2024 - 2027
M.Eng. in Mechanical Engineering
Huazhong University of Science and Technology, Wuhan, China
Hi there!
I am an M.Eng. student in Mechanical Engineering at Huazhong University of Science and Technology, advised by Prof. Hua Yang. My research focuses on vision-based tactile sensing for contact-rich robotic manipulation.
I work across sensor structure and fabrication, imaging hardware, geometric and force calibration, 3D reconstruction, force estimation, neural inverse rendering, and FPGA acceleration. I am interested in systems where sensing mechanics, perception algorithms, and real-time deployment are designed together.
Education
2020 - 2024
B.Eng. in Robotics Engineering
Huazhong University of Science and Technology, Wuhan, China
Research Interests
- Vision-based tactile sensing
- Contact geometry and force perception
- Neural inverse rendering and synthetic tactile data
- Multimodal perception for robotic manipulation
- Edge AI and FPGA acceleration
Technical Practice
- Sensing
- Optical and mechanical sensor design, elastomer fabrication, Multi-spectral imaging, geometric and force calibration
- Perception
- Photometric stereo, surface reconstruction, force estimation, inverse rendering, synthetic data
- Hardware
- FPGA acceleration, embedded deployment, camera interfaces, PCB prototyping
- Programming
- Python, C/C++, MATLAB, Verilog/SystemVerilog, PyTorch, OpenCV
News
- Released the FasTac project page and arXiv preprint.
- Launched this academic website.
- Published the SMF-PSNN project page and paper in IEEE/ASME Transactions on Mechatronics.
- Published the GelSplitter3D project page and paper at IEEE/RSJ IROS.
Publications
Selected peer-reviewed work. My name is shown in bold.
FasTac: A Curved Multispectral Vision-Based Tactile Sensor for High-Speed High-Precision 3D Shape and Force Perception
A compact RGB-NIR tactile fingertip combining boundary-prior 3D reconstruction, position-aware three-axis force estimation, and a low-latency FPGA perception pipeline.
arXiv:2607.28416; submitted to IEEE/ASME Transactions on Mechatronics, 2026
High-Precision RGB-NIR Vision-Tactile Sensor via Neural Inverse Rendering for Robotic Perception
High-precision tactile reconstruction, self-calibration, and synthetic tactile image generation with RGB-NIR sensing.
IEEE/ASME Transactions on Mechatronics, 2025
3D Vision-Tactile Reconstruction from Infrared and Visible Images for Robotic Fine-Grained Tactile Perception
A curved RGB-NIR tactile fingertip with neural photometric stereo and geometry-prior normal integration.
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2025
Projects
Selected research and engineering work across sensing, perception, and deployment.
FasTac: Curved Multispectral Vision-Based Tactile Perception
Led the development of a compact curved RGB-NIR tactile sensing system spanning sensor design, synchronized multispectral imaging, boundary-prior 3D reconstruction, HyperForce three-axis force estimation, and FPGA edge deployment.
Achieves 0.0415 mm depth MAE, 2.74% / 2.39% normal / shear force NMAE, and 1.09 ms FPGA image-to-Fz latency.
Neural Inverse Rendering and Synthetic Tactile Data
Developed SMF-PSNN for near-field multi-source illumination self-calibration, surface-normal and depth reconstruction, and RGB-NIR tactile image synthesis. The calibrated rendering model generates transferable tactile observations for pose estimation, fingerprint recognition, and slip detection.
Neural inverse rendering reaches 30.22 dB PSNR, improving by 6.33 dB over the single-light-source baseline.
FPGA Edge Deployment
Developing hardware-oriented pipelines for surface-normal inference, boundary-prior depth reconstruction, and normal-force estimation on Xilinx Zynq UltraScale+ platforms.
Computation rates reach 1022 Hz for image-to-depth and 918 Hz for image-to-normal-force.
Multimodal Dexterous-Hand Integration
Contributed vision-tactile sensor adaptation, fabrication, integration, and perception algorithms for two-finger, three-finger, and five-finger robotic end effectors.
Focused on repeatable calibration, contact geometry reconstruction, and system-level validation.
Industry Impact. My work on vision-tactile sensor adaptation, calibration, and perception algorithms supported Zhejiang Shiyue Technology (LumiBot) in developing the Lumi-Tac vision-tactile sensor and Lumi-Dex dexterous-hand product line.
Build
Hardware systems I helped build for vision-based tactile manipulation.
Two-Finger Gripper
Vision-tactile end-effector for fine manipulation and contact reconstruction. Project page ↗
Three-Finger Gripper
Multifinger tactile platform for object interaction and manipulation demos. Project page ↗
Five-Finger Dexterous Hand
Dexterous hand prototype with integrated tactile modules. Project page ↗