Papers
3
Total Citations
22
H-Index
3
About
Teng Xue is a robotics researcher advancing the frontiers of autonomous manipulation, with a focus on non-prehensile and in-hand object control. Their work tackles some of the most complex challenges in robot dexterity: underactuation, hybrid contact dynamics, and long-horizon planning under uncertainty. Xue’s 2023 paper on demonstration-guided optimal control for long-term planar manipulation (9 citations) introduces a method to determine both continuous and discrete contact configurations—a critical step toward enabling robots to push, slide, and pivot objects over extended periods. In 2024, they proposed a logic learning framework from demonstrations (7 citations) that allows robots to generalize and react to disturbances in dynamic environments for multi-step tasks, bridging the gap between imitation and robust autonomy. Xue also contributed to hardware design with the Roller Grasper V3 (6 citations), a non-anthropomorphic grasper featuring steerable rollers on each fingertip, enabling sophisticated in-hand manipulation. This work exemplifies their commitment to integrating novel mechanisms with intelligent control. With a growing citation footprint and a focus on real-world applicability, Teng Xue is a rising voice in robotic manipulation, offering practical solutions for tasks that demand both precision and adaptability.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3Design and Control of Roller Grasper V3 for In-Hand Manipulation6 citations · 2024