CRII: CHS: Leveraging Implicit Human Cues to Design Effective Behaviors for Collaborative Robots

机器人有潜力通过在制造、医疗保健和太空探索等关键领域与人们积极合作，为社会带来巨大利益。 但为了提供有效的帮助，机器人必须能够以自然、直觉和社交能力强的方式与人合作。 当前的人机协作要求人们明确地向机器人合作伙伴传达他们的目标和期望的响应。 因此，人类-机器人联合活动与涉及人类-人类团队合作的场景几乎没有相似之处，在人类-人类团队合作中，人们能够理解他们的伴侣的隐含暗示，例如眼睛凝视，面部表情和语调，并直觉地做出适当的反应，例如移动到某个位置，抢先获取工具或提供澄清。 PI在这个项目中的目标是建立一个研究计划，通过开发计算模型，使他们能够感知隐含的人类交流线索，并通过推断线索意图来指导机器人的反应，并评估新算法在人类机器人研究中的有效性，从而探索协作机器人的有效行为设计。 这项研究具有造福社会的重大前景，有助于实现机器人在人类工作中充当关键贡献者、合作伙伴和助手的愿景，其应用范围包括家务劳动、制造业、建筑业、医疗保健和太空探索。 除了向更大的研究社区传播项目成果外，PI还将在过去成功的外展活动的基础上，为以机器人和计算机科学教育为中心的K-12暑期项目提供机会。为此，PI将通过开发初步框架，流程，以及一套感知和回应人类隐性交际行为的方法。 他的方法将涉及（1）观察和分类参与原型协作任务的人类-人类团队的隐式提示和响应，（2）使用从观察到的行为中提取的特征和参数开发目标，提示和响应之间关系的计算模型，（3）集成隐式提示感知和响应算法，以指导特定协作用例中的机器人行为，以及（4）评估这些行为对协作任务结果的有效性。 这项研究将为协作机器人产生一套可推广的设计原则，生成展示实际实现的开源算法，并推进有关人类行为的计算理解的知识。 总的来说，这项工作将使机器人能够更有效地与人合作，并加速辅助机器人融入社会。 它将综合人类沟通的理论，并探索它们在人机交互中的应用，以及关于机器人如何作为人类合作者提供帮助以及机器人与人类合作伙伴密切合作所需的传感器类型的知识。 在HRI实验中已经经验验证的隐式传感和响应算法将作为开源机器人操作系统（ROS）的模块进行传播。

Robots have the potential to significantly benefit society by actively collaborating with people in critical domains including manufacturing, healthcare, and space exploration. But to provide effective assistance, robots must be able to work with people in a natural, intuitive, and socially adept manner. Current human-robot collaborations require that people explicitly communicate their goals and desired responses to robotic partners. As a result, joint human-robot activities bear little resemblance to scenarios involving human-human teamwork, where people are able to understand their partner's implicit cues, such as eye gaze, facial expressions, and intonations, and intuit appropriate responses, such as moving to a certain location, preemptively fetching a tool, or providing a clarification. The PI's goal in this project is to establish a research program that will explore the design of effective behaviors for collaborative robots by developing computational models that enable them to sense implicit human communicative cues and guide robot responses by inferring cue intent, and to evaluate the effectiveness of the new algorithms in human-robot studies. The research holds significant promise of benefiting society by helping to achieve a vision of robots acting as key contributors, partners, and assistants in human work, with applications across a range of activities including domestic housework, manufacturing, construction, healthcare, and space exploration. In addition to disseminating project outcomes to the larger research community, the PI will build on his successful past outreach activities to provide opportunities for K-12 summer programs centered on robotics and computer science education.To these ends, the PI will address the challenge of designing effective collaborative robots by developing a preliminary framework, process, and set of methods to sense and respond to implicit human communicative behaviors. His approach will involve (1) observing and classifying implicit cues and responses for human-human teams engaged in an archetypical collaborative task, (2) developing computational models of the relationships between goals, cues, and responses using features and parameters extracted from observed behaviors, (3) integrating implicit cue sensing and response algorithms to guide robot behaviors in specific collaborative use cases, and (4) evaluating the effectiveness of these behaviors on collaborative task outcomes. This research will produce a set of generalizable design principles for collaborative robots, generate open-source algorithms showcasing practical implementations, and advance knowledge regarding computational understanding of human behaviors. Overall, the work will lead to robots that are able to work more effectively with people and accelerate the integration of assistive robots into society. It will synthesize theories of human communication and explore their application to human-robot interaction, as well as advancing knowledge regarding how robots might provide assistance as human collaborators and the types of sensors necessary for robots working closely with human partners. Implicit sensing and response algorithms that have been empirically validated in HRI experiments will be disseminated as modules for the open-source Robotic Operating System (ROS).