Mingyang Feng
Papers
1
Total Citations
3
H-Index
1
About
Mingyang Feng is a pioneering researcher in robotics and autonomous systems, with a primary focus on formal methods for robot path planning under uncertainty. His most cited work, "No-regret path planning for temporal logic tasks in partially-known environments" (2025, 3 citations), addresses a critical challenge in robotics: enabling robots to execute complex, high-level tasks—specified by co-safe linear temporal logic (scLTL) formulae—in environments where map geometry is only partially known. Feng’s key contribution lies in developing a graph-based, no-regret planning framework that allows robots to make robust decisions despite incomplete information, minimizing performance loss over time. This work bridges the gap between formal verification and practical robotics, offering provable guarantees for task completion in real-world scenarios like search-and-rescue or autonomous exploration. While his citation count is still growing, Feng’s research is notable for its theoretical rigor and practical relevance, positioning him as an emerging leader in the intersection of temporal logic, path planning, and decision-making under partial observability—a vital area for the next generation of intelligent, adaptive robots.
Research Focus
Key Achievements
Top Papers
- 1