CARIS让机器人交互实验跨场景通用，支持心理陪伴与导览双模式。
CARIS: A Context-Adaptable Robot Interface System for Personalized and Scalable Human-Robot Interaction
- 基于远程操控与多模态数据记录，实现跨场景自适应机器人控制。
- 在心理健康与导览场景中验证有效性，提升人机交互研究效率。
- 开源工具支持快速部署，适合关注个性化交互的研究者。
人机交互（HRI）领域长期依赖傀儡师式（Wizard-of-Oz, WoZ）机器人探索导航、对话动态及人机协同等行为。然而，现有WoZ工具通常局限于单一场景，难以适配不同环境、用户和机器人平台。为此，本文提出上下文自适应机器人交互系统（CARIS），集成远程操控、人类感知、人机对话与多模态数据记录能力。通过试点研究，证明CARIS可在两种情境下有效支持机器人控制：1）心理健康陪伴；2）旅游导览。同时识别出改进方向，包括运动与沟通的流畅整合、功能模块清晰分离、推荐提示词及一键通信选项，以提升傀儡师操作体验。本项目提供公开可获取的工具，助力研究者采用数据驱动方法开发智能机器人行为。
原文摘要 · Abstract (English)
The human-robot interaction (HRI) field has traditionally used Wizard-of-Oz (WoZ) controlled robots to explore navigation, conversational dynamics, human-in-the-loop interactions, and more to explore appropriate robot behaviors in everyday settings. However, existing WoZ tools are often limited to one context, making them less adaptable across different settings, users, and robotic platforms. To mitigate these issues, we introduce a Context-Adaptable Robot Interface System (CARIS) that combines advanced robotic capabilities such teleoperation, human perception, human-robot dialogue, and multimodal data recording. Through pilot studies, we demonstrate the potential of CARIS to WoZ control a robot in two contexts: 1) mental health companion and as a 2) tour guide. Furthermore, we identified areas of improvement for CARIS, including smoother integration between movement and communication, clearer functionality separation, recommended prompts, and one-click communication options to enhance the usability wizard control of CARIS. This project offers a publicly available, context-adaptable tool for the HRI community, enabling researchers to streamline data-driven approaches to intelligent robot behavior.
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