RI: Small: Collaborative Research: Learning to perform consistently in human/multi-robot teams

该项目的重点是在机器人可以探索不适合人类的区域的情况下，人类/多机器人团队的实际部署。例如，一组“救援”机器人可以在倒塌的建筑物中搜寻受害者，并将其位置发送给外面的人类第一反应人员。在动态环境中管理人/多机器人团队是一个具有挑战性的问题。世界不仅是可变的，而且团队成员可能会因为机器人迷路或人类操作员需要休息而发生变化--世界在变化，探索世界的团队也在变化，本研究的目标是为人类/多机器人团队制定策略，以学习一致有效地执行任务。将追求三个主要目标：第一，通过一个实用的机构记忆框架，记住和使用过去的经验，以减轻团队组成的变化;第二，建模和记录的专业知识，供以后使用的学习行为由人类操作员;第三，有效地分配团队成员之间的任务，通过提供一个平衡的社会选择机制。该项目的新方法适用于广泛的人类/多机器人和人类/多智能体团队，通过整合机构记忆，向人类队友学习，并解决不同观点之间的冲突。这些策略将使用一个人/多机器人试验台进行评估，该试验台由一个人操作员加上一组廉价的、功能有限的机器人组成。虽然每个机器人的移动性和感知能力有限，但团队成员共同构成了一个多功能的人类/多机器人设施。该项目解决了鲁棒智能的重要挑战，包括行为建模，从经验中学习，做出协调决策，以及在不确定性下进行推理。预期成果包括人类/多机器人团队的策略，这些团队学习在各种条件下有效协作，并且尽管团队成员在运行时发生变化，也可以保持他们的性能，以及人们如何与机器人团队互动的知识。更广泛的影响包括为远程合作者提供网络实验测试平台;出版针对研究生，本科生和高中生的多机器人团队的经过验证的课程材料;让本科生参与研究活动;与当地博物馆的现有联系人合作，向公众展示成果。

This project focuses on practical deployment of human/multi-robot teams in situations where robots can explore regions that are unsuitable for humans. For example, a team of "rescue" robots can sweep through a collapsed building searching for victims and transmit their positions to human first-responders outside. Managing a human/multi-robot team in a dynamic environment is a challenging problem. Not only is the world mutable, but also the team can experience altered membership because a robot gets lost or a human operator needs rest---the world is changing, and so is the team that is exploring that world.The goal of this research is to develop strategies for human/multi-robot teams to learn to perform consistently and effectively. Three primary aims will be pursued: first, to mitigate changes in team composition via a practical framework for institutional memory that remembers and uses past experiences; second, to model and record expertise for later use by learning behaviors performed by a human operator; and third, to distribute tasks among team members efficiently by providing a balanced mechanism for social choice. The novel approach of this project is applicable to a broad spectrum of human/multi-robot, and human/multi-agent teams, by integrating institutional memory, learning from human teammates, and resolving conflict among differing perspectives. The strategies will be evaluated using a human/multi-robot testbed comprised of one human operator plus a heterogeneous set of inexpensive, limited-function robots. Although each individual robot has restricted mobility and sensing capabilities, together the team members constitute a multi-function, human/multi-robot facility.This project addresses important challenges in robust intelligence, including behavior modeling, learning from experience, making coordinated decisions, and reasoning under uncertainty. Expected outcomes include strategies for human/multi-robot teams that learn to collaborate effectively under a variety of conditions and can maintain their performance despite run-time changes in team membership, as well as knowledge about how people interact with robot teams. Broader impacts include providing access to a networked experimental testbed for remote collaborators; publishing proven curricular materials on multi-robot teams addressed to graduate, undergraduate and high school students; involving undergraduates in research activities; and working with existing contacts at local museums to demonstrate results to the general public.