Ryan K. Cosner
Papers
13
Total Citations
149
H-Index
9
About
Ryan K. Cosner is a robotics and control theory researcher whose work sits at the intersection of safety-critical control, uncertainty quantification, and real-world robotic systems. His primary contributions center on extending **Control Barrier Functions (CBFs)** — a mathematical framework for guaranteeing system safety — to operate robustly under the imperfect conditions that characterize real-world deployment. His 2021 paper on Measurement-Robust Control Barrier Functions (34 citations) established a rigorous foundation for safety guarantees when state estimates are erroneous, while his 2023 work on stochastic uncertainty with discrete-time CBFs (30 citations) further advanced probabilistic safety assurances for robots facing model error and disturbances. Cosner's research spans bipedal locomotion, autonomous driving, and vision-based control, demonstrating a commitment to translating theoretical guarantees into practical systems. His contributions to multirate controller design for differentially-flat systems, self-supervised learning for stereo vision, and generative modeling of residual dynamics collectively reflect a sophisticated approach to bridging learning and formal control methods. His work on Input-to-State Stability in probability and responsibility allocation for human-robot interaction highlights his breadth across foundational theory and applied autonomy, making him a notable emerging voice in safe, uncertainty-aware robotics research.
Research Focus
Key Achievements
Top Papers
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- 5Input-to-State Stability in Probability10 citations · 2023
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- 10Safety-Aware Preference-Based Learning for Safety-Critical Control5 citations · 2021