Selected Publications

* indicates the corresponding author. # indicates co-first author.

Year 2026

• From Reaction to Anticipation: Proactive Failure Recovery through Agentic Task Graph for Robotic Manipulation
  Sheng Xu, Ruixing Jin, Huayi Zhou, Bo Yue, Guanren Qiao, Yunxin Tai, Yueci Deng, Kui Jia, Guiliang Liu*
  Robotics: Science and Systems (RSS) 2026.
  [Paper (PDF)],
• DyGRO-VLA: Cross-Task Scaling of Vision Language Action Models via Dynamic Grouped Residual Optimization
  Sixu Lin, Yunpeng Qing, Litao Liu, Ming Zhou, Ruixing Jin, Xiaoyi Fan, Guiliang Liu*
  International Conference on Machine Learning (ICML) 2026.
  [Paper (PDF)],
• RoboFlow4D: A Lightweight Flow World Model Toward Real-Time Flow-Guided Robotic Manipulation
  Sixu Lin, Huaiyuan Xu, Junliang Chen, Zhuohao Li, Guangming Wang, Yixiong Jing, Sheng Xu, Runyi Zhao, Brian Sheil, Lap-Pui Chau, Guiliang Liu*
  International Conference on Machine Learning (ICML) 2026.
  [Paper (PDF)],
• Focus-Then-Contact: Speeding Up Robotic Contact-Rich Task Learning with Affordance-Guided Real-World Residual Reinforcement Learning
  Guanren Qiao, Ruixiang Ouyang, Sheng Xu, Ruixing Jin, Yueci Deng, Yunxin Tai, Kui Jia, Guiliang Liu*
  International Conference on Machine Learning (ICML) 2026.
  [Paper (PDF)],
• YOTO++: Learning Long-Horizon Closed-Loop Bimanual Manipulation from One-Shot Human Video Demonstrations
  Huayi Zhou, Ruixiang Wang, Yunxin Tai, Yueci Deng, Guiliang Liu, Kui Jia
  IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI).
  [Paper (PDF)],
• HoopEval: Individual Player Action Evaluation via Deep Reinforcement Learning
  Xing Wang, Yu Fu, Sheng Xu, Konstantinos Pelechrinis, Mingxin Zhang, Miguel Angel Gomez Ruano, Guiliang Liu, Shaoliang Zhang
  MIT Sloan Sports Analytics Conference (SSAC).
  [Paper (PDF)],
• CycleManip: Enabling Cycle-based Manipulation via Effective History Perception and Understanding
  Yi-Lin Wei, Haoran Liao, Yuhao Lin, Pengyue Wang, Zhizhao Liang, Guiliang Liu, Wei-Shi Zheng
  IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2026.
  [Paper (PDF)],
• SignBot: Learning Human-to-Humanoid Sign Language Interaction
  Guanren Qiao, Sixu Lin, Ronglai Zuo, Zhizheng Wu, Kui Jia, Guiliang Liu*
  IEEE International Conference on Robotics & Automation (ICRA) 2026.
  [Paper (PDF)],
• Sim2Real VLA: Zero-Shot Generalization of Synthesized Skills to Realistic Manipulation
  Runyi Zhao, Sheng Xu, Ruixing Jin, Yueci Deng, Yunxin Tai, Kui Jia, Guiliang Liu*
  International Conference on Learning Representations (ICLR) 2026.
  [Paper (PDF)],
• HWC-Loco: A Hierarchical Whole-Body Control Approach to Robust Humanoid Locomotion
  Sixu Lin, Guanren Qiao, Yunxin Tai, Ang Li, Kui Jia, Guiliang Liu*
  International Conference on Learning Representations (ICLR) 2026.
  [Paper (PDF)],

