Yaowei Chen
Papers
2
Total Citations
20
H-Index
2
About
Yaowei Chen is an emerging researcher in robotics and artificial intelligence, with a primary focus on autonomous exploration and legged locomotion. His most cited work, "An improved frontier-based robot exploration strategy combined with deep reinforcement learning" (2024, 15 citations), introduces a novel hybrid approach that merges traditional frontier-based exploration with deep reinforcement learning, significantly enhancing the efficiency of autonomous robots in unknown environments. This contribution addresses a critical challenge in robotics by enabling more intelligent and adaptive exploration strategies. Chen further demonstrates his expertise in control systems through "Real-time robust nonlinear model predictive control with monotonically increasing weight for quadruped locomotion" (2024, 5 citations), where he develops a real-time control framework that improves the stability and robustness of quadruped robots during dynamic movements. Despite the recent publication dates of his work, the citations reflect growing interest and recognition in the robotics community. Chen’s research bridges the gap between classical robotics methods and modern learning-based techniques, positioning him as a promising contributor to the fields of autonomous navigation and legged robot control.
Research Focus
Key Achievements
Top Papers
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