220 citations · 545 across the 21 of their papers we have counts for
21 papers
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…
Control Barrier Functions and Input-to-State Safety with Application to Automated Vehicles
Anil Alan, Andrew J. Taylor, Chaozhe R. He +2
Balancing safety and performance is one of the predominant challenges in modern control system design. Moreover, it is crucial to robustly ensure safety without inducing unnecessar…
Safe Backstepping with Control Barrier Functions
Andrew J. Taylor, Pio Ong, Tamas G. Molnar +1
Complex control systems are often described in a layered fashion, represented as higher-order systems where the inputs appear after a chain of integrators. While Control Barrier Fu…
Multi-Rate Planning and Control of Uncertain Nonlinear Systems: Model Predictive Control and Control Lyapunov Functions
Noel Csomay-Shanklin, Andrew J. Taylor, Ugo Rosolia +1
Modern control systems must operate in increasingly complex environments subject to safety constraints and input limits, and are often implemented in a hierarchical fashion with di…
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…
Planar Bipedal Locomotion with Nonlinear Model Predictive Control: Online Gait Generation using Whole-Body Dynamics
Manuel Y. Galliker, Noel Csomay-Shanklin, Ruben Grandia +4
The ability to generate dynamic walking in real-time for bipedal robots with input constraints and underactuation has the potential to enable locomotion in dynamic, complex and uns…
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…
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…
Measurement-Robust Control Barrier Functions: Certainty in Safety with Uncertainty in State
Ryan K. Cosner, Andrew W. Singletary, Andrew J. Taylor +3
The increasing complexity of modern robotic systems and the environments they operate in necessitates the formal consideration of safety in the presence of imperfect measurements.…
Sampled-Data Stabilization with Control Lyapunov Functions via Quadratically Constrained Quadratic Programs
Andrew J. Taylor, Victor D. Dorobantu, Yisong Yue +2
Controller design for nonlinear systems with Control Lyapunov Function (CLF) based quadratic programs has recently been successfully applied to a diverse set of difficult control t…
Safe Controller Synthesis with Tunable Input-to-State Safe Control Barrier Functions
Anil Alan, Andrew J. Taylor, Chaozhe R. He +2
To bring complex systems into real world environments in a safe manner, they will have to be robust to uncertainties - both in the environment and the system. This paper investigat…
Towards Robust Data-Driven Control Synthesis for Nonlinear Systems with Actuation Uncertainty
Andrew J. Taylor, Victor D. Dorobantu, Sarah Dean +3
Modern nonlinear control theory seeks to endow systems with properties such as stability and safety, and has been deployed successfully across various domains. Despite this success…
Multi-Layered Safety for Legged Robots via Control Barrier Functions and Model Predictive Control
Ruben Grandia, Andrew J. Taylor, Aaron D. Ames +1
The problem of dynamic locomotion over rough terrain requires both accurate foot placement together with an emphasis on dynamic stability. Existing approaches to this problem prior…
Guaranteeing Safety of Learned Perception Modules via Measurement-Robust Control Barrier Functions
Sarah Dean, Andrew J. Taylor, Ryan K. Cosner +2
Modern nonlinear control theory seeks to develop feedback controllers that endow systems with properties such as safety and stability. The guarantees ensured by these controllers o…
Nonlinear Model Predictive Control of Robotic Systems with Control Lyapunov Functions
Ruben Grandia, Andrew J. Taylor, Andrew Singletary +2
The theoretical unification of Nonlinear Model Predictive Control (NMPC) with Control Lyapunov Functions (CLFs) provides a framework for achieving optimal control performance while…
A Control Barrier Perspective on Episodic Learning via Projection-to-State Safety
Andrew J. Taylor, Andrew Singletary, Yisong Yue +1
In this paper we seek to quantify the ability of learning to improve safety guarantees endowed by Control Barrier Functions (CBFs). In particular, we investigate how model uncertai…
Safety-Critical Event Triggered Control via Input-to-State Safe Barrier Functions
Andrew J. Taylor, Pio Ong, Jorge Cortes +1
The efficient utilization of available resources while simultaneously achieving control objectives is a primary motivation in the event-triggered control paradigm. In many modern c…
Learning for Safety-Critical Control with Control Barrier Functions
Andrew Taylor, Andrew Singletary, Yisong Yue +1
Modern nonlinear control theory seeks to endow systems with properties of stability and safety, and have been deployed successfully in multiple domains. Despite this success, model…
Adaptive Safety with Control Barrier Functions
Andrew J. Taylor, Aaron D. Ames
Adaptive Control Lyapunov Functions (aCLFs) were introduced 20 years ago, and provided a Lyapunov-based methodology for stabilizing systems with parameter uncertainty. The goal of…
A Control Lyapunov Perspective on Episodic Learning via Projection to State Stability
Andrew J. Taylor, Victor D. Dorobantu, Meera Krishnamoorthy +3
The goal of this paper is to understand the impact of learning on control synthesis from a Lyapunov function perspective. In particular, rather than consider uncertainties in the f…
Episodic Learning with Control Lyapunov Functions for Uncertain Robotic Systems
Andrew J. Taylor, Victor D. Dorobantu, Hoang M. Le +2
Many modern nonlinear control methods aim to endow systems with guaranteed properties, such as stability or safety, and have been successfully applied to the domain of robotics. Ho…