On using mixed-initiative control: A perspective for managing large-scale robotic teams

Prior work suggests that the potential benefits of mixed initiative management of multiple robots are mitigated by situational factors, including workload and operator expertise. In this paper, we present an experiment where allowing a supervisor and group of searchers to jointly decide the correct level of autonomy for a given situation (“mixed initiative”) results in better overall performance than giving an agent exclusive control over their level of autonomy (“adaptive autonomy”) or giving a supervisor exclusive control over the agent's level of autonomy (“adjustable autonomy”), regardless of the supervisor's expertise or workload. In light of prior work, we identify two elements of our experiment that appear to be requirements for effective mixed initiative control of large-scale robotic teams: (a) Agents must be capable of making progress toward a goal without having to wait for human input in most circumstances. (b) The operator control interface must help the human to rapidly understand and modify the progress and intent of several agents.