Collaborative Research: Novel Approaches to Human-Robot Interaction

机器人开发人员主要关注编程行为，特别是关于机器人的导航和传感器系统。 很多时候，人机交互（HRI）设计是事后的想法，如果有任何想法的话。 从数十年的人机交互（HCI）研究中吸取的经验教训很少应用于机器人系统。 在这个项目中，PI将试图通过研究现有的人机界面和开发新的界面使用人机交互技术修改机器人应用程序来弥合这一差距。 为此，PI将首先通过研究AAAI救援竞赛并通过与领域专家的正式测试，评估HRI在城市搜索和救援方面的现状。 在这项研究中，将特别强调：发现最佳实践，提供在HRI的操作员是物理上与机器人分离的情况下的感知;发现HRI的最佳方法时，在机器人系统中切换的自主权水平，无论是当开关是机器人驱动或人类驱动的;和调查如何HRI的影响时，机器人可以改变它的形状。 PI将从他们的研究指南中抽象出设计有效的HRI;这些反过来将为PI设计城市搜索和救援系统提供信息，其中机器人行为的设计与机器人界面的设计同时发生。更广泛的影响：一般的人机交互评估方法，特别是城市搜索和救援应用的改进HRI，将直接造福于整个社会。 在这个项目中获得的对数据表示和融合的见解将为未来人机系统的设计提供信息，这些系统涉及安全关键情况下的决策。类似地，更好地理解人类-机器人系统中人类所需的意识类型将影响多用户计算领域。 由于机器人通常携带一个以上的机载传感器（如视频和声纳），信息可视化技术开发期间，联合收割机不同的传感器信息将适用于系统，如防空系统或公共交通控制室。

Robot developers focus mostly on programming behaviors, particularly in regards to the robot's navigation and sensor systems. All too often, the human-robot interaction (HRI) design is an afterthought, if any thought is given to it at all. Lessons learned from decades of research in human-computer interaction (HCI) are rarely applied to robotic systems. In this project, the PIs will attempt to bridge this gap by studying existing human-robot interfaces and developing novel interfaces using HCI techniques modified for robot applications. To these ends, the PIs will first evaluate the current state of HRI for urban search and rescue, by studying the AAAI Rescue Competition and through formal testing with domain experts. Particular emphasis will be placed in this study on: discovering best practices for providing situation awareness in HRI where the operator is physically separated from the robot; discovering the best methods for HRI when switching autonomy levels in a robot system, either when the switch is robot-driven or human-driven; and investigating how HRI is affected when a robot can change its shape. The PIs will abstract from their study guidelines for designing effective HRI; these in turn will inform the PIs' design of a system for urban search and rescue, where the design of the robot's behaviors occurs simultaneously with the design of the robot's interface.Broader Impacts: Evaluation methods for human-robot interaction in general, and improved HRI for urban search and rescue applications in particular, will directly benefit society as a whole. Insights acquired in this project into data presentation and fusion will inform the design of future human-machine systems that involve decision-making in safety-critical situations. Similarly, better understanding of the types of awareness required of the human in a human-robot system will impact the multi-user computing domain in general. Since robots often carry more than one on-board sensor (such as video and sonar), information visualization techniques developed during this project that combine disparate sensor information will be applicable to systems such as air defense systems or mass transit control rooms.