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.
