Papers
9
Total Citations
180
H-Index
6
About
Chengjun Chen is a leading researcher in intelligent robotics and human-robot interaction, with a focus on augmented reality (AR) teleoperation, reinforcement learning, and autonomous assembly. His work bridges virtual and physical systems, notably through an AR-based robot teleoperation system using RGB-D imaging and attitude teaching devices (58 citations), which enhances intuitive control. Chen’s contributions to robot teaching are significant: he developed a virtual-physical collision detection interface for AR-based interactive teaching (49 citations) and a system that detects hand-robot contact states and motion intentions (18 citations), enabling safer, more natural human-robot collaboration. In assembly automation, he pioneered active compliance control for peg-in-hole tasks using combined reinforcement learning (21 citations) and advanced deep reinforcement learning for autonomous grasping and assembly skill acquisition (13 citations). His recent work extends to underwater robotics, proposing a one-stage multi-scale efficient network for underwater target detection (6 citations), addressing challenges in small and dense target recognition. Chen’s research, with over 180 total citations, consistently integrates perception, control, and learning, pushing the boundaries of robot autonomy and human-robot interaction in manufacturing and beyond.
Research Focus
Key Achievements
Top Papers
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- 7One stage multi-scale efficient network for underwater target detection6 citations · 2024
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