Inferring World Belief States in Dynamic Real-World Environments

arXiv cs.RO / 4/14/2026

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Key Points

The paper studies how to estimate a human teammate’s “world belief state” from a robot’s observations in dynamic, partially observable 3D environments.
It builds on mental model theory, using both an individual internal world simulation and a team model that captures each teammate’s beliefs and capabilities to support more fluent collaboration.
The core contribution is an inference method to estimate a teammate’s belief state (level one situation awareness) as a human-robot team navigates a household environment.
The authors validate the approach in realistic simulation, then extend it to a real-world robot platform to test practical feasibility.
They also demonstrate a downstream application where the inferred belief state improves active assistance via semantic reasoning.

Abstract

We investigate estimating a human's world belief state using a robot's observations in a dynamic, 3D, and partially observable environment. The methods are grounded in mental model theory, which posits that human decision making, contextual reasoning, situation awareness, and behavior planning draw from an internal simulation or world belief state. When in teams, the mental model also includes a team model of each teammate's beliefs and capabilities, enabling fluent teamwork without the need for constant and explicit communication. In this work we replicate a core component of the team model by inferring a teammate's belief state, or level one situation awareness, as a human-robot team navigates a household environment. We evaluate our methods in a realistic simulation, extend to a real-world robot platform, and demonstrate a downstream application of the belief state through an active assistance semantic reasoning task.