用可微优化学习多智能体互动中的责任分配，让机器人更懂安全协作。
Learning responsibility allocations for multi-agent interactions: A differentiable optimization approach with control barrier functions
- 基于控制屏障函数与可微优化，从数据中学习智能体的责任分配。
- 在真实和合成数据上验证，能定量分析行为调整程度以保障安全。
- 适合研究人机协作、自动驾驶等需安全交互的场景。
从自动驾驶到包裹配送，确保多智能体互动的安全与高效极具挑战，因为互动动态受社交规范、上下文线索等难以建模因素影响。理解这些因素有助于设计与评估符合人类价值观的社会化自主代理。本文通过‘责任’视角（即智能体为安全互动而偏离自身期望控制的意愿）来编码影响安全互动的因素。具体提出一种基于控制屏障函数和可微优化的数据驱动建模方法，可高效地从数据中学习智能体的责任分配。在合成数据集和真实世界数据集上的实验表明，该方法能获得对当前环境下智能体为保障他人安全而调整行为程度的可解释、定量理解。
原文摘要 · Abstract (English)
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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