CAREER: Robots that Plan Interactions, Come and Go, and Build Trust

职业：规划交互、来来去去并建立信任的机器人

基本信息

批准号：
2046770
负责人：
Ryan Williams
金额：
$ 56.99万
依托单位：
Virginia Polytechnic Institute and State University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-04-01 至 2026-03-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2046770&HistoricalAwards=false
关键词：
CAREER Robots Plan Interactions Come

项目摘要

After decades of fundamental advances, we are beginning to see meaningful influence of autonomous robots on crucial social and economic problems. While tremendously exciting, these advances are primarily seen in single robot systems, a natural progression given the highly complex nature of multi-robot systems. In order to scale to real-world problems, this Faculty Early Career Development (CAREER) project advances multi-robot system theory to allow robots to plan their interactions intelligently, gracefully enter and exit systems, and participate in trustful decision-making processes with other robots and human teammates. In addition, the theoretical work in this project applies to multi-robot multi-human search and rescue. Indeed, when robots and humans search for a lost person in large wilderness, the theoretical advancements listed above will prove invaluable. Finally, this project includes a comprehensive education and outreach plan consisting of: curriculum focused on autonomy that crosses departmental boundaries; pedagogical programs with an emphasis on persons with a disability; K-12 academic experiences for underrepresented students in engineering; and channels for national and international education and outreach.This project focuses on developing new theory and technologies, including: a framework based on independence systems for modeling robot interaction structures over time with sampling and gradient-based methods for efficiently computing interaction plans; a combinatorial optimization framework for modeling optimally open multi-robot systems yielding plans for robots entering and exiting systems while guaranteeing the correctness of underlying collaborative objectives; a framework for trust-building in collaborative multi-robot multi-human decision-making based on multi-armed bandits, a concept we call the trustful multi-armed bandit; a set of search and rescue case studies for evaluating our research thrusts; and a portable, indoor/outdoor, multi-scale testbed for experimental validation of heterogeneous multi-robot teams. This project addresses fundamental challenges in four areas critical to the flexibility, scalability, and resilience of autonomous coordination: (1) systems that plan their interactions in a manner that adapts to high-level mission objectives and the deployment environment, while respecting low-level collaboration requirements; (2) systems whose composition changes over time while remaining resilient to such changes; (3) systems that select actions that actively build trust from other systems over time; and (4) systems that are prototyped and tested under realistic conditions across varying scales of deployment.This project is supported by the cross-directorate Foundational Research in Robotics program, jointly managed and funded by the Directorates for Engineering (ENG) and Computer and Information Science and Engineering (CISE).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.

经过数十年的基本进步，我们开始看到自主机器人对关键社会和经济问题的有意义的影响。尽管令人兴奋，但这些进步主要是在单个机器人系统中看到的，但鉴于多机器人系统的高度复杂性质，自然的进展。为了扩展到现实世界中的问题，这位教师早期职业发展（职业）项目的进步多机器人系统理论使机器人能够智能，优雅地进入和退出系统，并与其他机器人和人类队友一起智能地计划其互动，并参与信任的决策过程。此外，该项目的理论工作适用于多机器人多人搜索和救援。的确，当机器人和人类在大荒野中寻找失落的人时，上面列出的理论进步将是无价的。最后，该项目包括一项全面的教育和外展计划，该计划包括：课程侧重于跨越部门界限的自主权；强调残疾人的教学计划； K-12的学术经验为工程专业的学生提供了不足的学生；国家和国际教育和推广的渠道。本项目着重于开发新的理论和技术，包括：基于独立系统的框架，用于建模机器人交互结构，随着时间的流逝，采样和基于梯度的方法有效地计算交互计划；一个组合优化框架，用于建模最佳打开多机器人系统，为机器人提供进入和退出系统的计划，同时保证基本协作目标的正确性；一个基于多军强盗的多机器人多人类决策的信任建设框架，我们称之为信任的多臂强盗概念；一组搜救案例研究，用于评估我们的研究推力；以及一个便携式，室内/室外的多尺度测试床，用于实验验证异质多机器人团队。该项目解决了对自主协调的灵活性，可伸缩性和弹性至关重要的四个领域的基本挑战：（1）以适应高级任务目标和部署环境的方式计划其相互作用的系统，同时尊重低级协作要求；（2）其成分随时间变化的系统，同时保持对这种变化的弹性；（3）选择随着时间的推移从其他系统中积极建立信任的系统；（4）在不同规模的部署范围内进行原型和测试的系统。该项目得到了机器人技术计划中的跨领域基础研究的支持，该项目由工程和计算机和信息科学和工程局共同管理和资助。影响审查标准。