Houcheng Tang
Papers
2
Total Citations
10
H-Index
2
About
Houcheng Tang is a researcher at the forefront of applying artificial intelligence to robotics, with a primary focus on neural network-based modeling and transfer learning for robotic manipulation. His work addresses the critical challenge of enabling robots to adapt knowledge across different tasks and configurations, significantly reducing the need for retraining from scratch. Tang’s most impactful contribution is his pioneering study on neural network-based transfer learning for the inverse displacement analysis of robot manipulators, which has garnered 8 citations and demonstrates the feasibility of using pre-trained models to solve complex kinematic problems for new end-effector paths. He further advanced this approach in his work on robot path generation, exploring how artificial neural networks with varying structures can leverage data from different trajectories to improve learning efficiency. By bridging machine learning and robotics, Tang’s research offers practical pathways toward more flexible, intelligent, and data-efficient robotic systems, making his work highly relevant for students and researchers interested in the intersection of AI and automation.
Research Focus
Key Achievements
Top Papers
- 1
- 2Neural Network Based Transfer Learning for Robot Path Generation2 citations · 2021