Year 2025

• Uncertainty-aware Preference Alignment for Diffusion Policies
  Runqing Miao, Sheng Xu, Runyi Zhao, Wai Kin Victor Chan, Guiliang Liu*
  Advances in Neural Information Processing Systems (NeurIPS) 2025.
  [Paper (PDF)],
• Offline inverse constrained reinforcement learning for safe-critical decision making in healthcare
  Nan Fang, Guiliang Liu, Wei Gong
  IEEE Transactions on Artificial Intelligence 2025.
  [Paper (PDF)],
• GAT-Grasp: Gesture-Driven Affordance Transfer for Task-Aware Robotic Grasping
  Ruixiang Wang, Huayi Zhou, Xinyue Yao, Guiliang Liu, Kui Jia.
  IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2025.
  [Paper (PDF)],
• DexScale: Automating Data Scaling for Sim2Real Generalizable Robot Control
  Guiliang Liu#, Yueci Deng#, Runyi Zhao, Huayi Zhou, Jian Chen, Jietao Chen, Ruiyan Xu, Yunxin Tai, Kui Jia.
  International Conference on Machine Learning (ICML) 2025.
  [Paper (PDF)],
• Provably Efficient Exploration in Inverse Constrained Reinforcement Learning
  Bo Yue, Jian Li, Guiliang Liu*.
  International Conference on Machine Learning (ICML) 2025.
  [Paper (PDF)],
• Unimodal Training-Multimodal Prediction:Cross-modal Federated Learning with Hierarchical Aggregation
  Rongyu Zhang, Xiaowei Chi, Guiliang Liu, Wenyi Zhang, Yuan Du, Fangxin Wang.
  Transactions on Mobile Computing (TMC) 2025.
  [Paper (PDF)],
• You Only Teach Once: Learn One-Shot Bimanual Robotic Manipulation from Video Demonstrations
  Huayi Zhou, Ruixiang Wang, Yunxin Tai, Yueci Deng, Guiliang Liu, Kui Jia.
  Robotics: Science and Systems (RSS) 2025.
  [Paper (PDF)], [Code],
• Prof. Robot: Differentiable Robot Rendering Without Static and Self-Collisions
  Quanyuan Ruan, Jiabao Lei, Wenhao Yuan, Yanglin Zhang, Dekun Lu, Guiliang Liu*, Kui Jia*.
  IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2025.
  [Paper (PDF)],
• A Distributional Approach to Uncertainty-Aware Preference Alignment Using Offline Demonstrations
  Sheng Xu, Bo Yue, Hongyuan Zha, Guiliang Liu*.
  International Conference on Learning Representations (ICLR) 2025.
  [Paper (PDF)],
• Toward Exploratory Inverse Constraint Inference with Generative Diffusion Verifiers
  Runyi Zhao, Sheng Xu, Bo Yue, Guiliang Liu*.
  International Conference on Learning Representations (ICLR) 2025.
  [Paper (PDF)],
• Understanding Constraint Inference in Safety-Critical Inverse Reinforcement Learning
  Bo Yue, Shufan Wang, Ashish Gaurav, Jian Li, Pascal Poupart, Guiliang Liu*.
  International Conference on Learning Representations (ICLR) 2025.
  [Paper (PDF)],

Year 2024

• A Comprehensive Survey on Inverse Constrained Reinforcement Learning: Definitions, Progress and Challenges
  Guiliang Liu, Sheng Xu, Shicheng Liu, Ashish Gaurav, Sriram Ganapathi Subramanian, Pascal Poupart
  Transactions on Machine Learning Research (TMLR) 2024.
  [Paper (PDF)]
• Modelling Competitive Behaviors in Autonomous Driving Under Generative World Model
  Guanren Qiao, Guorui Quan, Rongxiao Qu, Guiliang Liu*
  European Conference on Computer Vision (ECCV) 2024.
  [Paper (PDF)], [Code],
• Learning Constraints from Offline Demonstrations via Superior Distribution Correction Estimation
  Guorui Quan, zhiqiang xu, Guiliang Liu*.
  International Conference on Machine Learning (ICML) 2024.
  [Paper (PDF)], [Code],
• Robust Inverse Constrained Reinforcement Learning under Model Misspecification
  Sheng Xu, Guiliang Liu*.
  International Conference on Machine Learning (ICML) 2024.
  [Paper (PDF)], [Code],
• Confidence Aware Inverse Constrained Reinforcement Learning
  Sriram Ganapathi Subramanian, Guiliang Liu, Mohammed Elmahgiubi, Kasra Rezaee, Pascal Poupart.
  International Conference on Machine Learning (ICML) 2024.
  [Paper (PDF)], [Code],
• Uncertainty-aware Constraint Inference in Inverse Constrained Reinforcement Learning
  Sheng Xu, Guiliang Liu*.
  International Conference on Learning Representations (ICLR) 2024.
  [Paper (PDF)], [Code],
• Risk, Reward, and Reinforcement Learning in Hockey Analytics
  Sheng Xu, Oliver Schulte, Yudong Luo, Pascal Poupart, Guiliang Liu.
  Invited Book Chapter in Artificial intelligence and machine learning in sports science 2024.
  [Book],

