En-lai Chen
Papers
1
Total Citations
8
H-Index
1
About
En-lai Chen is a researcher in robotics and intelligent control, with a primary focus on motion planning and reinforcement learning for robotic manipulators. His most-cited work, "Research on Motion Planning of Seven Degree of Freedom Manipulator Based on DDPG" (2018), introduces a deep deterministic policy gradient (DDPG) approach to tackle the complex, high-dimensional control challenges of seven-degree-of-freedom manipulators. This contribution bridges deep reinforcement learning with practical robotic motion planning, offering a data-driven alternative to traditional kinematic methods. With 8 citations, the paper has informed subsequent studies in autonomous manipulation and adaptive control. Chen’s research is particularly relevant for advancing flexible manufacturing and service robotics, where precise, real-time motion in constrained environments is critical. His work underscores the growing synergy between artificial intelligence and robotics, highlighting how model-free algorithms can enhance dexterity and efficiency in multi-jointed systems. For students and researchers exploring reinforcement learning applications in robotics, Chen’s study serves as a foundational reference for integrating DDPG into motion planning pipelines.
Research Focus
Key Achievements
Top Papers
- 1