Papers
5
Total Citations
64
H-Index
4
About
Wei Qi Toh is a robotics researcher whose work bridges the gap between creative expression and intelligent manipulation. His primary research areas include human-robot interaction, robotic drawing, and visuo-tactile perception for object manipulation. Toh is best known for developing **RoboCoDraw**, a real-time collaborative robotic drawing system that uses Generative Adversarial Networks (GANs) for style transfer and time-efficient path optimization, enabling robots to create stylized human face sketches interactively. This work, with over 30 combined citations, showcases his ability to blend AI with artistic robotics. His most impactful contribution, however, is in **visuo-tactile feedback-based manipulation**, where his 2023 paper (24 citations) introduces novel methods for object packing by integrating visual and tactile sensory data to handle perceptual uncertainty. Toh has also advanced **deep model fusion reinforcement learning** for efficient robotic task generalization, reducing the need for extensive retraining across different environments. His research demonstrates a unique ability to combine deep learning, sensory fusion, and real-time control, making him a notable figure in the development of more adaptive and interactive robotic systems for both industrial and entertainment applications.
Research Focus
Key Achievements
Top Papers
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- 2Visuo-Tactile Feedback-Based Robot Manipulation for Object Packing24 citations · 2023
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