124 citations · 200 across the 25 of their papers we have counts for
27 papers
Reach-Avoid Certificates for Contact-based Stochastic Hybrid Systems
Tianyi Xia, Ryan K. Cosner
This work studies certified reach-avoid control of stochastic hybrid systems in which a stochastic differential equation governs continuous flow and a controlled stochastic kernel…
MARCH: Model-Assisted Reinforcement Learning for the Perceptive Control of Humanoids over Sparse Footholds
Codrin Crismariu, Ryan K. Cosner
Perceptive bipedal locomotion over sparse terrain remains a difficult challenge: model-based methods are precise but brittle to uncertainty, while model-free methods are robust but…
Safe-SAGE: Social-Semantic Adaptive Guidance for Safe Engagement through Laplace-Modulated Poisson Safety Functions
Lizhi Yang, Ryan M. Bena, Meg Wilkinson +4
Traditional safety-critical control methods, such as control barrier functions, suffer from semantic blindness, exhibiting the same behavior around obstacles regardless of contextu…
Risk-Aware Safety Filters with Poisson Safety Functions and Laplace Guidance Fields
Gilbert Bahati, Ryan M. Bena, Meg Wilkinson +3
Robotic systems navigating in real-world settings require a semantic understanding of their environment to properly determine safe actions. This work aims to develop the mathematic…
Probabilistic Control Barrier Functions: Safety in Probability for Discrete-Time Stochastic Systems
Pol Mestres, Blake Werner, Ryan K. Cosner +1
Control systems operating in the real world face countless sources of unpredictable uncertainties. These random disturbances can render deterministic guarantees inapplicable and ca…
Geometry-Aware Predictive Safety Filters on Humanoids: From Poisson Safety Functions to CBF Constrained MPC
Ryan M. Bena, Gilbert Bahati, Blake Werner +3
Autonomous navigation through unstructured and dynamically-changing environments is a complex task that continues to present many challenges for modern roboticists. In particular,…
Learning Safe Control via On-the-Fly Bandit Exploration
Alexandre Capone, Ryan Cosner, Aaaron Ames +1
Control tasks with safety requirements under high levels of model uncertainty are increasingly common. Machine learning techniques are frequently used to address such tasks, typica…
SHIELD: Safety on Humanoids via CBFs In Expectation on Learned Dynamics
Lizhi Yang, Blake Werner, Ryan K. Cosner +3
Robot learning has produced remarkably effective ``black-box'' controllers for complex tasks such as dynamic locomotion on humanoids. Yet ensuring dynamic safety, i.e., constraint…
Control Barrier Function Synthesis for Nonlinear Systems with Dual Relative Degree
Gilbert Bahati, Ryan K. Cosner, Max H. Cohen +2
Control barrier functions (CBFs) are a powerful tool for synthesizing safe control actions; however, constructing CBFs remains difficult for general nonlinear systems. In this work…
Constructive Safety-Critical Control: Synthesizing Control Barrier Functions for Partially Feedback Linearizable Systems
Max H. Cohen, Ryan K. Cosner, Aaron D. Ames
Certifying the safety of nonlinear systems, through the lens of set invariance and control barrier functions (CBFs), offers a powerful method for controller synthesis, provided a C…
A Constructive Method for Designing Safe Multirate Controllers for Differentially-Flat Systems
Devansh R. Agrawal, Hardik Parwana, Ryan K. Cosner +3
We present a multi-rate control architecture that leverages fundamental properties of differential flatness to synthesize controllers for safety-critical nonlinear dynamical system…
Bounding Stochastic Safety: Leveraging Freedman's Inequality with Discrete-Time Control Barrier Functions
Ryan K. Cosner, Preston Culbertson, Aaron D. Ames
When deployed in the real world, safe control methods must be robust to unstructured uncertainties such as modeling error and external disturbances. Typical robust safety methods a…
Generative Modeling of Residuals for Real-Time Risk-Sensitive Safety with Discrete-Time Control Barrier Functions
Ryan K. Cosner, Igor Sadalski, Jana K. Woo +2
A key source of brittleness for robotic systems is the presence of model uncertainty and external disturbances. Most existing approaches to robust control either seek to bound the…
Safe Online Dynamics Learning with Initially Unknown Models and Infeasible Safety Certificates
Alexandre Capone, Ryan Cosner, Aaron Ames +1
Safety-critical control tasks with high levels of uncertainty are becoming increasingly common. Typically, techniques that guarantee safety during learning and control utilize cons…
Input-to-State Stability in Probability
Preston Culbertson, Ryan K. Cosner, Maegan Tucker +1
Input-to-State Stability (ISS) is fundamental in mathematically quantifying how stability degrades in the presence of bounded disturbances. If a system is ISS, its trajectories wil…
Learning Responsibility Allocations for Safe Human-Robot Interaction with Applications to Autonomous Driving
Ryan K. Cosner, Yuxiao Chen, Karen Leung +1
Drivers have a responsibility to exercise reasonable care to avoid collision with other road users. This assumed responsibility allows interacting agents to maintain safety without…
Robust Safety under Stochastic Uncertainty with Discrete-Time Control Barrier Functions
Ryan K. Cosner, Preston Culbertson, Andrew J. Taylor +1
Robots deployed in unstructured, real-world environments operate under considerable uncertainty due to imperfect state estimates, model error, and disturbances. Given this real-wor…
End-to-End Imitation Learning with Safety Guarantees using Control Barrier Functions
Ryan K. Cosner, Yisong Yue, Aaron D. Ames
Imitation learning (IL) is a learning paradigm which can be used to synthesize controllers for complex systems that mimic behavior demonstrated by an expert (user or control algori…
Receding Horizon Planning with Rule Hierarchies for Autonomous Vehicles
Sushant Veer, Karen Leung, Ryan Cosner +3
Autonomous vehicles must often contend with conflicting planning requirements, e.g., safety and comfort could be at odds with each other if avoiding a collision calls for slamming…
Self-Supervised Online Learning for Safety-Critical Control using Stereo Vision
Ryan K. Cosner, Ivan D. Jimenez Rodriguez, Tamas G. Molnar +4
With the increasing prevalence of complex vision-based sensing methods for use in obstacle identification and state estimation, characterizing environment-dependent measurement err…
Safety of Sampled-Data Systems with Control Barrier Functions via Approximate Discrete Time Models
Andrew J. Taylor, Victor D. Dorobantu, Ryan K. Cosner +2
Control Barrier Functions (CBFs) have been demonstrated to be a powerful tool for safety-critical controller design for nonlinear systems. Existing design paradigms do not address…
Safety-Aware Preference-Based Learning for Safety-Critical Control
Ryan K. Cosner, Maegan Tucker, Andrew J. Taylor +7
Bringing dynamic robots into the wild requires a tenuous balance between performance and safety. Yet controllers designed to provide robust safety guarantees often result in conser…
Model-Free Safety-Critical Control for Robotic Systems
Tamas G. Molnar, Ryan K. Cosner, Andrew W. Singletary +2
This paper presents a framework for the safety-critical control of robotic systems, when safety is defined on safe regions in the configuration space. To maintain safety, we synthe…
Episodic Learning for Safe Bipedal Locomotion with Control Barrier Functions and Projection-to-State Safety
Noel Csomay-Shanklin, Ryan K. Cosner, Min Dai +2
This paper combines episodic learning and control barrier functions in the setting of bipedal locomotion. The safety guarantees that control barrier functions provide are only vali…