NRI: INT: COLLAB: Collaborative Task Planning and Learning through Language Communication in a Human-Robot Team.

NRI：INT：COLLAB：人机团队中通过语言交流进行协作任务规划和学习。

• 批准号: 1830282
• 负责人: Julie Shah
• 金额: $ 73.16万
• 依托单位: Massachusetts Institute of Technology
• 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: 2018-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1830282&HistoricalAwards=false
• 关键词: NRI; INT; COLLAB; Collaborative; Task

When deployed in the field, robots will often encounter new situations or new tasks they don't have any knowledge or experience about. Even given sufficient knowledge, designing planners that can generate high quality plans and perform efficiently across various domains remains an open challenge. To address these issues, this project aims to empower robots to harness human expertise to acquire new knowledge and to engage humans in the loop of plan generation so that humans and robots can collectively arrive at a joint plan. The results will lead to principles and computational models for enabling effective human-robot teams that can adapt to new and changing environments and tasks, which will benefit many applications such as manufacturing, service, assistive technology, and search and rescue. This project will also provide new exciting training and education opportunities for students through research mentoring and curriculum development. This project investigates how humans and robots strive to mediate goals, world models, and plans to establish common ground for joint tasks. It will develop a computational framework that tightly links language and dialogue processing with the robot's underlying planning system to support collaborative task planning and learning in a human-robot team. It further will evaluate collaborative model acquisition and plan generation in terms of consistency of shared understanding, plan quality, and situational awareness. The research will transform planning in a human-robot team by integrating human expertise and knowledge in a collaborative process to improve planning and task performance. It will endow the robot with an ability to explain its internal states, goals and plans, and to continuously learn new states, actions, and plans through language communication with human partners. It will also advance language and dialogue research by providing a rich context for studying grounded semantics of language.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.

当部署在现场时，机器人经常会遇到他们没有任何知识或经验的新情况或新任务。即使拥有足够的知识，设计能够生成高质量计划并在各个领域高效执行的规划器仍然是一个开放的挑战。为了解决这些问题，该项目旨在使机器人能够利用人类的专业知识来获取新知识，并使人类参与计划生成的循环，以便人类和机器人能够共同达成联合计划。研究结果将产生原理和计算模型，使有效的人机团队能够适应新的和不断变化的环境和任务，这将有利于制造、服务、辅助技术以及搜索和救援等许多应用。该项目还将通过研究指导和课程开发为学生提供新的令人兴奋的培训和教育机会。该项目研究人类和机器人如何努力协调目标、世界模型，并计划为联合任务建立共同点。 它将开发一个计算框架，将语言和对话处理与机器人的底层规划系统紧密联系起来，以支持人机团队的协作任务规划和学习。它将进一步从共享理解的一致性、计划质量和态势感知方面评估协作模型获取和计划生成。该研究将通过在协作过程中整合人类专业知识和知识来改变人机团队的规划，以提高规划和任务绩效。它将赋予机器人解释其内部状态、目标和计划的能力，并通过与人类伙伴的语言交流不断学习新的状态、行动和计划。 它还将通过为研究语言基础语义提供丰富的背景来推进语言和对话研究。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。

项目成果

Overcoming Blind Spots in the Real World: Leveraging Complementary Abilities for Joint Execution

• DOI: 10.1609/aaai.v33i01.33016137
• 发表时间: 2019-07
• 作者: Ramya Ramakrishnan;Ece Kamar;Besmira Nushi;Debadeepta Dey;J. Shah;E. Horvitz