Inferring Belief States in Partially-Observable Human-Robot Teams
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910822576422912 |
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| author | Kolb, Jack Feigh, Karen M. |
| author_facet | Kolb, Jack Feigh, Karen M. |
| contents | We investigate the real-time estimation of human situation awareness using observations from a robot teammate with limited visibility. In human factors and human-autonomy teaming, it is recognized that individuals navigate their environments using an internal mental simulation, or mental model. The mental model informs cognitive processes including situation awareness, contextual reasoning, and task planning. In teaming domains, the mental model includes a team model of each teammate's beliefs and capabilities, enabling fluent teamwork without the need for explicit communication. However, little work has applied team models to human-robot teaming. In this work we compare the performance of two models, logical predicates and large language models, at estimating user situation awareness over varying visibility conditions. Our results indicate that the methods are largely resilient to low-visibility conditions in our domain, however opportunities exist to improve their overall performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_11955 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Inferring Belief States in Partially-Observable Human-Robot Teams Kolb, Jack Feigh, Karen M. Robotics Human-Computer Interaction We investigate the real-time estimation of human situation awareness using observations from a robot teammate with limited visibility. In human factors and human-autonomy teaming, it is recognized that individuals navigate their environments using an internal mental simulation, or mental model. The mental model informs cognitive processes including situation awareness, contextual reasoning, and task planning. In teaming domains, the mental model includes a team model of each teammate's beliefs and capabilities, enabling fluent teamwork without the need for explicit communication. However, little work has applied team models to human-robot teaming. In this work we compare the performance of two models, logical predicates and large language models, at estimating user situation awareness over varying visibility conditions. Our results indicate that the methods are largely resilient to low-visibility conditions in our domain, however opportunities exist to improve their overall performance. |
| title | Inferring Belief States in Partially-Observable Human-Robot Teams |
| topic | Robotics Human-Computer Interaction |
| url | https://arxiv.org/abs/2403.11955 |