Inferring Belief States in Partially-Observable Human-Robot Teams
<|reference_start|>Inferring Belief States in Partially-Observable Human-Robot Teams: 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. We compare the performance of two current methods 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.<|reference_end|>
@article{kolb2024inferring,
    title={Inferring Belief States in Partially-Observable Human-Robot Teams},
    author={Jack Kolb, Karen M. Feigh},
    journal={arXiv preprint arXiv:2403.11955},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.11955},
    primaryClass={cs.RO cs.HC}
}