Inferring World Belief States in Dynamic Real-World Environments
This work addresses the challenge of enabling fluent human-robot teamwork by inferring human situation awareness without explicit communication, though the approach is incremental and domain-specific.
The authors developed a method to infer a human's belief state from robot observations in dynamic 3D environments, achieving successful estimation in both simulation and real-world settings, and demonstrated its utility in an active assistance task.
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.