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

• URL: http://arxiv.org/abs/2410.07409v1
• Date: Wed, 9 Oct 2024 20:20:41 GMT
• Title: Learning responsibility allocations for multi-agent interactions: A differentiable optimization approach with control barrier functions
• Authors: Isaac Remy, David Fridovich-Keil, Karen Leung,
• Abstract summary: We seek to codify factors governing safe multi-agent interactions via the lens of responsibility. We propose a data-driven modeling approach based on control barrier functions and differentiable optimization.
• Score: 12.074590482085831
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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