Zhaoji Huang
Papers
1
Total Citations
3
H-Index
1
About
Zhaoji Huang is a rising researcher in the field of robotic perception and manipulation, with a focus on visuo-tactile fusion for intelligent grasping. His work addresses a fundamental challenge in robotics: enabling machines to detect in-hand object slip with the same reliability as humans. Huang’s most cited paper, “Visuo-Tactile-Based Slip Detection Using A Multi-Scale Temporal Convolution Network” (2023, 3 citations), introduces a novel deep neural network that integrates visual and tactile data to improve slip detection accuracy. This contribution is critical for advancing dexterous robotic manipulation, with applications in manufacturing, prosthetics, and human-robot interaction. While his citation count is still growing, Huang’s innovative approach to multi-modal sensory fusion has already garnered attention in the robotics community. His work stands out for its practical relevance and potential to enhance robotic autonomy in dynamic environments. As an early-career researcher, Huang is laying the groundwork for more robust and adaptive robotic systems, making him a promising figure to watch in the field of embodied AI and sensorimotor control.
Research Focus
Key Achievements
Top Papers
- 1