提出基于努力的交互模型，让机器人更好理解人类互动中的参与状态。
IM HERE: Interaction Model for Human Effort Based Robot Engagement
- 用双方投入的努力描述交互关系，提炼出焦点定位与四种核心状态。
- 可识别误沟通，支持自主系统在社交规范下行动。
- 适用于人-人、人-机、机-机多种场景，适合社交机器人研发者。
人机交互的有效性往往取决于能否培育参与感——一种支持有意义交流的认知投入动态过程。现有参与度定义和模型或过于模糊，或难以跨情境推广。本文提出 IM HERE 框架，有效建模人-人、人-机及机-机交互中的参与行为。通过采用双主体间努力投入的描述方式，该框架准确分解关系模式，简化为焦点定位与四个关键状态。模型可捕捉相互关系、群体行为及符合社会规范的行为，并转化为自主系统的具体指令。融合主观感知与客观状态，精确识别并描述误沟通。本文主要目标是实现社交行为的自动化分析、建模与描述，使自主系统在遵循社会规范的同时达成自身社交目标，实现全面社会融合。
原文摘要 · Abstract (English)
The effectiveness of human-robot interaction often hinges on the ability to cultivate engagement - a dynamic process of cognitive involvement that supports meaningful exchanges. Many existing definitions and models of engagement are either too vague or lack the ability to generalize across different contexts. We introduce IM HERE, a novel framework that models engagement effectively in human-human, human-robot, and robot-robot interactions. By employing an effort-based description of bilateral relationships between entities, we provide an accurate breakdown of relationship patterns, simplifying them to focus placement and four key states. This framework captures mutual relationships, group behaviors, and actions conforming to social norms, translating them into specific directives for autonomous systems. By integrating both subjective perceptions and objective states, the model precisely identifies and describes miscommunication. The primary objective of this paper is to automate the analysis, modeling, and description of social behavior, and to determine how autonomous systems can behave in accordance with social norms for full social integration while simultaneously pursuing their own social goals.
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