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CRII: CHS: Identifying When People Need a Robot's Assistance

CRII：CHS：识别人们何时需要机器人的帮助

基本信息

批准号：
1755823
负责人：
Henny Admoni
金额：
$ 17.5万
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：
2018
资助国家：
美国
起止时间：
2018-03-15 至 2021-02-28
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1755823&HistoricalAwards=false
关键词：
CRII CHS Identifying When People

项目摘要

Robot collaborators and assistants have the potential to improve lives by helping people perform physical tasks more safely, quickly, and effectively. For example, wheelchair-mounted assistive robot arms can help people with motor impairments perform activities of daily living (like eating) independently, increasing their self-sufficiency and quality of life. However, robot assistance is limited by the fact that robots cannot always recognize when people want or need help. The goal of this research is to develop algorithms that enable robots to recognize when a person is having difficulty with a physical task, based on their behavior before they reach a failure point, and then provide the necessary assistance to complete the task. This work will draw from psychology to explore how nonverbal behaviors like eye gaze, body posture, and facial expression can reveal people's need for assistance. The project will include a data collection study of nonverbal behavior during robot operation. The nonverbal behavior collected during this study will be open sourced to enable other researchers to draw insights about human behavior during human-robot interactions. The work will improve the usefulness of collaborative and assistive robots and lead to better integration of personal robots in workplaces, homes, and assistive care environments.The main research question in this work is: can robots recognize that a person needs assistance based on their nonverbal behaviors during a human-robot interaction? To investigate this, the project has four goals. Goal 1: Recognize the need for assistance. The work will begin with a large-scale data collection of people's nonverbal behavior (eye gaze, body posture, and facial expressions) during an assistive human-robot manipulation task. Using machine learning approaches, the project team will train predictors on the data that can use nonverbal behavior patterns to recognize when people need assistance. Goal 2: Provide assistance. By monitoring real-time nonverbal behaviors during a human-robot interaction, this system will use the trained predictors from Goal 1 to identify when a person needs help. Once the system predicts that assistance is required, it should be able to provide that assistance seamlessly and in real time using shared autonomy. Goal 3: Evaluate the system. Individual system components will be validated separately, then a full-scale evaluation will be conducted to measure the utility of the implemented system in a real-world assistive human-robot interaction. Goal 4: Create and disseminate an open source data set. A major goal of this project is to collect and share a data set of nonverbal behavior during assistive human-robot interaction, which represents a novel contribution to the field.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.

机器人协作者和助手有潜力通过帮助人们更安全、快速、有效地执行体力任务来改善生活。例如，安装在轮椅上的辅助机器人手臂可以帮助有运动障碍的人独立进行日常生活活动（例如吃饭），提高他们的自给自足和生活质量。然而，机器人援助受到以下事实的限制：机器人无法总能识别人们何时需要或需要帮助。这项研究的目标是开发算法，使机器人能够根据人在达到故障点之前的行为来识别人何时在执行体力任务时遇到困难，然后提供必要的帮助来完成任务。这项工作将从心理学的角度探讨眼神、身体姿势和面部表情等非语言行为如何揭示人们对帮助的需求。该项目将包括对机器人操作过程中非语言行为的数据收集研究。本研究期间收集的非语言行为将开源，以便其他研究人员能够深入了解人机交互过程中的人类行为。这项工作将提高协作和辅助机器人的实用性，并使个人机器人更好地融入工作场所、家庭和辅助护理环境中。这项工作的主要研究问题是：机器人能否根据人机交互过程中的非语言行为识别出一个人需要帮助？为了调查这一点，该项目有四个目标。目标 1：认识到援助的需要。这项工作将从在辅助人类机器人操作任务期间收集人们的非语言行为（眼神、身体姿势和面部表情）的大规模数据开始。使用机器学习方法，项目团队将根据数据训练预测器，这些预测器可以使用非语言行为模式来识别人们何时需要帮助。目标 2：提供援助。通过监控人机交互过程中的实时非语言行为，该系统将使用目标 1 中经过训练的预测器来识别一个人何时需要帮助。一旦系统预测需要帮助，它应该能够利用共享自主权无缝、实时地提供帮助。目标 3：评估系统。各个系统组件将分别进行验证，然后进行全面评估，以衡量所实施系统在现实世界辅助人机交互中的效用。目标 4：创建并传播开源数据集。该项目的一个主要目标是收集和共享辅助人机交互过程中的非语言行为数据集，这代表了对该领域的新颖贡献。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。