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

CRII：CHS：利用隐式人类提示为协作机器人设计有效的行为

• 批准号: 1566612
• 负责人: Daniel Szafir
• 金额: $ 17.43万
• 依托单位: University of Colorado at Boulder
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-06-15 至 2019-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1566612&HistoricalAwards=false
• 关键词: CRII; CHS; Leveraging; Implicit; Human

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).

机器人有可能通过与关键领域的人们进行积极合作，包括制造，医疗保健和太空探索，从而显着受益。 但是，为了提供有效的帮助，机器人必须能够以自然，直观和社会熟练的方式与人们合作。 当前的人类机器人合作要求人们明确地传达其目标并对机器人合作伙伴的理想回应。 结果，联合人机活动与涉及人类团队合作的场景几乎没有相似之处，在那里人们能够理解伴侣的隐式线索，例如眼睛目光，面部表情和语调以及适当的响应，例如移动到某个位置，可以预先提取工具或提供澄清。 PI在该项目中的目标是建立一个研究计划，该计划将通过开发计算模型来探索为协作机器人的有效行为设计，从而使他们能够通过推断人类的意图来感知隐式人类交流提示和指导机器人的响应，并评估新算法在人类机器人研究中的有效性。 这项研究具有巨大的希望，可以通过帮助实现作为人类工作的主要贡献者，合作伙伴和助手的机器人的愿景来使社会受益的巨大希望，并在包括家庭董事会工作，制造业，建筑，医疗保健和太空探索等各种活动中进行了应用。 除了将项目成果传播到更大的研究社区外，PI还将基于他过去的成功推广活动，为以机器人技术和计算机科学教育为中心的K-12夏季计划提供机会。为此，PI将解决设计有效的协作机器人的挑战，该挑战是通过开发有意义的方法和对人类沟通性行为的方法来设计有效的协作机器人。 他的方法将涉及（1）观察和分类对参与原型协作任务的人类人类团队的隐式线索和响应，（2）开发目标，提示和响应之间的关系的计算模型，以及使用观察到的行为提取的特征和参数之间的响应和参数，（3）与cue协作和响应的案例相结合，以指导robiith and private private prictive and private pristion and private and privation and pristion and privation and decival oferanms candiors（3）这些行为对协作任务成果的有效性。 这项研究将为协作机器人提供一套可推广的设计原则，生成开源算法，展示实际实现，并提高有关对人类行为的计算理解的知识。 总体而言，这项工作将导致能够与人更有效地合作并加速辅助机器人进入社会的机器人。 它将综合人类交流的理论，并探索他们在人类机器人互动中的应用，并促进有关机器人如何为人类合作者提供帮助的知识以及与人合作伙伴紧密合作的机器人所必需的传感器类型。 在HRI实验中经过经验验证的隐式感测和响应算法将被传播为开源机器人操作系统（ROS）的模块。