发表机构
Johns Hopkins University（约翰霍普金斯大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过部署RoboCafé咖啡机器人12天，探讨长期公共人机交互中的连续性挑战，提出四项系统设计需求以维持交互连续性。
AI 中文摘要
随着机器人在公共空间中长时间驻留，它们必须在人员、遭遇和环境变化时，通过保留并正确应用上下文来维持交互连续性。为了研究长期公共人机交互中的交互连续性，我们开发了RoboCafé，一个自主对话式咖啡机器人，旨在通过任务感知对话、实时多模态感知和先前遭遇的记忆来支持重复交互。我们在大学建筑中部署RoboCafé长达12天，期间收到148个订单。该部署涉及回头客、路过者、变化的群体以及连续订单，这些情况反复跨越了系统以订单为中心的交互模型所假定的边界。我们发现，成功的交互连续性要求机器人确定当前在场人员、哪些先前上下文属于谁、交互在何处开始和结束，以及其对交互的表示是否与物理世界中实际发生的情况相符。基于这些观察，我们得出了在纵向公共人机交互中维持交互连续性的四个系统设计需求：上下文交互状态、持久人员锚定、显式交互生命周期管理和交互可观测性。
英文摘要
As robots remain in public spaces over extended periods, they must maintain interaction continuity by preserving and correctly applying context as people, encounters, and circumstances change. To study interaction continuity in long-term public human-robot interactions, we developed RoboCafé, an autonomous conversational coffee robot designed to support repeated interactions through task-aware dialogue, real-time multimodal perception, and memory of prior encounters. We deployed RoboCafé for 12 days in a university building, where it received 148 orders. The deployment involved repeat customers, passersby, changing groups, and back-to-back orders that repeatedly crossed the boundaries assumed by the system's order-centered interaction model. We found that successful interaction continuity requires a robot to determine who is currently present, which prior context belongs to whom, where interactions begin and end, and whether its representation of an interaction matches what is occurring in the physical world. From these observations, we derive four system design requirements for maintaining interaction continuity in longitudinal public human-robot interactions: contextual interaction state, persistent person grounding, explicit interaction life-cycle management, and interaction observability.
Comments8 pages, 3 figures. Kaitlynn Taylor Pineda and Kush Kumar Kushwaha contributed equally to this work. Submitted to IEEE International Conference on Robotics and Automation (ICRA 2027)
