Qi Ye
Papers
1
Total Citations
2
H-Index
1
About
Qi Ye is an emerging researcher in the field of robotic manipulation and dexterous control, with a focus on bridging the gap between human and machine dexterity. Their work centers on developing intelligent systems that enable anthropomorphic multifingered robotic hands to achieve human-level manipulation capabilities — a notoriously difficult challenge in robotics due to high-dimensional action-observation spaces, complex contact dynamics, and visual occlusions during manipulation tasks. Ye's most notable contribution to date, "Visual-tactile pretraining and online multitask learning for humanlike manipulation dexterity" (2026), tackles these challenges head-on by combining multimodal sensory pretraining — integrating both visual and tactile feedback — with adaptive online multitask learning frameworks. This approach represents a significant methodological advance in how robots can learn and generalize dexterous skills in a more human-inspired manner. While still in the early stages of building a citation record, with 2 citations on their leading work, Ye's research addresses one of the most compelling frontiers in embodied AI and robotics. Students and researchers working on robot learning, tactile sensing, or manipulation policy design will find their contributions a valuable and forward-looking reference in the field.
Research Focus
Key Achievements
Top Papers
- 1