CAREER: Toward Proactive Assistive Robotics

辅助机器人有可能在许多方面提高人类的效率和生活质量，例如使患有严重运动障碍的人能够独立完成日常生活活动（ADL）。然而，大多数为辅助人机交互（HRI）设计的机器人都是反应性的，要么提供“永远在线”的帮助，要么只响应明确的请求，而不是主动帮助他们的伙伴实现目标。HRI的下一个重大进展将涉及能够主动预测和响应人们需求的机器人，就像经验丰富的护理人员一样。要做到这一点，机器人必须观察和推理上下文和人类行为的潜在微妙线索。例如，某人正在看的东西可以揭示他们的下一步行动或表明他们需要帮助完成任务。该项目研究人类行为和环境如何揭示与任务相关的心理状态，然后开发辅助机器人，在各种日常情况下为人们提供支持。在研究过程中，本项目还将通过开发使用辅助机器人作为有效HRI示例的HRI课程和文本，产生教育影响。本项目将首先研究人类眼睛的凝视如何揭示人们在日常生活活动中何时以及如何需要帮助，如准备食物和进食。然后将开发辅助算法，监测眼睛注视并主动响应机器人动作。这些算法将在研究中进行评估，用户有上运动障碍，可以从新技术中受益。（1）计算模型，使用人类的眼睛注视和任务上下文来预测与任务相关的人类心理状态，例如当前动作的目标，要选择的下一个动作，以及某人是否在任务中遇到困难;（2）应用心理状态模型的算法，以使机器人能够主动协助协同操作，在正确的时间提供正确类型的协助;（3）在具有运动障碍的用户的真实环境中评估主动机器人辅助算法;（4）机器人辅助类型的新本体，从一阶和二阶辅助的初始定义开始，并通过文献调查和访谈向外扩展;（5）将共享自治算法从单步任务扩展到多步任务，该奖项反映了NSF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。

Assistive robots have the potential to increase human efficiency and quality of life in many ways, such as enabling people with severe motor impairments to independently complete activities of daily living (ADLs). However, most robots designed for assistive human-robot interaction (HRI) are reactive, either providing "always on" assistance or responding only to explicit requests, rather than proactively taking initiative to help with their partner's goal. The next major advance in HRI will involve robots that are able to proactively anticipate and respond to people's needs, just as an experienced caregiver does. To do this, robots must observe and reason about potentially subtle cues of context and human behavior. For example, what someone is looking at can reveal their next action or indicate that they need help with a task. This project investigates how human behavior and context reveal task-relevant mental states, and then develops assistive robots that provide support to people in a variety of everyday situations. In the process of doing the research, this project will also make an educational impact by developing HRI courses and texts that use assistive robotics as an example of effective HRI.This project will first investigate how human eye gaze can reveal when and how people need assistance in activities of daily living like food preparation and eating. Assistance algorithms will then be developed that monitor eye gaze and proactively respond with robot actions. These algorithms will be evaluated in studies with users who have upper motor impairments and could benefit from the new technology. To these ends, the project contributes: (1) Computational models that use a human's eye gaze and task context to predict task-relevant human mental states such as the target of the current action, the next action to be selected, and whether someone is having difficulty with the task; (2) algorithms that apply mental state models to enable proactive robot assistance in co-manipulation, providing the right type of assistance at the right time; (3) an evaluation of proactive robot assistance algorithms in real-world contexts with users who have motor impairments; (4) a novel ontology of robot assistance types, starting with initial definitions of first- and second-order assistance and expanding outward through literature surveys and interviews; and (5) an extension of shared autonomy algorithms from single-step tasks to multi-step tasks, with a particular focus on ADLs.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.