I am a first-year CS Ph.D. student at the School of Data Science, Chinese University of Hong Kong, Shenzhen (CUHK-SZ), supervised by Prof Kui Jia. I received my Bachelor’s degree in University of Electronic Science and Technology of China (UESTC, 2014 - 2018), and Master’s degree in Nanyang Technological University (NTU, 2018-2019), Singapore. Before joining CUHK-SZ, I worked as a R&D engineer at DexForce Technology, where I lead the development of DexVerseTM, the world’s leading Sim2Real AI Platform for Embodied Intelligence.
My research interests are mainly in the following areas:
- Systems:
- High-performance, Heterogeneous and GPU-accelerated Simulation Engine Architecture
- Data Generation and Model Training Systems for Embodied Intelligence
- Simulation:
- Generative Model for Simulation
- Differentiable Rendering and Physics
- Neural Representation for Simulation
- Embodied Intelligence:
- Physics-Structured Model Architecture
- Online and Continual Learning for Embodied Agents
- Sim2Real Transfer and Domain Adaptation
Projects
 | EmbodiChain: An end-to-end, GPU-accelerated, and modular platform for building generalized Embodied Intelligence Website | Code Description: EmbodiChain is a unified, GPU-accelerated framework designed for pushing the boundaries of embodied AI research and development. It integrates high-performance simulation, data collection via real-to-sim techniques, data scaling pipeline, modular model architectures, and efficient training & evaluation tools. All of these components work seamlessly together to facilitate rapid experimentation and deployment of embodied intelligence and perform Sim2Real transfer into real-world robotic systems. |
 | Open3D: A Modern Library for 3D Data Processing Website | Code Description: The leading open-source library for 3D processing with 400K+ monthly downloads from PyPI. Open3D exposes a set of carefully selected data structures and algorithms in both C++ and Python for 3D data processing tasks including point cloud processing, mesh processing, and 3D visualization. |
Publications
 | 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 Poster Paper / Code Description: This paper introduces Sim2Real-VLA, a generalist robotic control model that enables zero-shot transfer from synthetic simulation to real-world manipulation tasks. |
 | GS-World: An Engine-driven Learning Paradigm for Pursuing Embodied Intelligence using World Models of Generative Simulation Position paper Guiliang Liu, Yueci Deng, Zhen Liu, Kui Jia Paper |
 | 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 (*Equal contribution) International Conference on Machine Learning (ICML), 2025 Poster Paper / Code Description: A novel data engine for automating data generation and scaling for sim-to-real transfer of robotic manipulation tasks. |
 | 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 / Code Description: This work proposes the YOTO (You Only Teach Once), which can extract and then inject patterns of bimanual actions from as few as a single binocular observation of hand movements, and teach dual robot arms various complex tasks. |