Learning responsibility allocations for multi-agent interactions: A differentiable optimization approach with control barrier functions
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866911502270726144 |
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| author | Remy, Isaac Fridovich-Keil, David Leung, Karen |
| author_facet | Remy, Isaac Fridovich-Keil, David Leung, Karen |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_07409 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Learning responsibility allocations for multi-agent interactions: A differentiable optimization approach with control barrier functions Remy, Isaac Fridovich-Keil, David Leung, Karen Systems and Control Machine Learning Multiagent Systems Robotics 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. |
| title | Learning responsibility allocations for multi-agent interactions: A differentiable optimization approach with control barrier functions |
| topic | Systems and Control Machine Learning Multiagent Systems Robotics |
| url | https://arxiv.org/abs/2410.07409 |