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👋 Zhexi Luo I am Zhexi Luo, an undergraduate in Computer Science at Sun Yat-sen University, where I conduct research at ISEE@SYSU. Currently, I am a research intern at LinS Lab, advised by Lin Shao. My current research interests focus on dexterous grasping and dexterous manipulation, aiming to develop robust and reliable policies that endow robotic systems with human-like dexterity. 🎓 I am actively seeking PhD positions. For relevant opportunities or any thoughts related to my research, feel free to reach out! |
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📰 News |
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📚 Research |
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OmniDexGrasp: Generalizable Dexterous Grasping via Foundation Model and Force Feedback
Yi-Lin Wei*, Zhexi Luo*, Yuhao Lin, Mu Lin, Zhizhao Liang, Shuoyu Chen, Wei-Shi Zheng Accepted, IEEE International Conference on Robotics and Automation (ICRA), 2026 arXiv / project page / code A generalizable dexterous framework that leverages generative foundation models to achieve omni-capabilities across diverse user prompts, dexterous embodiments, and grasping tasks. |
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Generalizable Humanoid Loco-Manipulation via Spatial Perception and Decision-Making of Multi-Agent Large Model
Zhizhao Liang, Yi-Lin Wei, Xuhang Chen, Mu Lin, Yi-Xiang He, Zhexi Luo, Jun-Hui Liu, Kun-Yu Lin, Wei-Shi Zheng Under Review, 2026 arXiv / project page / code A generalizable humanoid loco-manipulation framework that leverages multi-agent large models, combining an Active Spatial Brain for spatial perception and task planning with a Generalizable Action Cerebellum for executable action generation without task-specific real-robot data. |
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DriftTrace: Combating Concept Drift in Security Applications through Detection and Explanation
Yuedong Pan, Lixin Zhao, Tao Leng, Zhexi Luo, Lijun Cai, Aimin Yu, Dan Meng Accepted, IEEE Transactions on Information Forensics and Security (T-IFS), 2026 IEEE Xplore A unified framework that combines detecting, explaining, and adapting to out-of-training-distribution (OOD) data for improving model robustness in dynamic open-world environments. |
🔬 Project |
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Smoke Removal in Laparoscopic Surgical Videos Using Temporal Smoke-Free Semantic Information
Developed a novel framework that integrates video prediction and image desmoking to address surgical smoke in laparoscopic videos. By leveraging temporal semantic information from smoke-free frames within a Cycle-GAN based architecture, the framework achieves real-time smoke removal and demonstrates superior performance over existing approaches, improving surgical visibility and safety. |
💼 Experience |
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LinS Lab, National University of Singapore (NUS) Research Intern, advised by Lin Shao 2025.11 - Present |
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Sun Yat-sen University Bachelor in Computer Science and Technology 2022 - Present |
🏆 Awards |
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🧑🔬 Academic Service |
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Conference Reviewer: CVPR 2026, ICCV 2026, ECCV 2026 |
