Model-Assisted Probabilistic Safe Adaptive Control With Meta-Bayesian Learning

TL;DR

This paper introduces a novel adaptive safe control framework combining meta learning, Bayesian models, and control barrier functions to ensure probabilistic safety in uncertain control tasks, with theoretical guarantees and practical validation.

Contribution

It develops a unified Bayesian adaptive control method with meta-learned priors and confidence bounds, enhancing safety and efficiency in uncertain environments.

Findings

Significantly improves Bayesian model-based CBF safety methods

Enables efficient safe exploration with multiple uncertainties

Provides theoretical safety guarantees during control processes

Abstract

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.