Learning responsibility allocations for multi-agent interactions: A differentiable optimization approach with control barrier functions
<|reference_start|>Learning responsibility allocations for multi-agent interactions: A differentiable optimization approach with control barrier functions: 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.<|reference_end|>
@article{remy2024learning,
    title={Learning responsibility allocations for multi-agent interactions: A
  differentiable optimization approach with control barrier functions},
    author={Isaac Remy, David Fridovich-Keil, Karen Leung},
    journal={arXiv preprint arXiv:2410.07409},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.07409},
    primaryClass={eess.SY cs.LG cs.MA cs.RO cs.SY}
}