用语义感知提升人机协作中的态势感知能力
An Exploratory Study on Human-Robot Interaction using Semantics-based Situational Awareness
- 基于语义信息构建环境认知框架，动态揭示环境复杂度
- 降低操作员工作负荷，提升信任感，缩短自主权切换反应时间
- 适合人机协同、应急响应等高压力场景的系统设计
本文研究高层语义（环境评估）对移动机器人部署中人机团队（HRT）与人机交互（HRI）的影响。尽管语义在人工智能中广泛研究，其在人机协同中的作用仍不明确且难以实现。我们采用基于语义的框架，在模拟灾害救援任务中揭示环境的语义信息量。此类任务要求快速决策，人类操作员易因高工作负荷和压力导致态势感知建立困难，尤其在多任务间切换时。实验表明：1）语义信息可缓解操作员感知负荷；2）提升操作员对态势感知的信任；3）减少自主权切换时的反应时间。此外，对系统信任度高的参与者更倾向于使用遥操作模式。
原文摘要 · Abstract (English)
In this paper, we investigate the impact of high-level semantics (evaluation of the environment) on Human-Robot Teams (HRT) and Human-Robot Interaction (HRI) in the context of mobile robot deployments. Although semantics has been widely researched in AI, how high-level semantics can benefit the HRT paradigm is underexplored, often fuzzy, and intractable. We applied a semantics-based framework that could reveal different indicators of the environment (i.e. how much semantic information exists) in a mock-up disaster response mission. In such missions, semantics are crucial as the HRT should handle complex situations and respond quickly with correct decisions, where humans might have a high workload and stress. Especially when human operators need to shift their attention between robots and other tasks, they will struggle to build Situational Awareness (SA) quickly. The experiment suggests that the presented semantics: 1) alleviate the perceived workload of human operators; 2) increase the operator's trust in the SA; and 3) help to reduce the reaction time in switching the level of autonomy when needed. Additionally, we find that participants with higher trust in the system are encouraged by high-level semantics to use teleoperation mode more.
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