Li Mingyu 李明宇

School of Mechanical Engineering

Tongji University

Shanghai, China

I am a junior undergraduate student at Tongji University, majoring in Mechanical Engineering. My research interests lie at the intersection of embodied intelligence, embedded systems, and computer vision, with a specific focus on low-cost dexterous intelligent grasping systems.

I am deeply passionate about building intelligent robotic platforms. Currently, I lead a research project on zero-shot visual grasping for dexterous hands in the Vision4Robotics (V4R) Lab under the supervision of Prof. Changhong Fu, while also contributing to the embedded control and intelligent manufacturing simulation of robotic arms. My research efforts include a first-author and corresponding-author paper accepted by IEEE ICARM 2026, as well as a CNIPA-accepted utility model patent application.

I am currently seeking master’s and direct Ph.D. opportunities where I can continue working on hardware-software co-design for practical robotic manipulation.

Academically, I maintain a GPA of 4.28/5.0, earning me the First-Class Scholarship and the Outstanding Student Award in 2025. Beyond the lab and classroom, I have actively participated in national-level engineering competitions, winning multiple awards that reflect my strong foundation in engineering practice and teamwork.

Research Experience

Vision4Robotics (V4R) Lab, Tongji University · 2025 – Present
Core member. Working on dexterous robotic manipulation and edge-native perception systems, with contributions to a dual-vision infinite-roll dexterous hand (IEEE ICARM 2026, accepted) and a cross-domain edge-AI snake robot (CIIF Shanghai).

Intelligent Vehicle Lab, Tongji University · 2024 – 2026
Core member. Developed a precision robotic arm grasping system under 2D-camera and embedded-system constraints, and competed in the National Intelligent Vehicle Competition, winning the 2nd Prize.

Tongji University Outstanding Student Award / 同济大学校级优秀学生

news

May 16, 2026	Won Shanghai Regional First Prize and National Final Qualification in 15th Shanghai College Student Mechanical Engineering Innovation Competition & 12th National College Student Mechanical Design Innovation Competition Shanghai Regional (Smart Shrimp Peeling / 智械解虾)
Apr 03, 2026	Submitted a first-author and corresponding-author paper to IEEE ICARM 2026: “Hardware-Software Co-Design of a Dual-Vision, Infinite-Roll Hand for Edge-Native Grasping.”
Aug 23, 2025	Won National Third Prize in BMHRI Cup Logistics Technology Creativity Contest
Jul 23, 2025	Won Regional Second Prize in 20th National University Students Intelligent Car Race - Baidu Smart Transportation Track
Jul 07, 2025	Won National First Prize in Higher Education Cup National College Student Advanced Graphics Technology and Product Information Modeling Innovation Competition - Additive Manufacturing Track
Jun 10, 2025	Utility model patent application “Folding Cane Chair” accepted by the China National Intellectual Property Administration. Application No. 202521174680.9.
May 16, 2025	Won Municipal First Prize in 14th Shanghai College Student Mechanical Engineering Innovation Competition

selected publications

ICARM
Hardware-Software Co-Design of a Dual-Vision, Infinite-Roll Hand for Edge-Native Grasping

Mingyu Li, Zhixian Gao, and Zhangchi Guo

In Proceedings of the IEEE International Conference on Advanced Robotics and Mechatronics (ICARM), Paris, France, Tongji University , 2026

Accepted. Mingyu Li is the first author and corresponding author.

Abs Bib PDF

Conventional robotic grasping in semi-structured service and workstation environments is often constrained by two physical bottlenecks: visual blind spots in rearward coverage and the bounded rotational range of traditional wrists. This paper presents an edge-native hand-wrist platform that addresses these coupled limitations through electromechanical co-design. We integrate a dorsal-ventral dual-camera relay for complementary front-rear perception with a direct-drive slip-ring mechanism enabling infinite continuous wrist roll. To support real-time operation on resource-constrained edge processors, the system features a reparameterized zero-shot detector that supports language-guided grasping while removing online text-encoding overhead via offline feature reparameterization. Kinematic and spatial validations in a predefined workstation-style setup indicate that the integrated platform can execute task-prior-driven grasp acquisitions for both forward and rearward targets entirely on the edge, without additional task-specific fine-tuning or cloud offloading.
@inproceedings{li2026icarm, title = {Hardware-Software Co-Design of a Dual-Vision, Infinite-Roll Hand for Edge-Native Grasping}, author = {Li, Mingyu and Gao, Zhixian and Guo, Zhangchi}, booktitle = {Proceedings of the IEEE International Conference on Advanced Robotics and Mechatronics (ICARM)}, location = {Paris, France}, year = {2026}, note = {Accepted. Mingyu Li is the first author and corresponding author.}, keywords = {Robotic grasping, Dexterous hand, Edge AI, Embedded vision, Hardware-software co-design, Zero-shot detection}, }