Inferring World Belief States in Dynamic Real-World Environments
Jack Kolb, Aditya Garg, Nikolai Warner, Karen M. Feigh
TLDR
This paper explores how robots can infer a human's world belief state in dynamic environments to improve human-robot teamwork.
Key contributions
- Develops methods for robots to infer human world belief states in dynamic, partially observable environments.
- Grounds the approach in mental model theory, crucial for human decision-making and teamwork.
- Validated through realistic simulation and deployment on a real-world robot platform.
- Shows a practical application in an active assistance semantic reasoning task.
Why it matters
This research is crucial for enabling more fluent and effective human-robot teamwork by allowing robots to understand their human partners' perspectives. By inferring belief states, robots can provide proactive assistance and improve overall collaboration in complex, real-world settings.
Original 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.
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