Safe Online Dynamics Learning with Initially Unknown Models and Infeasible Safety Certificates
Safety-critical control tasks with high levels of uncertainty are becoming increasingly common. Typically, techniques that guarantee safety during learning and control utilize constraint-based safety certificates, which can be leveraged to compute safe control inputs. However, excessive model uncertainty can render robust safety certification methods or infeasible, meaning no control input satisfies the constraints imposed by the safety certificate. This paper considers a learning-based setting with a robust safety certificate based on a control barrier function (CBF) second-order cone program. If the control barrier function certificate is feasible, our approach leverages it to guarantee safety. Otherwise, our method explores the system dynamics to collect data and recover the feasibility of the control barrier function constraint. To this end, we employ a method inspired by well-established tools from Bayesian optimization. We show that if the sampling frequency is high enough, we recover the feasibility of the robust CBF certificate, guaranteeing safety. Our approach requires no prior model and corresponds, to the best of our knowledge, to the first algorithm that guarantees safety in settings with occasionally infeasible safety certificates without requiring a backup non-learning-based controller.
Code (0)
등록된 구현이 없습니다.
Tasks
Bayesian OptimizationSimilar Papers 제목 키워드 기반
Safe Active Dynamics Learning and Control: A Sequential Exploration-Exploitation Framework
Safe deployment of autonomous robots in diverse scenarios requires agents that are capable of efficiently adapting to new environments while satisfying constraints. In this work, we propose a practical and theoretically-…
Meta-LearningMeta Reinforcement LearningLearning k-Inductive Control Barrier Certificates for Unknown Nonlinear Dynamics Beyond Polynomials
This work is concerned with synthesizing safety controllers for discrete-time nonlinear systems beyond polynomials with unknown mathematical models using the notion of k-inductive control barrier certificates (k-CBCs). C…
LEMMASafe Exploration for Identifying Linear Systems via Robust Optimization
Safely exploring an unknown dynamical system is critical to the deployment of reinforcement learning (RL) in physical systems where failures may have catastrophic consequences. In scenarios where one knows little about t…
Reinforcement LearningReinforcement Learning (RL)Safe ExplorationNeural Koopman Control Barrier Functions for Safety-Critical Control of Unknown Nonlinear Systems
We consider the problem of synthesis of safe controllers for nonlinear systems with unknown dynamics using Control Barrier Functions (CBF). We utilize Koopman operator theory (KOT) to associate the (unknown) nonlinear sy…
validNeural Differentiable Integral Control Barrier Functions for Unknown Nonlinear Systems with Input Constraints
In this paper, we propose a deep learning based control synthesis framework for fast and online computation of controllers that guarantees the safety of general nonlinear control systems with unknown dynamics in the pres…
Imitation Learning