Papers
2
Total Citations
23
H-Index
2
About
Zeji Yi is an emerging researcher working at the intersection of robotics, control systems, and human-robot interaction. His work spans two compelling domains: advanced locomotion control for legged robots and assistive rehabilitation robotics, demonstrating a breadth of expertise that bridges high-performance autonomous systems with human-centered applications. In his most notable recent work, Yi tackles one of robotics' most formidable challenges — real-time optimal control for legged robots. His 2025 paper on full-order sampling-based Model Predictive Control (MPC) introduces a diffusion-style annealing approach to overcome the high dimensionality and non-convexity that traditionally force researchers to rely on simplified, reduced-order models. This contribution represents a meaningful step toward more capable and physically realistic locomotion control, already garnering 12 citations since publication. Yi's earlier work in rehabilitation robotics reflects an equally impactful humanitarian vision. His 2023 research on adaptive learning-based upper-limb rehabilitation systems demonstrates how collaborative robots can expand access to motor disability treatment beyond specialized clinical settings, making home-based therapy more feasible and affordable — earning 11 citations from the rehabilitation and HRI communities. Together, these contributions position Yi as a versatile and promising voice in modern robotics research.
Research Focus
Key Achievements
Top Papers
- 1
- 2