Hi there 
Welcome to my academic base!
I am a junior at Xidian University, currently pursuing research in robot learning with particular attention to perception and inference about the real world under the guidance of Prof. Lixin Yang and Prof. Cewu Lu at the MVIG Lab, Shanghai Jiao Tong University. My overarching objective is to develop autonomous robots capable of independent exploration and self-directed learning through dynamic interaction with real world.
Previously, during my sophomore year, I contributed to research on adversarial attacks against computer vision systems at the Key Laboratory of Cooperative Intelligent Systems, Ministry of Education, under the supervision of Prof. Hao Li and Prof. Maoguo Gong. This experience instilled in me a deep appreciation for the importance of robustness and security in vision-based systems, particularly for applications in agents’ autonomous exploration.
Research Interests
My research interests and the deep learning paradigm I aim to shape primarily focus on:
Reasoning-based Learning: Inferring knowledge from real-world interactions, such as object motions, contacts, and affordances, to enhance agents’ reasoning and generalization, paving the way for active learning development.
Generative Modeling: Using generative methods to model objects under physical laws, spanning feature, semantic, and scene levels, to ultimately build a world model.
News
- Our work Advdisplay was accepted at AAAI 2025 🔥
- My first work on robot learning:MBA, about object motion for robots manipulation.
- In charge of Microsoft Club. Feel free to reach out if you’d like to join.
- I have set up a Blog Site, welcome everyone to visit!
Research Experience
Publications
Yue Su*, Xinyu Zhan*, Hongjie Fang, Yong-Lu Li, Cewu Lu, Lixin Yang ✉
Propose MBA, a novel plug-and-play module leveraging cascaded diffusion processes to generate actions guided by object motion, enabling seamless integration with manipulation policies.
[arxiv] [website] [code]
Yue Su, Hao Li✉, Maoguo Gong✉
A generative physical adversarial attack on VI-ReID models perturbs modality-invariant features.
[arxiv]
Hao Li✉, Fanggao Wan, Yue Su, Yue Wu, Mingyang Zhang, Maoguo Gong✉
Historically, infrared adversarial attacks were single-use and tough to deploy. Using TEC, we implemented efficient attacks adaptable to hardware scenarios.Accepted at AAAI 2025. 🔥
Projects
Time series forecasting is suited for U-Net's architecture due to its consistent input-output distributions and strong mathematical alignment. Combining U-Net with Bert-Encoder improved performance by incorporating both local and global attention.
[code] [report-cn]
The problem of finding the maximum flow lies in how to design better heuristic information to find the augmenting path. We boldly challenge this problem through the ant colony algorithm.
[code] [report-cn]
We tried to extend FGSM to the 3D field and achieved significant success within a certain gradient range, but the sampling method of 3D models tells us that things seem to be not that simple...
[code] [report-cn]
