Safe learning in robotics: From learning-based control to safe reinforcement learning
The last half decade has seen a steep rise in the number of contributions on safe learning
methods for real-world robotic deployments from both the control and reinforcement learning …
methods for real-world robotic deployments from both the control and reinforcement learning …
Adaptive tracking control with global performance for output-constrained MIMO nonlinear systems
In this article, a novel adaptive tracking control technique is developed for multiple-input-
multiple-output nonlinear systems with model uncertainty and under output constraints …
multiple-output nonlinear systems with model uncertainty and under output constraints …
Data-driven safety filters: Hamilton-jacobi reachability, control barrier functions, and predictive methods for uncertain systems
Today's control engineering problems exhibit an unprecedented complexity, with examples
including the reliable integration of renewable energy sources into power grids, safe …
including the reliable integration of renewable energy sources into power grids, safe …
Adaptive control barrier functions
It has been shown that optimizing quadratic costs while stabilizing affine control systems to
desired (sets of) states subject to state and control constraints can be reduced to a sequence …
desired (sets of) states subject to state and control constraints can be reduced to a sequence …
Toward model-free safety-critical control with humans in the loop
This vision article shows how to build on the framework of event-triggered Control Barrier
Functions (CBFs) to design model-free controllers for safety-critical multi-agent systems with …
Functions (CBFs) to design model-free controllers for safety-critical multi-agent systems with …
Control barriers in bayesian learning of system dynamics
This article focuses on learning a model of system dynamics online, while satisfying safety
constraints. Our objective is to avoid offline system identification or hand-specified models …
constraints. Our objective is to avoid offline system identification or hand-specified models …
Safe multi-agent interaction through robust control barrier functions with learned uncertainties
Robots operating in real world settings must navigate and maintain safety while interacting
with many heterogeneous agents and obstacles. Multi-Agent Control Barrier Functions …
with many heterogeneous agents and obstacles. Multi-Agent Control Barrier Functions …
Control barrier functions for stochastic systems
A Clark - Automatica, 2021 - Elsevier
Abstract Control Barrier Functions (CBFs) aim to ensure safety by constraining the control
input at each time step so that the system state remains within a desired safe region. This …
input at each time step so that the system state remains within a desired safe region. This …
Multi-robot collision avoidance under uncertainty with probabilistic safety barrier certificates
Safety in terms of collision avoidance for multi-robot systems is a difficult challenge under
uncertainty, non-determinism, and lack of complete information. This paper aims to propose …
uncertainty, non-determinism, and lack of complete information. This paper aims to propose …
[BUKU][B] Safe autonomy with control barrier functions: Theory and applications
This book presents the concept of Control Barrier Function (CBF), which captures the
evolution of safety requirements during the execution of a system and can be used to …
evolution of safety requirements during the execution of a system and can be used to …