Sliding Autonomy for Peer-To-Peer Human-Robot Teams

The vision of humans and robots working together as peers to accomplish complex tasks has motivated many recent research endeavors with a variety of applications ranging from lunar construction to soccer. However, much of this research is still at an early stage, and many challenges still remain in realizing this vision. A key requirement for enabling robustness and efficiency in human-robot teams is the ability to dynamically adjust the level of autonomy to optimize the use of resources and capabilities as conditions evolve. While sliding autonomy is well defined and understood in applications where a single human is working with a single robot, it is largely unexplored when applied to teams of humans working with multiple robots. This paper highlights the challenges of enabling sliding autonomy in peer-to-peer human-robot teams and extends the current literature to identify and extend six key capabilities that are essential for overcoming these challenges. These capabilities are requesting help, maintaining coordination, establishing situational awareness, enabling interactions at different levels of granularity, prioritizing team members, and learning from interactions. We demonstrate the importance of several of these characteristics with results from a peer-to-peer human-robot team engaged in a treasure hunt task.