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Andrew J. Taylor
Researcher at California Institute of Technology
Publications - 29
Citations - 667
Andrew J. Taylor is an academic researcher from California Institute of Technology. The author has contributed to research in topics: Control theory & Computer science. The author has an hindex of 9, co-authored 23 publications receiving 273 citations. Previous affiliations of Andrew J. Taylor include University of California, Berkeley & ETH Zurich.
Papers
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Proceedings ArticleDOI
Adaptive Safety with Control Barrier Functions
Andrew J. Taylor,Aaron D. Ames +1 more
TL;DR: In this paper, adaptive control Lyapunov functions (aCLFs) and adaptive Control Barrier Functions (aCBFs) are combined into a single control methodology for systems with uncertain parameters in the context of a Quadratic Program (QP) based framework.
Posted Content
Learning for Safety-Critical Control with Control Barrier Functions.
TL;DR: A machine learning framework utilizing Control Barrier Functions (CBFs) to reduce model uncertainty as it impact the safe behavior of a system, ultimately achieving safe behavior.
Learning for Safety-Critical Control with Control Barrier Functions
TL;DR: In this paper, a machine learning framework utilizing Control Barrier Functions (CBFs) was developed to reduce model uncertainty as it impacts the safe behavior of a Segway system, and the approach iteratively collects data and updates a controller, ultimately achieving safe behavior.
Proceedings ArticleDOI
Episodic Learning with Control Lyapunov Functions for Uncertain Robotic Systems
TL;DR: A machine learning framework centered around Control Lyapunov Functions to adapt to parametric uncertainty and unmodeled dynamics in general robotic systems and yields a stabilizing quadratic program model-based controller.
Journal ArticleDOI
Safe Controller Synthesis With Tunable Input-to-State Safe Control Barrier Functions
TL;DR: In this article, the concept of tunable input-to-state safe control barrier functions (TISSf-CBFs) is introduced to guarantee safety for disturbances that vary with state and, therefore, provide less conservative means of accommodating uncertainty.