Papers
12
Total Citations
197
H-Index
6
About
Yongbin Jin is a leading researcher in legged locomotion and dexterous robotic manipulation, whose work bridges reinforcement learning, tactile sensing, and novel actuation mechanisms. His most impactful contributions include the development of a phase-guided controller for learning free gait transitions in quadruped robots (73 citations), enabling smooth and adaptive locomotion without manual tuning. He also pioneered imitation-relaxation reinforcement learning for high-speed quadrupedal running (62 citations), demonstrating that robots can achieve agile, natural gaits through a combination of motion imitation and policy relaxation. Beyond locomotion, Jin has advanced tactile sensing with the DotView sensor (23 citations), a low-cost compact device capable of estimating pressure, shear, and torsion, and its curved successor DotTip, designed to enhance dexterous manipulation. His work on twisted-string-driven mechanisms has produced both a wheeled jumping robot and an anthropomorphic hand that approaches human-level dexterity and grasp power. Jin’s research is notable for its integration of learning-based control with mechanical innovation, yielding practical, high-performance robotic systems. With over 200 total citations and recent publications in top venues, he is shaping the future of agile, perceptive robots.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5
- 6
- 7Novel dielectric elastomer structure of soft robot6 citations · 2015
- 8
- 9
- 10