Safe Multi-Agent Interaction through Robust Control Barrier Functions with Learned Uncertainties

TL;DR

This paper develops a robust multi-agent control barrier function framework that learns and incorporates uncertainties in agent dynamics, ensuring safety in real-world multi-agent robot interactions.

Contribution

It introduces a method to learn high-confidence bounds on dynamic uncertainties and integrates them into a robust CBF framework for safe multi-agent interactions.

Findings

Robust CBF maintains safety with higher probability.

Simulation shows nominal CBF often violated during interactions.

Proposed quadratic program is computationally efficient.

Abstract

Robots operating in real world settings must navigate and maintain safety while interacting with many heterogeneous agents and obstacles. Multi-Agent Control Barrier Functions (CBF) have emerged as a computationally efficient tool to guarantee safety in multi-agent environments, but they assume perfect knowledge of both the robot dynamics and other agents' dynamics. While knowledge of the robot's dynamics might be reasonably well known, the heterogeneity of agents in real-world environments means there will always be considerable uncertainty in our prediction of other agents' dynamics. This work aims to learn high-confidence bounds for these dynamic uncertainties using Matrix-Variate Gaussian Process models, and incorporates them into a robust multi-agent CBF framework. We transform the resulting min-max robust CBF into a quadratic program, which can be efficiently solved in real time. We verify via simulation results that the nominal multi-agent CBF is often violated during agent interactions, whereas our robust formulation maintains safety with a much higher probability and adapts to learned uncertainties