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Learning for Safety-Critical Control with Control Barrier Functions.
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TLDR
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.Abstract:
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 uncertainty remains a significant challenge in synthesizing safe controllers, leading to degradation in the properties provided by the controllers. This paper develops a machine learning framework utilizing Control Barrier Functions (CBFs) to reduce model uncertainty as it impact the safe behavior of a system. This approach iteratively collects data and updates a controller, ultimately achieving safe behavior. We validate this method in simulation and experimentally on a Segway platform.read more
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References
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Proceedings ArticleDOI
Learning-Based Model Predictive Control for Safe Exploration
TL;DR: This paper presents a learning-based model predictive control scheme that can provide provable high-probability safety guarantees and exploits regularity assumptions on the dynamics in terms of a Gaussian process prior to construct provably accurate confidence intervals on predicted trajectories.
Journal ArticleDOI
End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Tasks
TL;DR: In this article, a controller architecture that combines a model-free RL-based controller with model-based controllers utilizing control barrier functions (CBFs) and online learning of the unknown system dynamics is proposed to ensure safety during learning.
Proceedings Article
Safe Exploration for Optimization with Gaussian Processes
TL;DR: This work develops an efficient algorithm called SAFEOPT, and theoretically guarantees its convergence to a natural notion of optimum reachable under safety constraints, as well as two real applications: movie recommendation, and therapeutic spinal cord stimulation.
Proceedings ArticleDOI
Safe controller optimization for quadrotors with Gaussian processes
TL;DR: In this paper, a safe optimization algorithm, SafeOptimization, is applied to the problem of automatic controller parameter tuning for low-performance quadrotor vehicles, where the underlying performance measure is modeled as a Gaussian process and only new controller parameters whose performance lies above a safe performance threshold with high probability.
Journal ArticleDOI
Robustness of Control Barrier Functions for Safety Critical Control
TL;DR: This paper develops several important extensions to the notion of a control barrier function, including conditions for the control law obtained by solving the quadratic program to be Lipschitz continuous and therefore to gives rise to well-defined solutions of the resulting closed-loop system.