“Robotics and other combinations will make the world pretty fantastic compared with today.” -- Bill Gates.

Hi, there. I'm a senior student at Tsinghua University, majoring in Electronic Engineering.

Currently, I am fortunate to work with Prof. Wenzhen Yuan as a research assistant at RoboTouch Lab, UIUC CS.

Previously, I was honored to be a research intern at Microsoft Research, advised by Senior Researcher Shaohan Huang.

Meanwhile, I also spent time at Institute for Interdisciplinary Information Sciences (IIIS), Tsinghua University, advised by Prof. Jianyu Chen.

Currently, my research interests lie in the intersection of Robotics and AI. My ultimate goal is to develop intelligent robotic agents capable of performing complex manipulation tasks and push the boundaries of robotic manipulation.

Future Research Directions: General-Purpose Robot Foundation Models:
- (1)Integrating tactile sensing for fine-grained and effective manipulation.
- (2)Utilizing IL and RL for robust long-horizon embodied interaction.
- (3)Designing novel learning architectures to output low-level motor commands efficiently.
- (4)Developing generalizable robotic manipulation policies across diverse embodiments.

I am actively applying for a Ph.D. position in 2025 Fall!

Email / CV / GitHub / LinkedIn / Twitter /

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Updates
Selected Research
DoorBot: Closed-Loop Task Planning and Manipulation for Door Opening in the Wild with Haptic Feedback
Zhi Wang*, Yuchen Mo*, Shengmiao Jin, Wenzhen Yuan
IEEE International Conference on Robotics and Automation (ICRA), 2025, Under Review .
Website / Paper / Video / Code /
Proposed DoorBot, a haptic-aware closed-loop hierarchical control framework that enables robots to explore and open different unseen doors in the wild. We test our system on 20 unseen doors across different buildings, featuring diverse appearances and mechanical types. Our framework achieves a 90% success rate, demonstrating its ability to generalize and robustly handle varied door-opening tasks.


KOSMOS-E: Learning to Follow Instruction for Robotic Grasping
Zhi Wang*, Xun Wu*, Shaohan Huang, Li Dong, Wenhui Wang, Shuming Ma, Furu Wei
IEEE International Conference on Intelligent Robots and System (IROS), 2024.
Website / Paper / Video / Code /
Proposed KOSMOS-E, a Multimodal Large Language Model (MLLM) that leverages instruction-following robotic grasping data to enhance capabilities for precise and intricate robotic grasping maneuvers.
Education
Experiences
Teaching Experience
MISC