H., Schulte, O., & Poupart, P. (2022). Distributional Reinforcement Learning with Monotonic Splines. In Proceedings of the International Conference on Learning Representations (ICLR).
20. Liu, Guiliang, Sun, X., Schulte, O., & Poupart, P. (2021). Learning Tree Interpretation from Object Representation for Deep Reinforcement Learning. In Proceedings of the Conference on Neural Information Processing Systems (NeurIPS).
21. Liu, Guiliang, Schulte, O., Poupart, P., Rudd, M., & Javan, M. (2020). Learning Agent Representations for Ice Hockey. In Proceedings of the Conference on Neural Information Processing Systems (NeurIPS).
22. Liu, Guiliang, Li, X., Sun, M., & Li, P. (2020). An Advantage Actor-Critic Algorithm with Confidence Exploration for Open Information Extraction. In Proceedings of the SIAM International Conference on Data Mining (SDM).
23. Liu, Guiliang, Li, X., Wang, J., Sun, M., & Li, P. (2020). Extracting Knowledge from Web Text with Monte Carlo Tree Search. In Proceedings of the Web Conference (WWW).
24. Liu, Guiliang, & Schulte, O. (2018). Deep Reinforcement Learning in Ice Hockey for Context-Aware Player Evaluation. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI).
25. Liu, Guiliang, Schulte, O., & Zhu, W. (2018). Toward Interpretable Deep Reinforcement Learning with Linear Model U-Trees. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD).