让机器人通过观察推断人类对环境的认知状态，提升人机协作效率。
Inferring World Belief States in Dynamic Real-World Environments
- 基于心理模型理论，从机器人视角推断人类对环境的认知状态。
- 在真实模拟与机器人平台上验证方法，实现对队友认知状态的准确推断。
- 适用于需要默契协作的人机系统，如家庭服务机器人场景。
我们研究在动态、三维且部分可观测的环境中，利用机器人的观测来估计人类的世界信念状态。该方法基于心理模型理论，认为人类决策、情境推理、态势感知和行为规划均依赖于内部构建的环境信念状态。在团队协作中，心理模型还包括对每个队友信念与能力的认知，从而实现无需频繁显式沟通的流畅协作。本文复现了团队模型的核心组件，即在人机协同导航家庭环境时，推断队友的信念状态（一级态势感知）。我们在真实感仿真环境中评估了该方法，并扩展至真实机器人平台，展示了信念状态在主动辅助语义推理任务中的下游应用。
原文摘要 · Abstract (English)
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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