Collaborative Research: EAGER: Foundations of Secure Multi-Robot Computation

协作研究：EAGER：安全多机器人计算的基础

• 批准号: 2034123
• 负责人: Leonardo Bobadilla
• 金额: $ 9.75万
• 依托单位: Florida International University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2023-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2034123&HistoricalAwards=false
• 关键词: Collaborative; Research; EAGER; Foundations; Secure

As people are starting to face the prospect of robots becoming part of our everyday lives, it has become increasingly clear that the information robots could gather can be both sensitive and valuable. But the robots may need to gather this information in order to function properly: as elsewhere in our lives, we need to understand how to best reconcile the tension between utility and privacy. The scientific progress made to date on algorithms for planning, control, and coordination of multi-robot systems has been enormous, but it also has paid too little attention to "who knows what." This research effort sets out to understand how the essential computational operations underlying many common robotic tasks can be safely accomplished in circumstances where there is some doubt about the integrity of other elements in the system, including whether they can be trusted to never expose information. This is crucial for autonomous robots operating within socially sensitive settings, as well as contested or adversarial scenarios. Beyond the anticipated impact on robotics research, the project will benefit society by addressing questions of strategic national interest and help facilitate privacy protections. It includes education and outreach activities that serve underrepresented groups, firstly via direct engagement with undergraduate and graduate students at Florida International University and Texas A&M, and secondly, in working with and mentoring high school teachers. The project will conduct both theoretical and empirical research, through a multi-part research agenda that will enable privacy-preserving filtering and planning in multi-robot scenarios via secure multi-party computation methods. This research endeavor represents a radical departure from present computational assumptions for robots: it aims to introduce abstractions, algorithms, and systems to solve robot tasks in scenarios characterized by collaboration between mutually distrusting robots, this is the first systematic effort to do so. The research will allow multiple robots to coordinate their use of shared resources, without divulging sensitive information that each robot possesses, despite their effective cooperation actually depending on that sensitive information. The research program will produce: (1) a new set of geometric primitives that allow the solution of motion planning (and related) problems in a privacy-preserving fashion; (2) novel filters, constructed to preserve privacy; and (3) constructions for calculating and querying computational topology properties, subject to limits on information shared. Together, these pieces lay groundwork, establishing the research area of secure multi-robot computation.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.

随着人们开始面对机器人成为我们日常生活一部分的前景，越来越清楚的是，机器人可以收集的信息既敏感又有价值。但机器人可能需要收集这些信息才能正常工作：就像我们生活中的其他方面一样，我们需要了解如何最好地协调实用性和隐私之间的紧张关系。迄今为止，在多机器人系统的规划、控制和协调算法方面取得的科学进展是巨大的，但它对“谁知道什么”的关注也太少。这项研究工作旨在了解在系统中其他元素的完整性存在一些疑问的情况下，如何安全地完成许多常见机器人任务的基本计算操作，包括是否可以信任它们永远不会泄露信息。这对于在社会敏感环境中操作的自主机器人，以及有争议或对抗的场景至关重要。除了对机器人研究的预期影响之外，该项目还将通过解决战略国家利益问题和帮助促进隐私保护来造福社会。它包括为代表性不足的群体提供教育和拓展活动，首先是通过与佛罗里达国际大学和德克萨斯农工大学的本科生和研究生直接接触，其次是与高中教师合作并指导他们。该项目将通过多部分研究议程进行理论和实证研究，通过安全的多方计算方法，在多机器人场景中实现隐私保护过滤和规划。这项研究的努力代表了对目前机器人计算假设的彻底背离：它旨在引入抽象、算法和系统来解决机器人任务，这些任务以相互不信任的机器人之间的协作为特征，这是第一次系统地这样做。该研究将允许多个机器人协调它们对共享资源的使用，而不会泄露每个机器人拥有的敏感信息，尽管它们的有效合作实际上依赖于这些敏感信息。该研究计划将产生：(1)一组新的几何原语，允许以保护隐私的方式解决运动规划（及相关）问题；(2)为保护隐私而设计的新型过滤器；(3)计算和查询计算拓扑属性的结构，但受信息共享的限制。这些部分共同奠定了基础，建立了安全多机器人计算的研究领域。该奖项反映了美国国家科学基金会的法定使命，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。

项目成果

Multi-Robot Information Gathering Subject to Resource Constraints

受资源限制的多机器人信息采集

• DOI: 10.1109/ro-man50785.2021.9515447
• 发表时间: 2021
• 期刊: 2021 30th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN
• 作者: Al Redwan Newaz, Abdullah;Alam, Tauhidul;Mondello, Joseph;Johnson, Jonathan;Bobadilla, Leonardo
• 通讯作者: Bobadilla, Leonardo

Combining Remote and In-situ Sensing for Persistent Monitoring of Water Quality

结合遥感和现场传感来持续监测水质

• DOI: 10.1109/oceanschennai45887.2022.9775339
• 发表时间: 2022
• 期刊: OCEANS 2022 - Chennai
• 作者: Rojas, Cesar A.;Reis, Gregory M.;Albayrak, Arif R.;Osmanoglu, Batuhan;Bobadilla, Leonardo;Smith, Ryan N.
• 通讯作者: Smith, Ryan N.

Towards Learning Ocean Models for Long-term Navigation in Dynamic Environments

学习动态环境中长期导航的海洋模型

• DOI: 10.1109/oceanschennai45887.2022.9775460
• 发表时间: 2022
• 期刊: OCEANS 2022 - Chennai
• 作者: Padrao, Paulo;Dominguez, Alberto;Bobadilla, Leonardo;Smith, Ryan N.
• 通讯作者: Smith, Ryan N.

Oblivious Markov Decision Processes: Planning and Policy Execution

忽视马尔可夫决策过程：规划和政策执行

• DOI: 10.1109/cdc49753.2023.10383231
• 发表时间: 2023
• 期刊: IEEE
• 作者: Alsayegh, Murtadha;Fuentes, Jose;Bobadilla, Leonardo;Shell, Dylan A.
• 通讯作者: Shell, Dylan A.

Digital Twins Utilizing XR-Technology as Robotic Training Tools

利用 XR 技术作为机器人培训工具的数字孪生

• DOI: 10.3390/machines11010013
• 发表时间: 2023
• 期刊: Machines
• 影响因子: 2.6
• 作者: Kaarlela, Tero;Padrao, Paulo;Pitkäaho, Tomi;Pieskä, Sakari;Bobadilla, Leonardo
• 通讯作者: Bobadilla, Leonardo