• 2012-2016：华南理工大学 计算机科学与技术 学士学位
• 2016-2020：加拿大西蒙弗雷泽大学 计算机科学学院 博士学位

工作经历：

• 2020-2022：滑铁卢大学，博士后研究员
• 2020-2022：Vector Institute，研究员（兼职）
• 2022-至今：香港中文大学（深圳），助理教授
• 2025-至今：深圳河套学院 助理教授，（双聘）

刘桂良博士现任香港中文大学（深圳）数据科学学院助理教授。他在加拿大西蒙弗雷泽大学（Simon Fraser University）计算机科学学院获得博士学位，并在加拿大滑铁卢大学（University of Waterloo）及向量研究院（Vector Institute）完成博士后研究。刘桂良教授研究主要聚焦于具身智能决策与强化学习，研发了EmbodiedChain具身智能数据引擎，实现虚实迁移下的机器人操作技能生成和部署；并提出了逆约束推断强化学习模型，致力于提升强化学习控制模型的安全性和稳定性。他同时兼任跨维智能强化学习技术专家、学院具身决策实验室负责人以及河套学院研究员。刘桂良教授在包括NeurIPS、ICML、ICLR在内的国际机器学习顶级学术会议和期刊上发表论文30余篇，担任NeurIPS、ICLR领域主席。

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).