Year 2023

• Multi-Modal Inverse Constrained Reinforcement Learning from a Mixture of Demonstrations
  Guanren Qiao, Guiliang Liu*, Pascal Poupart, Zhiqiang Xu.
  Advances in Neural Information Processing Systems (NeurIPS) 2023.
  [Paper (PDF)], [Code],
• An Alternative to Variance: Gini Deviation for Risk-averse Policy Gradient.
  Yudong Luo, Guiliang Liu, Pascal Poupart, Yangchen Pan.
  Advances in Neural Information Processing Systems (NeurIPS) 2023.
  [Paper (PDF)], [Slides], [Code],
• Benchmarking Constraint Inference in Inverse Reinforcement Learning.
  Guiliang Liu, Yudong Luo, Ashish Gaurav, Kasra Rezaee, Pascal Poupart.
  International Conference on Learning Representations (ICLR) 2023.
  [Paper (PDF)], [Slides], [Code],
• Learning Soft Constraints From Constrained Expert Demonstrations.
  Ashish Gaurav, Kasra Rezaee, Guiliang Liu, Pascal Poupart.
  International Conference on Learning Representations (ICLR) 2023.
  [Paper (PDF)],
• NTS-NOTEARS: Learning Nonparametric DBNs With Prior Knowledge.
  Xiangyu Sun, Oliver Schulte, Guiliang Liu, Pascal Poupart.
  International Conference on Artificial Intelligence and Statistics (AISTATS) 2023.
  [Paper (PDF)],

Year 2022 and Before

• Uncertainty-Aware Reinforcement Learning for Risk-Sensitive Player Evaluation in Sports Game.
  Guiliang Liu, Yudong Luo, Oliver Schulte, Pascal Poupart.
  Advances in Neural Information Processing Systems (NeurIPS) 2022.
  [Paper (PDF)], [Slides], [Code (To be released)],
• Learning Object-Oriented Dynamics for Planning from Text.
  Guiliang Liu, Ashutosh Adhikari, Amir-massoud Farahmand, Pascal Poupart.
  International Conference on Learning Representations (ICLR) 2022.
  [Paper (PDF)], [Slides], [Code],
• Distributional Reinforcement Learning with Monotonic Splines.
  Yudong Luo, Guiliang Liu, Haonan Duan, Oliver Schulte, Pascal Poupart.
  International Conference on Learning Representations (ICLR) 2022.
  [Paper (PDF)], [Code],
• Learning Tree Interpretation from Object Representation for Deep Reinforcement Learning.
  Guiliang Liu, Xiangyu Sun, Oliver Schulte, Pascal Poupart.
  Advances in Neural Information Processing Systems (NeurIPS) 2021.
  [Paper (PDF)], [Slides], [Code],
• Learning Agent Representations for Ice Hockey.
  Guiliang Liu, Oliver Schulte, Pascal Poupart, Mike Rudd, Mehrsan Javan.
  Advances in Neural Information Processing Systems (NeurIPS) 2020.
  [Paper (PDF)], [Slides], [Video], [Code ],
• An Advantage Actor-Critic Algorithm with Confidence Exploration for Open Information Extraction.
  Guiliang Liu, Xu Li, Mingming Sun, Ping Li.
  SIAM International Conference on Data Mining (SDM) 2020.
  [Paper (PDF)]
• Extracting Knowledge from Web Text with Monte Carlo Tree Search.
  Guiliang Liu, Xu Li, Jiakang Wang, Mingming Sun, Ping Li.
  The ACM Web Conference (WWW) 2020.
  [Paper (PDF)], [Slides]
• Cracking the Black Box: Distilling Deep Sports Analytics.
  Xiangyu Sun, Jack Davis, Oliver Schulte, Guiliang Liu.
  SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2020.
  [Paper (PDF)], [arXiv], [Slides], [ Video], [Code]
• Deep soccer analytics: Learning an action-value function for evaluating soccer players.
  Guiliang Liu, Yudong Luo, Oliver Schulte, and Tarak Kharrat.
  ECML-PKDD 2020 Journal Track, published at Data Mining and Knowledge Discovery (DMKD).
  [Paper (PDF)], [Slides],
• Deep Reinforcement Learning in Ice Hockey for Context-Aware Player Evaluation.
  Guiliang Liu, Oliver Schulte.
  International Joint Conference on Artificial Intelligence (IJCAI) 2018.
  [Paper (PDF)], [arXiv], [Code], [Slides],
• Toward Interpretable Deep Reinforcement Learning with Linear Model U-Trees.
  Guiliang Liu, Oliver Schulte, Wang Zhu,
  European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Database (ECML-PKDD) 2018.
  [Paper (PDF)], [arXiv], [Slides],