Yueci Deng (邓岳慈)
Ph.D. Student, School of Data Science, CUHK-SZ
I am a first-year CS Ph.D. student at the Chinese University of Hong Kong, Shenzhen (CUHK-SZ), supervised by Prof Kui Jia. I received my B.S. from UESTC (2014-2018) and M.S. from NTU, Singapore (2018-2019). Before joining CUHK-SZ, I worked as an architect at DexForce Technology, where I led the development of DexVerseTM, a 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
- Generative 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
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.
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
Description: A part-centric generative framework for articulated object creation that synthesizes part geometry, composition, and articulation from a single image, enabling fast feed-forward inference without per-instance optimization.
Description: This paper introduces Sim2Real-VLA, a generalist robotic control model that enables zero-shot transfer from synthetic simulation to real-world manipulation tasks.
Description: A novel data engine for automating data generation and scaling for sim-to-real transfer of robotic manipulation tasks.
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.