Hi there 
Welcome to my academic base!
I am a third-year undergraduate(2022-2026) at Xidian University, currently pursuing research in robot learning under the guidance of Prof. Lixin Yang and Prof. Cewu Lu at the MVIG Lab, Shanghai Jiao Tong University. Previously, I contributed to research on adversarial attacks against vision models at the Laboratory of Cooperative Intelligent Systems, under the supervision of Prof. Hao Li and Prof. Maoguo Gong. I am looking for 2026 fall phd or master opportunities, alongside potential internships, whether in academia or industry.
Research Interests
My research interests and the learning paradigm I aim to shape primarily focus on:
Reasoning-based Learning: Inferring knowledge from interactions with environments to enhance agent’s reasoning and generalization, promoting the development of proactive learning.
Generative Modeling: Modeling the agent’s knowledge through generative methods to develop into a world model.
News
- Unlesh the potential of Autoregressive model in imitation learning: Dense Policy is on preprint!
- Build MetaPalace, Let you in a meta world of The Palace Museum.
- 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, Han Xue,
Haoshu Fang, Yong-Lu Li, Cewu Lu, Lixin Yang†
Propose Dense Policy, A bidirectional robotic autoregressive policy, which infers trajectories by gradually expanding actions from sparse keyframes, has demonstrated capabilities exceeding diffusion policies.
[arXiv] [website] [3D-code] [2D-code]
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.
AAAI 2025
[paper]
Projects
We've done what the Old Palace official website couldn't: offering 3D artifact views with single-view reconstruction and an interactive LLM-powered tour guider using RAG technology.
[website] [front-end code] [back-end code]
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]
