一种用于机器人介导任务的可部署架构（DART）：在社交辅助机器人引导的认知行为疗法练习中的评估

中文摘要 AI 辅助

社交辅助机器人（SARs）可以支持结构化的健康和福祉干预，但硬件和成本限制制约了交互复杂性和长期现实世界部署。我们提出了DART：用于机器人介导任务的可部署架构，该架构通过Web应用和云基础设施扩展SARs，使视觉内容、用户输入、远程计算和持久数据存储能够与机器人的物理体现、语音和运动协同工作。我们通过在一个迭代开发的、用于帮助具有高广泛性焦虑的大学生完成认知行为疗法（CBT）家庭作业练习的全栈人机交互（HRI）系统中实例化DART来评估它。由此产生的系统使用了低成本的开放源代码Blossom机器人平台，并通过参与式设计过程和多项用户研究进行了改进和评估，最终在103名参与者的实验室研究中进行了评估，随后在四名参与者中进行了为期六周的家庭部署。在实验室评估中，参与者在会话内表现出压力、状态焦虑和负面情绪的显著降低，并给出了平均系统可用性量表（SUS）得分为78.89。在家庭部署中，平均系统可用性量表得分为87.5，并获得了关于可用性的积极定性反馈。两组参与者均将语音输入、视觉呈现和Web-机器人同步确定为改进的优先事项。这些发现验证了DART作为一种有效架构，能够在实验室单次会话和现实世界长期部署中扩展低成本SAR的能力。

英文摘要

Socially assistive robots (SARs) can support structured health and well-being interventions, but hardware and cost constraints limit interaction complexity and longitudinal real-world deployments. We present DART: Deployable Architecture for Robot-Mediated Tasks, an architecture that extends SARs through a web application and cloud infrastructure, enabling visual content, user input, remote computation, and persistent data storage synergistically with the robot's physical embodiment, speech, and movement. We evaluated DART by instantiating it in an interatively-developed full-stack HRI system for helping university students with elevated generalized anxiety to complete cognitive behavioral therapy (CBT) homework exercises. The resulting system, which used the low-cost open-source Blossom robot platform, was refined and evaluated through a participatory design process and multiple user studies, and finally evaluated in an in-lab study with 103 participants, and then a six-week in-home deployment with four participants. In the in-lab evaluation, participants showed significant within-session reductions in stress, state anxiety, and negative affect, and gave the platform a mean System Usability Scale score of 78.89. In the home deployment, the mean System Usability Scale score was 87.5, with positive qualitative feedback on usability. Participants across both groups identified speech input, visual presentation, and web-robot synchronization as priorities for improvement. These findings validate DART as an effective architecture for extending the capabilities of a low-cost SAR in both in-lab single-session and in real-world longitudinal deployments.

发表机构

• University of Southern California（南加州大学）

机构由 AI 辅助整理，请以论文原文为准。