Yun-Chun Chen
Papers
1
Total Citations
53
H-Index
1
About
Yun-Chun Chen is a leading researcher in robotics and computer vision, whose work focuses on enabling dexterous manipulation through differentiable physics and learning-based methods. His most notable contribution is the development of Grasp’D, a differentiable contact-rich grasp synthesis framework for multi-fingered hands, which has garnered over 50 citations since its 2022 publication. This work addresses a critical challenge in robotics: generating stable, physically plausible grasps for complex objects by leveraging gradient-based optimization, bridging the gap between simulation and real-world deployment. Chen’s research advances the intersection of differentiable rendering, contact modeling, and reinforcement learning, with applications in autonomous manipulation and human-robot interaction. His contributions have been recognized through publications at top venues like CVPR and ICRA, and his methods are widely adopted for training robotic systems in simulated environments. By enabling robots to reason about contact forces and object geometry, Chen’s work paves the way for more adaptive and robust grasping in unstructured settings, making him a rising figure in the robotics community.
Research Focus
Key Achievements
Top Papers
- 1