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Learning for Safety-Critical Control with Control Barrier Functions.
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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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Journal ArticleDOI
Model-Free Safe Reinforcement Learning Through Neural Barrier Certificate
TL;DR: In this paper , the authors proposed a model-free safe RL algorithm that achieves near-zero constraint violations with high rewards by jointly learning a policy and a neural barrier certificate under stepwise state constraint setting.
Proceedings ArticleDOI
Self-Supervised Online Learning for Safety-Critical Control using Stereo Vision
Ryan K. Cosner,Ivan D. Rodriguez,Tamas G. Molnar,Wyatt Ubellacker,Yisong Yue,Aaron D. Ames,Katherine L. Bouman +6 more
TL;DR: An algorithm is proposed that exploits the structure of stereo-vision to learn an uncertainty estimate without the need for ground-truth data, and is adapted online to new visual environments, wherein this estimate is leveraged in a safety-critical controller in a robust fashion.
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Unsupervised Joint $k$-node Graph Representations with Compositional Energy-Based Models.
TL;DR: This work proposes MHM-GNN, an inductive unsupervised graph representation approach that combines joint $k-node representations with energy-based models (hypergraph Markov networks) and GNNs and endow the optimization with a loss upper bound using a finite-sample unbiased Markov Chain Monte Carlo estimator.
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Robust Adaptive Control Barrier Functions: An Adaptive & Data-Driven Approach to Safety (Extended Version)
TL;DR: A new framework is developed for control of constrained nonlinear systems with structured parametric uncertainties that is less expensive than nonlinear model predictive control as it does not require a full desired trajectory, but rather only a desired terminal state.
Journal ArticleDOI
Formal Verification of Unknown Discrete- and Continuous-Time Systems: A Data-Driven Approach
TL;DR: In this article , the authors proposed a formal verification scheme for both discrete-and continuous-time deterministic systems with unknown mathematical models, where the main target is to verify the safety of unknown systems based on the construction of barrier certificates via a set of data collected from trajectories of systems while providing an a-priori guaranteed confidence on the safety.
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