In-Distribution Barrier Functions: Self-Supervised Policy Filters that Avoid Out-of-Distribution States

TL;DR

This paper introduces a self-supervised control filter that uses in-distribution barrier functions to keep learned policies within safe, familiar states based solely on high-dimensional visual data, enhancing safety in robotic control.

Contribution

It proposes a novel in-distribution barrier function approach that learns in a latent space from visual observations, extending control barrier functions to high-dimensional perception data.

Findings

Effective in simulation for visuomotor tasks

Works with high-dimensional visual inputs

Maintains in-distribution safety constraints

Abstract

Learning-based control approaches have shown great promise in performing complex tasks directly from high-dimensional perception data for real robotic systems. Nonetheless, the learned controllers can behave unexpectedly if the trajectories of the system divert from the training data distribution, which can compromise safety. In this work, we propose a control filter that wraps any reference policy and effectively encourages the system to stay in-distribution with respect to offline-collected safe demonstrations. Our methodology is inspired by Control Barrier Functions (CBFs), which are model-based tools from the nonlinear control literature that can be used to construct minimally invasive safe policy filters. While existing methods based on CBFs require a known low-dimensional state representation, our proposed approach is directly applicable to systems that rely solely on high-dimensional visual observations by learning in a latent state-space. We demonstrate that our method is effective for two different visuomotor control tasks in simulation environments, including both top-down and egocentric view settings.