关于 | Li Mingyu 李明宇

研究方向

机器人操作

面向真实硬件约束，设计低成本、高实用性的灵巧抓取系统。

边缘智能

在资源受限的嵌入式平台上部署高效感知与实时控制算法。

软硬件协同设计

将机械、电子与算法决策作为一个完整系统共同优化。

我是同济大学机械工程与机器人学院的大三本科生。在 Vision4Robotics（V4R）实验室，我主持灵巧手零样本视觉抓取项目，并参与机械臂嵌入式控制与智能制造仿真研究。相关成果包括一篇以第一作者兼通讯作者身份完成、已被 IEEE ICARM 2026 录用的论文，以及一项获国家知识产权局受理的实用新型专利申请。

目前 GPA 为 4.28/5.0，曾获同济大学一等奖学金与校级优秀学生荣誉（2025）。我正在寻求机器人操作、具身智能与软硬件协同设计方向的硕士或直博机会。

科研经历

Vision4Robotics（V4R）实验室 · 同济大学

2025 – 至今

开展灵巧机器人操作与边缘端感知研究，参与双视觉无限翻转灵巧手和跨域边缘智能蛇形机器人项目。

智能车实验室 · 同济大学

2024 – 2026

在二维视觉与嵌入式平台约束下开发高精度机械臂抓取系统，获全国大学生智能汽车竞赛赛区二等奖。

精选项目

Dual-Vision Infinite-Roll Hand for Edge-Native Grasping

Panoptic Grasp

双视觉、无限翻转与边缘端感知协同的灵巧抓取系统。

Edge-AI Snake Robot for Cross-Domain Operations

跨域作业型边缘智能蛇形机器人

复合材料金属骨架模块化蛇形机器人，具备六种运动姿态与边缘端具身智能，展出于中国国际工业博览会（上海）。

Precision Grasping with 2D Camera and Embedded System

二维相机与嵌入式系统限定下的机械臂精准抓取

五自由度机械臂在仅有二维平面相机与嵌入式平台的严苛限制下，实现 ≥98% 抓取成功率与 ±0.5 mm 定位精度。

emaWD Manual Work Simulation in Smart Manufacturing

智能制造系统人工工作仿真优化

基于 ema WD 对减速器人工装配进行仿真，从工位布局、人体姿势与节拍三方面开展分析与优化，服务智能化产线中的人工作业设计。

代表荣誉

National Second Prize, Higher Education Cup (Team Event)

National Third Prize, BMHRI Cup Logistics Technology Contest

Municipal First Prize, 14th Shanghai Mech Innovation (Team)

Municipal First Prize, 14th Shanghai Mech Innovation (Individual)

Provincial First Prize & National Third Prize, Mechanical Design Innovation

University Silver Award, China International College Students' Innovation Competition (2025)

Track 3 National First Prize, 9th National Innovation Experience Competition

Tongji University Outstanding Student Award

Sanguo Rural English Teaching Volunteer Certificate

National First Prize, Higher Education Cup (Additive Manufacturing)

First-Class Scholarship, Tongji University (2024–2025)

Individual Second Prize, Shanghai Graphics Cup (3D Modeling)

Team Second Prize, Shanghai Graphics Cup (3D Modeling)

工程之外

2023 年 10–12 月，我参与三果“中国农村线上英语教育服务”远程支教项目，为云南学生开展每周一次、共十周的英语教学与陪伴。这段经历让我在工程实践之外进一步锻炼了耐心、沟通与换位思考能力。

动态

May 16, 2026	Won Provincial First Prize and National Third Prize in the 15th Shanghai College Student Mechanical Engineering Innovation Competition & 12th National College Student Mechanical Design Innovation Competition (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

代表论文

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}, }