NRI: Real Time Observation, Inference and Intervention of Co-Robot Systems Towards Individually Customized Performance Feedback Based on Students' Affective States

NRI：协作机器人系统的实时观察、推理和干预，以实现基于学生情感状态的个性化定制表现反馈

• 批准号: 1527148
• 负责人: Conrad Tucker
• 金额: $ 34.26万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1527148&HistoricalAwards=false
• 关键词: NRI; Real; Time; Observation; Inference

This NSF National Robotics Initiative project will investigate the potential of a cycle of observation, inference and intervention by co-robot systems to enhance students' affective states and improve their performance on engineering laboratory tasks. Co-robots are robots that work side-by-side with humans, assisting them and adapting to their needs. The two-way exchange of knowledge between students and co-robots creates a reciprocal relationship, in which each party learns from the other in service of a common goal. Affective states, such as frustration and engagement, play a major role in students' performance on everyday learning tasks. A student who is overly stressed or distracted may commit errors that would be otherwise easy to avoid. A co-robot system that is cognizant of students' affective states can intervene to prevent these errors. The results of this project may provide a template for skill-based instruction on topics well beyond engineering. Currently, such learning requires extensive interactions between a student and an instructor, with the instructor providing intensive feedback at all times. In many cases, personality mismatches or other issues between instructor and student can lead to frustration, learning difficulties, and eventual dropout. Furthermore, one-on-one learning is limited by scalability challenges, as an increase in the number of students, without a proportional increase in trained instructors, can result in decreases in quality and quantity of instructor time allocated to each student. Co-robot learning systems will be able to mitigate these challenges by providing both real time and scalable feedback systems that adapt to the individual needs of students and help to minimize the amount of human instructor time required by each student. This research will acquire facial, auditory, and body gesture data from students using the integrated visual, audio and depth sensory system of the co-robot. The system will make statistical inferences of students' affective states, based on machine learning classification of facial and body language data. Visual feedback will be used to present students with interventions (visual instructions and commentary) intended to enhance their affective state and improve their performance on laboratory tasks. The project will assess the impact of co-robots' ability to improve students' affective states and enhance students' performance on laboratory tasks over repeated iterations of learning and testing. This project will lead to a better understanding of how students interact and function during potentially stressful laboratory activities. The co-robot systems proposed in this work will help discover the correlations that exists between students' affect and task performance. Co-robots will actively adapt to the manner in which students learn complex engineering tasks and the affective states that accompany that learning. Co-robot systems that predict the effectiveness of specific intervention strategies for each student and situation will lead to individually-tailored student feedback that serves both students and instructors towards enhancing student performance over time. This proposal advances the impact of co-robots into educational research and practice and extends knowledge of how to succinctly represent the complexities of human behavior in digital form.

NSF国家机器人倡议项目将研究共同机器人系统的观察，推理和干预周期的潜力，以增强学生的情感状态并提高其在工程实验室任务上的表现。共同机器人是与人并排工作的机器人，协助他们并适应他们的需求。学生与共同机器人之间的知识交流形成了互惠的关系，在这种关系中，各方都从对方学习一个共同目标。情感状态，例如挫败感和参与，在学生在日常学习任务上的表现中起着重要作用。过于压力或分心的学生可能会犯错误，否则很容易避免。意识到学生情感状态的共同机器人系统可以干预以防止这些错误。该项目的结果可能为远远超出工程的主题提供了基于技能的指导的模板。当前，这样的学习需要学生与讲师之间的广泛互动，教师始终提供密集的反馈。在许多情况下，人格不匹配或教师和学生之间的其他问题会导致挫败感，学习困难和最终辍学。此外，一对一的学习受到可伸缩性挑战的限制，因为增加的学生人数的增加，而训练有素的教师没有成比例的增加，可能会导致分配给每个学生的教师时间的质量和数量下降。共同机器人学习系统将能够通过提供适应学生个人需求的实时和可扩展的反馈系统来减轻这些挑战，并有助于最大程度地减少每个学生所需的人类教师时间的数量。这项研究将使用共同行动的集成视觉，音频和深度感觉系统从学生那里获取面部，听觉和身体手势数据。 该系统将根据面部和肢体语言数据的机器学习分类来对学生的情感状态进行统计推断。 视觉反馈将用于向学生提供干预措施（视觉说明和评论），以增强其情感状态并提高其在实验室任务上的表现。 该项目将评估共同机器人改善学生情感状态的能力的影响，并在重复学习和测试中提高学生对实验室任务的表现。 该项目将在潜在的压力实验室活动中更好地了解学生如何互动和功能。这项工作中提出的共同机器人系统将有助于发现学生情感和任务表现之间存在的相关性。联合机组人将积极适应学生学习复杂的工程任务以及随之而来的情感状态的方式。预测每个学生和情况特定干预策略有效性的共同机器人系统将导致单独限制的学生反馈，从而为学生和讲师提供服务，以随着时间的推移提高学生的表现。该提议促进了共同机器人对教育研究和实践的影响，并扩展了如何简洁地代表数字形式的人类行为复杂性的知识。