Ruoxi Chen
Papers
1
Total Citations
9
H-Index
1
About
Dr. Ruoxi Chen is a leading researcher in robotic manipulation and safe control systems, whose work bridges reinforcement learning and physical human-robot interaction. Her most-cited paper, "Reinforcement Learning for Robotic Safe Control with Force Sensing" (2019, 9 citations), tackles a critical challenge: enabling robots to perform complex tasks in unstructured environments where traditional programming fails. By integrating force sensing with reinforcement learning, Chen developed adaptive policies that enhance both stability and reliability—addressing the notorious safety concerns that limit RL deployment in real-world robotics. This foundational work demonstrates how learning-based approaches can achieve delicate manipulation while maintaining physical safety, a key requirement for collaborative robots. Chen’s contributions are particularly impactful for applications in manufacturing, healthcare, and assistive robotics, where robots must interact safely with humans and unpredictable surroundings. Her research continues to push the boundaries of autonomous systems, making robots more capable and trustworthy in dynamic, real-world settings.
Research Focus
Key Achievements
Top Papers
- 1Reinforcement Learning for Robotic Safe Control with Force Sensing9 citations · 2019