The current spread of social and assistive robotics applications is increasingly highlighting the need for robots that can be easily taught and interacted with, even by users with no technical background. Still, it is often difficult to grasp what such robots know or to assess if a correct representation of the task is being formed. Augmented Reality (AR) has the potential to bridge this gap. We demonstrate three use cases where AR design elements enhance the explainability and efficiency of human-robot interaction: 1) a human teaching a robot some simple kitchen tasks by demonstration, 2) the robot showing its plan for solving novel tasks in AR to a human for validation, and 3) a robot communicating its intentions via AR while assisting people with limited mobility during daily activities.
External Human-Machine Interface based on Intent Recognition: Framework Design and Experimental Validation
College of Transportation and the Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University（同济大学交通学院和道路与交通工程教育部重点实验室）；Department of Civil and Environmental Engineering, National University of Singapore（新加坡国立大学土木与环境工程系）
Towards a Humanized Social-Media Ecosystem: AI-Augmented HCI Design Patterns for Safety, Agency & Well-Being
College of Engineering（工程学院）；Computer Sciences Marshall University Huntington, WV, USA（计算机科学马歇尔大学亨廷顿州威斯康星州）；Department of Computer Science（计算机科学系）；Engineering University of Rajshahi Rajshahi 6205, Bangladesh（工程 Rajshahi 大学 Rajshahi 6205 巴基斯坦）
