EAGER: Multi-Modal Communication for Teachable Robots

EAGER：可示教机器人的多模式通信

• 批准号: 1939037
• 负责人: Reid Simmons
• 金额: $ 29.98万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1939037&HistoricalAwards=false
• 关键词: EAGER; Multi; Modal; Communication; Teachable

Peer tutoring, in which one student teaches material to another student, has been proven to increase learning, especially for the student who is taking on the role of teacher. This is because detecting and correcting mistakes often requires the student teacher to reflect on their own knowledge to develop explanations, reinforcing their knowledge of the subject matter. The effectiveness of such peer tutoring, however, depends on how learners behave -- if they pick up the subject too quickly, the student teacher may benefit less; if they pick it up too slowly, the teacher may get frustrated or confused themselves. An alternative is to have the learner pretend not to know the subject and help guide the teacher by "back leading" -- asking the right questions, making occasional "mistakes", etc. To this end, the goal of this research is to develop a social robot that plays the role of learner, guiding the student teacher using both verbal and non-verbal behaviors, modeling both the cognitive and emotional state of the student, and providing feedback and encouragement.The project focuses on developing multi-modal communication for a social robot that provides cognitive and emotional support of students who are trying to teach the robot multi-step mathematical problem solving. The student and robot collaborate around a "smart" whiteboard that forms the joint focal point for learning. Concepts from Theory of Mind are used to infer the affective state of the student from their multi-modal behaviors and to provide support and encouragement, with the goal of facilitating the student persevering in the task and developing meta-cognitive skills to teach more effectively. Concepts from Common Ground Theory are used to generate robot actions, such as asking questions or making deliberate mistakes, to help guide the student teacher to better explain the concept, through an interactive presentation/acceptance process. As part of this project, a robot ambassador is being developed that can explain robotics and STEM to middle-school students.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.

同伴辅导，其中一个学生教材料给另一个学生，已被证明可以增加学习，特别是对于正在担任教师角色的学生。这是因为发现和纠正错误往往需要实习教师反思自己的知识，以发展解释，加强他们的知识的主题。然而，这种同伴辅导的效果取决于学习者的行为-如果他们学得太快，实习教师可能受益较少;如果他们学得太慢，教师自己可能会感到沮丧或困惑。另一种方法是让学习者假装不知道这个主题，并通过“背引导”来帮助指导老师--问正确的问题，偶尔犯“错误”等。为此，本研究的目标是开发一个扮演学习者角色的社会机器人，使用语言和非语言行为指导学生教师，模拟学生的认知和情感状态，并提供反馈和鼓励。该项目的重点是为社交机器人开发多模态通信，为试图教机器人多步数学问题解决的学生提供认知和情感支持。 学生和机器人围绕一个“智能”白板进行协作，形成学习的联合焦点。从心理理论的概念是用来推断情感状态的学生从他们的多模态行为，并提供支持和鼓励，以促进学生坚持在任务和发展元认知技能，更有效地教学的目标。来自共同点理论的概念用于生成机器人动作，例如提问或故意犯错误，以帮助指导学生教师通过交互式演示/接受过程更好地解释概念。作为该项目的一部分，正在开发一个机器人大使，可以向中学生解释机器人技术和STEM。该奖项反映了NSF的法定使命，并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

Perception of Emotion in Torso and Arm Movements on Humanoid Robot Quori

人形机器人 Quori 躯干和手臂运动的情感感知

• DOI: 10.1145/3434074.3447129
• 发表时间: 2021
• 期刊: Companion of the International Conference on Human-Robot Interaction
• 作者: Kaushik, Roshni;Simmons, Reid
• 通讯作者: Simmons, Reid

Affective Robot Behavior Improves Learning in a Sorting Game

机器人的情感行为改善了排序游戏中的学习

• DOI: 10.1109/ro-man53752.2022.9900654
• 发表时间: 2022
• 期刊: 2022 31st IEEE International Conference on Robot and Human Interactive Communication (RO-MAN
• 作者: Kaushik, Roshni;Simmons, Reid
• 通讯作者: Simmons, Reid