Multi-agent playbook for human-robot teaming

Effective human-robot teaming requires human and robot teammates to share a common understanding of the goals of their collaboration. Ideally, a complex task can be broken into smaller components to be performed by team members with defined roles, and the plan of action and assignment of roles can be changed on the fly to accommodate unanticipated situations. In this paper we describe research on adaptive human-robot teaming that uses a playbook approach to team behavior to bootstrap multi-agent collaboration. The goal is to leverage known good strategies for accomplishing tasks, such as from training and operating manuals, to enable humans and robots to "be on the same page" and work from a common knowledge base. Simultaneous and sequential actions are specified using hierarchical text-based plans and executed as behavior trees using finite state machines. We describe a real-time implementation that supports sharing of task status updates through distributed message passing. Tasks related to human-robot teaming for exploration and reconnaissance are explored with teams comprising humans wearing augmented reality headsets and quadruped robots. It is anticipated that shared task knowledge provided by multi-agent playbooks will enable humans and robots to track and predict teammate behavior and promote team transparency, accountability and trust.