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Learning for Safety-Critical Control with Control Barrier Functions.
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
Citations
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Sample-efficient Safe Learning for Online Nonlinear Control with Control Barrier Functions
TL;DR: This paper proposes a provably sample efficient episodic safe learning framework for online control tasks that leverages safe exploration and exploitation in an unknown, nonlinear dynamical system to achieve provable high-probability safety under uncertainty during model learning.
Proceedings ArticleDOI
Guiding Reinforcement Learning with Shared Control Templates
Abhishek Padalkar,Gabriel Quere,Franz Steinmetz,Antonin Raffin,Matthias Nieuwenhuisen,Joao Silv'erio,Freek Stulp +6 more
TL;DR: In this article , the authors show that constraint representations for shared control -in particular Shared Control Templates (SCTs) - are ideally suited for safely guiding RL and demonstrate this in simulation and on a real robot, where learning the task requires only 65 episodes in 16 minutes.
Proceedings ArticleDOI
Safety-Critical Control of Nonlinear Systems via New Exponential Control Barrier Functions
TL;DR: In this article, a new exponential control barrier function (NECBF) approach was proposed where the constraints with an arbitrarily high relative degree are expressed as equivalent constraints with a relative degree 1.
Journal ArticleDOI
Safely Learning Dynamical Systems
TL;DR: In this paper , the authors formulate a mathematical definition of what it means to safely learn a dynamical system by sequentially deciding where to initialize the next trajectory, and present a linear programming-based algorithm that either safely recovers the true dynamics from at most $n$ trajectories or certifies that safe learning is impossible.
Journal ArticleDOI
Run Time Assurance for Autonomous Spacecraft Inspection
TL;DR: In this article , the authors developed several translational motion safety constraints for a multi-agent autonomous spacecraft inspection problem, where all of these constraints can be enforced with Run Time Assurance (RTA).
References
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TL;DR: In this paper, the Third Edition of the Third edition of Linear Systems: Local Theory and Nonlinear Systems: Global Theory (LTLT) is presented, along with an extended version of the second edition.
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A Distribution-Free Theory of Nonparametric Regression
TL;DR: How to Construct Nonparametric Regression Estimates * Lower Bounds * Partitioning Estimates * Kernel Estimates * k-NN Estimates * Splitting the Sample * Cross Validation * Uniform Laws of Large Numbers
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TL;DR: In this article, the authors compare Linear vs. Nonlinear Control of Differential Geometry with Linearization by State Feedback (LSF) by using Linearization and Geometric Non-linear Control (GNC).