Learning responsibility allocations for multi-agent interactions: A differentiable optimization approach with control barrier functions

TL;DR

This paper introduces a differentiable optimization method using control barrier functions to learn responsibility allocations in multi-agent systems, enhancing safety and interpretability in autonomous interactions.

Contribution

It presents a novel data-driven approach to model and learn responsibility sharing among agents for safe multi-agent interactions using control barrier functions.

Findings

Successfully learned responsibility allocations from synthetic data

Demonstrated interpretability of responsibility adjustments in real-world scenarios

Improved understanding of agent behavior in safety-critical multi-agent systems

Abstract

From autonomous driving to package delivery, ensuring safe yet efficient multi-agent interaction is challenging as the interaction dynamics are influenced by hard-to-model factors such as social norms and contextual cues. Understanding these influences can aid in the design and evaluation of socially-aware autonomous agents whose behaviors are aligned with human values. In this work, we seek to codify factors governing safe multi-agent interactions via the lens of responsibility, i.e., an agent's willingness to deviate from their desired control to accommodate safe interaction with others. Specifically, we propose a data-driven modeling approach based on control barrier functions and differentiable optimization that efficiently learns agents' responsibility allocation from data. We demonstrate on synthetic and real-world datasets that we can obtain an interpretable and quantitative understanding of how much agents adjust their behavior to ensure the safety of others given their current environment.