Research

At a high-level my research focuses on controls and machine learning for real-world robot safety.

For an in-depth understanding of my research, check out my thesesi: Dynamic Safety Under Uncertainty: A Control Barrier Function Approach or the recording of my thesis defense presentation:

Here is a list of selected works. Please see my google scholar page for the most up-to-date list of publications.

Publications

L-CSS 2024: Bounding Stochastic Safety:: Leveraging Freedman's INequality with Discrete-Time Control Barrier Functions
Ryan K. Cosner^*, Preston Culbertson, and Aaron D. Ames.
L-CSS 2024: Bounding Stochastic Safety:: Leveraging Freedman's INequality with Discrete-Time Control Barrier Functions
Max H. Cohen, Ryan K. Cosner^*, and Aaron D. Ames.
ICRA 2024: Generative Modeling of Residuals for Real-Time Risk-Sensitive Safety with Discrete-Time Control Barrier Functions
Ryan K. Cosner^*, Igor Sadalski, Jana K. Woo, Preston Culbertson, and Aaron D. Ames.
ACC 2024: Safe Dynamics Learning with Initially Infeasible Safety Certificates (Under Review)
Alexandre Capone, Ryan K. Cosner^*, Aaron D. Ames, and Sandra Hirche.
CDC 2023: Input-to-State Stability in Probability
Preston Culbertson, Ryan K. Cosner^*, Maegan Tucker, and Aaron D. Ames. [pdf]
RSS 2023: Robust Safety under Stochastic Uncertainty with Discrete-Time Control Barrier Functions
Ryan K. Cosner^*, Preston Culbertson, Andrew J. Taylor, and Aaron D. Ames. [pdf]
ICRA 2023: Learning Responsibility Allocations for Safe Human-Robot Interaction with Applications to Autonomous Driving
Ryan K. Cosner^*, Yuxiao Chen, Karen Leung, and Marco Pavone. [pdf]
ICRA 2023: Receding Horizon Planning with Rule Hierarchies for Autonomous Vehicles
Shushant Veer, Karen Leung, Ryan K. Cosner^*, Yuxiao Chen, Peter Karkus, and Marco Pavone. [pdf]
CDC 2022: End-to-End Imitation Learning with Safety Guarantees using Control Barrier Functions
Ryan K. Cosner^*, Yisong Yue, Aaron D. Ames. [pdf]
CDC 2022: Safety of Sampled-Data Systems with Control Barrier Functions via Approximate Discrete Time Models
Adrew J. Taylor^*, Victor D. Dorobantu^*, Ryan K. Cosner^*, Yisong Yue, Aaron D. Ames. [pdf]
L4DC 2022: Safety-Aware Preference-Based Learning for Safety-Critical Control
Ryan K. Cosner^*, Maegan Tucker, Andrew J. Taylor, Kejun (Amy) Li, Tamas G. Molnar, Wyatt Ubellacker, Anil Alan, Gabor Orosz, Yisong Yue, Aaron D. Ames. [pdf]
ICRA 2022: Self-Supervised Online Learning for Safety-Critical Control using Stereo Vision
Ryan K. Cosner^*, Ivan D. Jimenez-Rodriguez^*, Tamas G. Molner, Wyatt Ubellacker, Yisong Yu, Aaron D. Ames, Katherine L. Bouman
L-CSS 2022: A Constructive Method for Designing Safe Multirate Controllers for Differentially-Flat Systems
Devansh R. Agrawal^*, Hardik Parwana, Ryan K. Cosner^*, Ugo Rosolia, Aaron D. Ames, Dmitra Panagou.
RAL 2022: Model-Free Safety-Critical Control for Robotic Systems
Tamas G. Molnar, Ryan K. Cosner, Andrew W. Singletary, Wyatt Ubellacker, Aaron D. Ames. [pdf]
IROS 2021: Measurement-Robust Control Barrier Functions: Certainty in Safety with Uncertainty in State
Ryan K. Cosner, Andrew W. Singletary, Andrew J Taylor, Tamas G. Molnar, Katherine L. Bouman, Aaron D. Ames. [pdf]
L-CSS 2021: Multi-rate control design under input constraints via fixed-time barrier functions
Kunal Garg, Ryan K. Cosner, Ugo Rosolia, Aaron D. Ames, Dmitra Panagou. [pdf]
L4DC 2021: Episodic Learning for Safe Bipedal Locomotion with Control Barrier Functions and Projection-to-State Safety
Noel Csomay-Shanklin^*, Ryan K. Cosner^*, Min Dai^*, Andrew J. Taylor, Aaron D. Ames. [pdf]
CoRL 2020: Guaranteeing Safety of Learned Perception Modules via Measurement-Robust Control Barrier Functions
Sarah Dean, Andrew J. Taylor, Ryan K Cosner, Benjamin Recht, Aaron D. Ames. [pdf]