GCR: Community-Embedded Robotics: Understanding Sociotechnical Interactions with Long-term Autonomous Deployments

GCR：社区嵌入式机器人：了解社会技术与长期自主部署的相互作用

• 批准号: 2219236
• 负责人: Luis Sentis
• 金额: $ 360万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2027-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2219236&HistoricalAwards=false
• 关键词: GCR; Community; Embedded; Robotics; Understanding

This project combines methods and expertise in the fields of human-robot interaction, human factors, organizational communication, science and technology studies, team science, and research informatics to achieve sustained access to phenomena at the intersection of communities and autonomous robots that is otherwise impractical or impossible to obtain. The research outcomes will identify and address ethical, privacy, and safety concerns created when multi-purpose robots are deployed into communities. The research plan enables transformative expansions of several scientific fields onto a shared research object: community-robot encounters. Specifically, the goal is to expand the fields of human factors for robotics and human-robot interaction beyond their traditional focus on robots encountering small numbers of humans, in episodic or incident-based temporalities, and beyond the confines of laboratory environments. This broadening will enable studying large, changing groups of humans interacting with autonomous robots longitudinally, in real-world environments. This work will allow the field of robot perception for long-term autonomy to expand its scope beyond the navigational challenges in the built environment and the relatively predictable movement of vehicles to also include pedestrian behavior. All three robotics subfields will contribute to important aspects of robotic behavior only testable in real-world conditions, including social navigation through crowds, safety and trust in community spaces, and introspective perception of robotic capabilities.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目结合了人机交互，人为因素，组织沟通，科学技术研究，团队科学和研究信息学等领域的方法和专业知识，以实现对社区和自主机器人交叉点现象的持续访问，否则不切实际或不可能获得。 研究成果将确定和解决多用途机器人部署到社区时产生的道德，隐私和安全问题。 该研究计划使几个科学领域的变革性扩展成为一个共同的研究对象：社区机器人相遇。具体来说，我们的目标是扩大机器人和人机交互的人为因素的领域，超越他们传统的关注机器人遇到少量的人类，在情节或基于事件的暂时性，并超越实验室环境的限制。这种扩大将使研究大型，不断变化的人类群体与自主机器人纵向互动，在现实世界的环境中。这项工作将使机器人长期自主感知领域的范围扩大到建筑环境中的导航挑战和相对可预测的车辆运动之外，还包括行人行为。所有三个机器人子领域都将为只能在现实世界条件下测试的机器人行为的重要方面做出贡献，包括人群中的社交导航、社区空间的安全和信任以及对机器人能力的内省感知。该奖项反映了NSF的法定使命，并且通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

"What’s That Robot Doing Here?": Perceptions Of Incidental Encounters With Autonomous Quadruped Robots

“那个机器人在这里做什么？”：对偶遇自主四足机器人的看法

• DOI: 10.1145/3597512.3599707
• 发表时间: 2023
• 期刊: ACM
• 作者: Hauser, Elliott;Chan, Yao-Cheng;Chonkar, Parth;Hemkumar, Geethika;Wang, Huihai;Dua, Daksh;Gupta, Shikhar;Enriquez, Efren Mendoza;Kao, Tiffany;Hart, Justin
• 通讯作者: Hart, Justin

Trustworthy Memory Practices for Better Living and Working with Robots

值得信赖的记忆实践，让机器人更好地生活和工作

• DOI: 10.1145/3597512.3597516
• 发表时间: 2023
• 期刊: ACM
• 作者: Baker, Samuel;Hauser, Elliott;Hart, Justin
• 通讯作者: Hart, Justin