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

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 sociallyaware 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 Figure 1: In a) and b), two cars are swapping lanes on a highway, but an interpretable and quantitative understanding of how much their desired controls lead to collision. In c) and d), we see how the agents adjust their behavior to ensure the safety of others given agents may deviate from their ideal trajectories, according to two their current environment.