Inferring Belief States in Partially-Observable Human-Robot Teams
CoRR(2024)
摘要
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.
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