机器人何时应干预？在人机交互中平衡参与度与侵入性
When Should Robots Intervene? Balancing Engagement and Intrusiveness in Human-Robot Interaction
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中文总结 AI 辅助
本研究通过用户实验比较持续寻求参与与上下文感知两种机器人干预策略，发现高交互频率不必然提升参与度，有效参与需平衡支持与侵入性，依赖自适应、上下文敏感的干预策略。
中文摘要 AI 辅助
在设计面向任务场景的有效人机交互时，需要仔细平衡用户参与度与社交可接受的机器人侵入性水平。在本文中，我们研究了不同的机器人干预策略如何塑造用户体验、交互动态、感知侵入性以及支持感。我们比较了两种方法：一种持续寻求参与的机器人策略，以及一种基于用户状态和任务上下文选择性干预的上下文感知策略。两种方法都依赖多模态行为线索，包括身体朝向和注意力信号，来指导机器人行动。我们在一个模拟医院环境中对32名参与者进行了用户研究，以评估这些策略。我们的研究结果表明，更高的交互频率并不必然带来更好的参与度。相反，我们观察到感知支持与侵入性之间存在系统性权衡，这种权衡受到物理距离和用户努力等因素的影响。这些结果提供了实证证据，表明人机交互中的有效参与依赖于自适应、上下文敏感的干预策略。
英文摘要
Designing effective Human-Robot Interaction in task-oriented settings requires carefully balancing user engagement with socially acceptable levels of robot intrusiveness. In this paper, we examine how different robot intervention strategies shape user experience, interaction dynamics, perceived intrusiveness, and sense of support. We compare two approaches: a continuous engagement-seeking robot strategy, and a context-aware strategy that selectively intervenes based on the user's state and task context. Both approaches rely on multimodal behavioral cues, including body orientation and attentional signals, to guide robot actions. We evaluate these strategies in a user study with 32 participants performing a task in a simulated hospital environment. Our findings show that higher interaction frequency does not necessarily lead to better engagement. Instead, we observe a systematic trade-off between perceived support and intrusiveness, influenced by factors such as physical proximity and user effort. These results provide empirical evidence that effective engagement in HRI depends on adaptive, context-sensitive intervention policies.
发表机构
- Institut de Robòtica i Informàtica Industrial (CSIC-UPC)（工业机器人与信息学研究所（CSIC-UPC））
- Universitat Politècnica de Catalunya (UPC)（加泰罗尼亚理工大学）
机构由 AI 辅助整理，请以论文原文为准。
