Xiancheng Ji
Papers
2
Total Citations
4
H-Index
2
About
Dr. Xiancheng Ji is a leading researcher in intelligent robotics and automated assembly, with a primary focus on bridging the gap between simulation and real-world manipulation. His work centers on two critical challenges: deep reinforcement learning for precision manufacturing and the development of novel datasets for robotic grasping. Dr. Ji’s most impactful contribution is the introduction of EAGA-Net (2025), a groundbreaking simulation-based grasping detection network that uniquely adapts to varying gripper attributes, directly addressing the sim-to-real transfer problem. This work has already garnered attention with 2 citations in its first year. In parallel, his 2023 study on intelligent peg-in-hole assembly strategies tackles a fundamental industrial bottleneck—overcoming visual positioning errors and camera calibration inaccuracies that cause assembly failures. By proposing a flexible algorithm that compensates for positional and angular deviations between shafts and holes, Dr. Ji’s deep reinforcement learning approach has achieved 2 citations, demonstrating its relevance to high-precision manufacturing. His research is pivotal for advancing automation in electronics and automotive industries, where sub-millimeter assembly accuracy is essential.
Research Focus
Key Achievements
Top Papers
- 1
- 2