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

机器人合作者和助手有潜力通过帮助人们更安全、更快速、更有效地完成体力劳动来改善生活。例如，安装在轮椅上的辅助机械臂可以帮助运动障碍患者独立进行日常生活活动（如进食），提高他们的自给自足能力和生活质量。然而，机器人的帮助是有限的，因为机器人不能总是识别出人们什么时候想要或需要帮助。这项研究的目标是开发算法，使机器人能够识别人在完成物理任务时遇到困难，基于他们在到达故障点之前的行为，然后提供必要的帮助来完成任务。这项工作将从心理学出发，探索眼神、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。该项目将包括机器人操作过程中非语言行为的数据收集研究。在这项研究中收集的非语言行为将被公开，以使其他研究人员能够在人机交互过程中了解人类的行为。这项工作将提高协作和辅助机器人的实用性，并使个人机器人在工作场所、家庭和辅助护理环境中更好地集成。这项工作的主要研究问题是：机器人能否在人机交互过程中根据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：认识到需要援助。这项工作将从大规模收集人们在辅助人机操作任务中的非语言行为（眼睛注视，身体姿势和面部表情）开始。使用机器学习方法，项目团队将根据数据训练预测者，这些预测者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：提供帮助。通过监测人机交互过程中的实时非语言行为，该系统将使用目标1中训练好的预测器来识别一个人何时需要帮助。一旦系统预测到需要帮助，它应该能够使用共享自主权无缝地实时提供帮助。目标3：评估系统。单个系统组件将被单独验证，然后将进行全面评估，以衡量实现系统在真实世界的辅助人机交互中的效用。目标4：创建和传播开源数据集。该项目的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，这是对该领域的一项新贡献。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.