S&AS:FND:COLLAB: Planning Coordinated Event Observation for Structured Narratives

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• 批准号: 1849291
• 负责人: Jason O'Kane
• 金额: $ 20万
• 依托单位: University of South Carolina at Columbia
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-03-15 至 2023-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1849291&HistoricalAwards=false
• 关键词: S& FND; COLLAB; Planning; Coordinated

People easily recognize the dramatic moments that unfold in human events. Dramatic turns of events are key to recognizing and communicating effective reports or stories about events. Autonomous systems will work more effectively with humans in obtaining and conveying such narrative when they too can recognize what is dramatic (or tragic, or comical) about human events. The challenge is to effectively convey such concepts to a computer in such a way that humans and autonomous systems can effectively work together in this. This research studies how to direct a team of robots to obtain video footage to produce clips that trace a dramatic story arc. It is an examination of how such systems might achieve goals that people consider to be abstract or high-level. Within this project, the programs that command teams of robots must predict likely events, direct the robots to be in position for obtaining the desired footage, and re-plan based on observed events. This challenge encompasses a rich and previously unstudied class of problems for robot systems. It will constitute a unique demonstration of robots that are capable of achieving high-level goals as they process data in forms which combine both continuous and discrete views of the world in a new and unusual way. More broadly, the research will advance how computers can fuse and summarize video streams. Both skills are needed for automatically generating synopses and in editing videos. Obvious places where this is useful include helping secure the nation (for surveillance), taming the deluge of online multimedia content (for summarization), and advancing applications in the creative industries (for editing). The research project will also use the ideas underlying these pieces in a new robotics course with students at three institutions going head-to-head in a series of competition-based class projects. This course (taught, among other places, at a Hispanic-Serving Institution) will contribute to the development of the STEM workforce of the future, helping increase American competitiveness.The project advances current knowledge by formulating new theory and developing novel algorithms for autonomous and robot systems, with a focus on those systems with minimal or no human operator intervention. The research contributes novel data representations for robots that will inhabit rich environments such as those characterized by uncertain, unanticipated, and dynamically changing circumstances. One of the foundational ideas of the project is a means to specify sophisticated mission objectives via a recursive structure using prior work in compiler theory for computer languages. The project involves a strong connection between this theoretical work and demonstrated systems.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.

人们很容易地认识到人类事件中发生的戏剧性时刻。 事件的戏剧性转弯是识别和传达有关事件的有效报告或故事的关键。 自主系统将在人类获得和传达这种叙事方面更有效地工作，而他们也可以认识到人类事件的戏剧性（或悲惨或可笑）。 面临的挑战是，将这种概念有效地传达给计算机，以使人类和自主系统可以有效地合作。这项研究研究了如何指导机器人团队获取视频录像以制作追踪戏剧性故事弧的剪辑。这是对这些系统如何实现人们认为是抽象或高级目标的目标的研究。在该项目中，指挥机器人团队的程序必须预测可能的事件，指导机器人可以获取所需录像，并根据观察到的事件重新计划。这项挑战涵盖了机器人系统的丰富且以前未研究的问题。 它将构成机器人的独特演示，这些机器人能够以新的和不寻常的方式以形式处理数据，以实现高级目标。 更广泛地说，研究将推进计算机如何融合和总结视频流的方式。自动生成概要和编辑视频所需的两种技能。显而易见的地方有用的地方包括帮助确保国家（用于监视），驯服在线多媒体内容（用于摘要）以及在创意产业中推进应用程序（用于编辑）。该研究项目还将在新的机器人课程中使用这些想法，其中三个机构的学生在一系列基于竞赛的课堂项目中正对面。本课程（在西班牙裔服务机构中教授的课程）将有助于发展未来的STEM劳动力，帮助提高美国的竞争力。机器人系统，专注于那些具有最少或没有人类操作员干预的系统。该研究为机器人提供了新的数据表示，这些机器人将居住在富裕环境中，例如以不确定，意外和动态变化的环境为特征。该项目的基本思想之一是一种通过递归结构来指定复杂的任务目标，该结构使用计算机语言的编译器理论中的先前工作。该项目涉及这项理论工作与展示系统之间的密切联系。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的评论标准来评估值得支持的。

项目成果

Planning to chronicle: Optimal policies for narrative observation of unpredictable events

规划编年史：对不可预测事件进行叙事观察的最佳策略

• DOI: 10.1177/02783649211069154
• 发表时间: 2022
• 期刊: The International Journal of Robotics Research
• 作者: Rahmani, Hazhar;Shell, Dylan A.;O’Kane, Jason M.
• 通讯作者: O’Kane, Jason M.

Planning to Chronicle

计划编年史

• DOI: 10.1007/978-3-030-66723-8_17
• 发表时间: 2020
• 期刊: Workshop on the Algorithmic Foundations of Robotics
• 作者: Rahmani, Hazhar;Shell, Dylan A.;O'Kane, Jason M.
• 通讯作者: O'Kane, Jason M.

Accelerating combinatorial filter reduction through constraints

• DOI: 10.1109/icra48506.2021.9562036
• 发表时间: 2020-11
• 期刊: 2021 IEEE International Conference on Robotics and Automation (ICRA)
• 作者: Yulin Zhang;Hazhar Rahmani;Dylan A. Shell;J. O’Kane

Tractable Planning for Coordinated Story Capture: Sequential Stochastic Decoupling

协调故事捕捉的易处理规划：顺序随机解耦

• 发表时间: 2021
• 期刊: International Symposium on Distributed Autonomous Robotic Systems
• 作者: Diptanil Chaudhuri, Hazhar Rahmani

Conditioning Style on Substance: Plans for Narrative Observation

实质内容的调节风格：叙事观察计划

• DOI: 10.1109/icra48506.2021.9562095
• 发表时间: 2021
• 期刊: 2021 IEEE International Conference on Robotics and Automation (ICRA
• 作者: Chaudhuri, Diptanil;Ike, Rhema;Rahmani, Hazhar;Shell, Dylan A.;Becker, Aaron T.;O'Kane, Jason M.
• 通讯作者: O'Kane, Jason M.