Papers
6
Total Citations
95
H-Index
4
About
Kehan Long is a robotics and control systems researcher whose work sits at the intersection of safety-critical control, robot motion planning, and machine learning. He is best known for advancing the theory and application of Control Barrier Functions (CBFs), developing innovative methods to construct and learn these functions online to guarantee safe robot navigation even under uncertain or dynamically changing conditions. His 2021 paper, "Learning Barrier Functions With Memory for Robust Safe Navigation," has garnered 52 citations and stands as his most influential contribution, introducing a data-driven approach that addresses a previously underexplored challenge: synthesizing safe controllers when barrier functions must be built in real time amid uncertainty. His 2022 follow-up extended this framework to handle both probabilistic and worst-case uncertainty in system dynamics and constraints, earning 26 citations. Long's research also spans multi-agent robotic communication, distributionally robust optimization for dynamic environments, continuum robot modeling via neural distance fields, and safe navigation for rigid-body robots among elliptical obstacles. Together, his growing body of work reflects a sustained commitment to making autonomous robotic systems provably safe, adaptable, and deployable in complex real-world settings.
Research Focus
Key Achievements
Top Papers
- 1Learning Barrier Functions With Memory for Robust Safe Navigation52 citations · 2021
- 2Safe Control Synthesis With Uncertain Dynamics and Constraints26 citations · 2022
- 3
- 4
- 5Neural Configuration Distance Function for Continuum Robot Control2 citations · 2025
- 6