Papers
9
Total Citations
100
H-Index
6
About
Ruiqi Wu is a pioneering researcher at the intersection of artificial intelligence, robotics, and traditional Chinese art, whose work focuses on teaching robots to master the complex skill of Chinese calligraphy. Their major contributions lie in developing novel frameworks that integrate deep reinforcement learning, generative adversarial networks (GANs), and actor-critic models to enable robots to learn and replicate calligraphic strokes directly from images, bypassing the need for explicit action labels. Wu’s most cited paper, "Integration of an actor-critic model and generative adversarial networks for a Chinese calligraphy robot" (2020, 24 citations), alongside "GANCCRobot" (2019, 21 citations), demonstrates how GANs can generate diverse, human-like writing styles. They have also advanced robotic calligraphy evaluation with a computational system using possibility-probability distribution methods (2017, 8 citations) and introduced human preference into robotic writing (2019, 6 citations). Beyond calligraphy, Wu explored developmental learning for mobile manipulators and deep learning for robot choreography. With a total of over 100 citations across their key works, Wu’s research not only pushes the boundaries of robotic dexterity and creative AI but also contributes to preserving and innovating cultural heritage through technology.
Research Focus
Key Achievements
Top Papers
- 1
- 2GANCCRobot: Generative adversarial nets based chinese calligraphy robot21 citations · 2019
- 3Towards Deep Reinforcement Learning Based Chinese Calligraphy Robot15 citations · 2018
- 4Internal Model Control Structure Inspired Robotic Calligraphy System9 citations · 2023
- 5
- 6
- 7Towards Deep Learning Based Robot Automatic Choreography System6 citations · 2019
- 8Robotic Chinese Calligraphy with Human Preference6 citations · 2019
- 9A Developmental Learning Approach of Mobile Manipulator via Playing4 citations · 2017