Andrew J. Taylor — accepted AI-conference papers

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

IROS 2021poster

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. In this paper we propose a rigorous framework for safety-critical control of systems with erroneous state estimates. We…

Cited by 47 Source ↗Scholar ↗

2021

Multi-Layered Safety for Legged Robots via Control Barrier Functions and Model Predictive Control

Ruben Grandia, Andrew J. Taylor, Aaron D. Ames, Marco Hutter

ICRA 2021poster

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 prioritize immediate safe foot placement over longer term dynamic stability considerations, or relegate the coordination of f…

Cited by 168 Source ↗Scholar ↗

2019

Episodic Learning with Control Lyapunov Functions for Uncertain Robotic Systems

Andrew J. Taylor, Victor D. Dorobantu, Hoang M. Le, Yisong Yue, Aaron D. Ames

IROS 2019poster

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. However, model uncertainty remains a persistent challenge, weakening theoretical guarantees and causing implementation fai…

Cited by 91 Source ↗Scholar ↗