Zhehuan Chen
Papers
2
Total Citations
6
H-Index
2
About
Zhehuan Chen is a rising researcher in robotics and embodied AI, whose work centers on enabling robots to perform complex, contact-rich manipulation tasks in real-world environments. His key research areas include multi-robot assembly, reinforcement learning for robotic manipulation, and visual affordance learning. Chen’s most notable contribution is **RoboAssembly**, a novel simulation environment for learning generalizable furniture assembly policies, which formulates part assembly as a concrete reinforcement learning problem and has garnered 4 citations. This work addresses a critical challenge in robotics: teaching multiple robots to collaboratively assemble diverse objects. In his subsequent work, **DualAfford** (2 citations), Chen advances collaborative visual affordance learning for dual-gripper manipulation, enabling robots to understand and manipulate a wide variety of 3D objects in unstructured human environments. By focusing on scalable, generalizable manipulation skills, Chen is laying the groundwork for future home-assistant robots that can autonomously perform daily tasks. His research, though early in its citation impact, represents a significant step toward bridging the gap between simulation and real-world robotic dexterity.
Research Focus
Key Achievements
Top Papers
- 1
- 2