Yijie Chen
Papers
2
Total Citations
13
H-Index
2
About
Yijie Chen is a robotics researcher whose work focuses on advancing human-robot interaction through intelligent torque estimation and collision detection. His primary research areas include robot dynamics, sensorless torque estimation, and deep learning applications in industrial robotics. Chen's major contribution lies in developing LSTM-based methods to predict external torques and estimate joint torques without force/torque sensors—a critical challenge for safe human-robot collaboration. His most cited work, "LSTM-based external torque prediction for 6-DOF robot collision detection" (2023, 11 citations), introduces a novel approach that leverages recurrent neural networks to anticipate collisions in real time. In his subsequent 2024 paper, Chen further refines these techniques by combining error compensation models with iterative weighted parameter identification, addressing the persistent inaccuracies in dynamic model-based torque estimation. This work is essential for enabling robots to operate safely alongside humans without expensive sensor hardware. Chen's research bridges the gap between theoretical dynamics and practical, cost-effective safety systems, making him a notable contributor to the field of intelligent industrial robotics.
Research Focus
Key Achievements
Top Papers
- 1LSTM-based external torque prediction for 6-DOF robot collision detection11 citations · 2023
- 2