梳理机器人群体交互的计算挑战，揭示感知与行为生成的关键难题
Social Group Human-Robot Interaction: A Scoping Review of Computational Challenges
- 系统分析44篇近十年论文，提炼感知与行为生成的核心挑战
- 发现群体检测、互动参与度与对话信息获取是主要感知难点
- 建议关注子群体识别与人际关系建模，适合人机交互研究者参考
群体互动是我们日常生活的自然组成部分，随着机器人日益融入社会，它们必须能够同时与多人进行社交互动。然而，群体人机交互（HRI）带来了当前文献中常被忽视的独特计算挑战。我们对2015至2024年间发表的44篇群体HRI论文进行了范围综述，提取了与感知和行为生成相关的变量，以及影响这些挑战的环境、群体和机器人能力因素。研究发现，感知层面的关键挑战包括群体检测、互动参与度及对话信息获取；行为生成方面则涉及接近行为与对话行为的设计。此外，我们识别出若干研究空白，如提升子群体与人际互动关系的识别能力，并提出未来研究方向以帮助应对这些计算挑战。
原文摘要 · Abstract (English)
Group interactions are a natural part of our daily life, and as robots become more integrated into society, they must be able to socially interact with multiple people at the same time. However, group human-robot interaction (HRI) poses unique computational challenges often overlooked in the current HRI literature. We conducted a scoping review including 44 group HRI papers from the last decade (2015-2024). From these papers, we extracted variables related to perception and behaviour generation challenges, as well as factors related to the environment, group, and robot capabilities that influence these challenges. Our findings show that key computational challenges in perception included detection of groups, engagement, and conversation information, while challenges in behaviour generation involved developing approaching and conversational behaviours. We also identified research gaps, such as improving detection of subgroups and interpersonal relationships, and recommended future work in group HRI to help researchers address these computational challenges
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。