Incremental Composition of Learned Control Barrier Functions in Unknown Environments

TL;DR

This paper introduces a method for safely exploring unknown environments by incrementally composing a global control barrier function from locally-learned functions, ensuring safety during exploration.

Contribution

It presents a novel approach to incrementally build a global CBF from local CBFs parameterized by radial basis functions, enabling safe exploration in unknown environments.

Findings

Successfully explores unknown environments in simulations

Maintains safety throughout the exploration process

Demonstrates growth of safe set over time

Abstract

We consider the problem of safely exploring a static and unknown environment while learning valid control barrier functions (CBFs) from sensor data. Existing works either assume known environments, target specific dynamics models, or use a-priori valid CBFs, and are thus limited in their safety guarantees for general systems during exploration. We present a method for safely exploring the unknown environment by incrementally composing a global CBF from locally-learned CBFs. The challenge here is that local CBFs may not have well-defined end behavior outside their training domain, i.e. local CBFs may be positive (indicating safety) in regions where no training data is available. We show that well-defined end behavior can be obtained when local CBFs are parameterized by compactly-supported radial basis functions. For learning local CBFs, we collect sensor data, e.g. LiDAR capturing obstacles in the environment, and augment it with simulated data from a safe oracle controller. Our work complements recent efforts to learn CBFs from safe demonstrations -- where learned safe sets are limited to their training domains -- by demonstrating how to grow the safe set over time as more data becomes available. We evaluate our approach on two simulated systems, where our method successfully explores an unknown environment while maintaining safety throughout the entire execution.