Model-Assisted Probabilistic Safe Adaptive Control With Meta-Bayesian Learning
Not on the map. Why?
Abstract (arXiv)
Breaking safety constraints in control systems can lead to potential risks, resulting in unexpected costs or catastrophic damage. Nevertheless, uncertainty is ubiquitous, even among similar tasks. In this paper, we develop a novel adaptive safe control framework that integrates meta learning, Bayesian models, and control barrier function (CBF) method. Specifically, with the help of CBF method, we learn the inherent and external uncertainties by a unified adaptive Bayesian linear regression (ABLR) model, which consists of a forward neural network (NN) and a Bayesian output layer. Meta learning techniques are leveraged to pre-train the NN weights and priors of the ABLR model using data collected from historical similar tasks. For a new control task, we refine the meta-learned models using a few samples, and introduce pessimistic confidence bounds into CBF constraints to ensure safe control. Moreover, we provide theoretical criteria to guarantee probabilistic safety during the control processes. To validate our approach, we conduct comparative experiments in various obstacle avoidance scenarios. The results demonstrate that our algorithm significantly improves the Bayesian model-based CBF method, and is capable for efficient safe exploration even with multiple uncertain constraints.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 34 of its 53 references that are in the map. First posted on arXiv 3 Jul 2023.
Builds on
34 of 53 references.
References · 34
- 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
- 2016CVXPY: A Python-Embedded Modeling Language for Convex Optimizationcited by 633
- 2017Sim-to-Real Transfer of Robotic Control with Dynamics Randomizationcited by 616
- 2017Safe Model-based Reinforcement Learning with Stability Guaranteescited by 347
- 2021Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learningcited by 337
- 2015Scalable Bayesian Optimization Using Deep Neural Networkscited by 284
- 2016Control Barrier Function Based Quadratic Programs for Safety Critical Systemscited by 261
- 2019End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Taskscited by 256
- 2017Hamilton-Jacobi Reachability: A Brief Overview and Recent Advancescited by 186
- 2018Recasting Gradient-Based Meta-Learning as Hierarchical Bayescited by 180
- 2018Learning-based Model Predictive Control for Safe Explorationcited by 116
- 2017Trial without Error: Towards Safe Reinforcement Learning via Human Interventioncited by 102
22 more
- 2019Learning for Safety-Critical Control with Control Barrier Functionscited by 66
- 2018Meta-Learning Priors for Efficient Online Bayesian Regressioncited by 47
- 2020Cautious Adaptation For Reinforcement Learning in Safety-Critical Settingscited by 42
- 2021Adaptive-Control-Oriented Meta-Learning for Nonlinear Systemscited by 38
- 2019Adaptive Safety with Control Barrier Functionscited by 25
- 2019Robust Regression for Safe Exploration in Controlcited by 24
- 2021Meta-Learning Reliable Priors in the Function Spacecited by 23
- 2018PAC-Bayes Control: Learning Policies that Provably Generalize to Novel Environmentscited by 21
- 2019Probabilistic model predictive safety certification for learning-based controlcited by 20
- 2019Bayesian Learning-Based Adaptive Control for Safety Critical Systemscited by 19
- 2020Safe Active Dynamics Learning and Control: A Sequential Exploration-Exploitation Frameworkcited by 19
- 2020Control Barriers in Bayesian Learning of System Dynamicscited by 17
- 2021Pointwise Feasibility of Gaussian Process-based Safety-Critical Control under Model Uncertaintycited by 13
- 2022Bayesian Multi-Task Learning MPC for Robotic Mobile Manipulationcited by 13
- 2023Online Control Barrier Functions for Decentralized Multi-Agent Navigationcited by 10
- 2022Learning Differentiable Safety-Critical Control using Control Barrier Functions for Generalization to Novel Environmentscited by 8
- 2022Provable Safe Reinforcement Learning with Binary Feedbackcited by 7
- 2020Learning Control Barrier Functions with High Relative Degree for Safety-Critical Controlcited by 6
- 2022Barrier Bayesian Linear Regression: Online Learning of Control Barrier Conditions for Safety-Critical Control of Uncertain Systemscited by 4
- 2021Robust Adaptive Safety-Critical Control for Unknown Systems with Finite-Time Element-Wise Parameter Estimationcited by 2
- 2022Recursively Feasible Probabilistic Safe Online Learning with Control Barrier Functionscited by 2
- 2020A Fixed-Time Stable Adaptation Law for Safety-Critical Control under Parametric Uncertaintycited by 1
Built on by
Citing papers per year: 2026: 1.
1 paper.