Wisdom of the swarm for cooperative decision-making in human-swarm interaction

Human-swarm interaction (HSI) is a developing field of research in which the problem of gesture-based control has been attracting an increasing attention, being at the same time a natural form of interaction and an effective way to point and select individual or groups of robots in the swarm. Gesture-based interaction usually requires vision-based recognition and classification of the gesture from the swarm. At this aim, existing methods for cooperative sensing and recognition make use of distributed consensus algorithms, which include for instance averaging and frequency counting. In this work we present a distributed consensus protocol that allows robot swarms to learn efficiently gestures from online interactions with a human teacher. The protocol also facilitates the integration of different consensus algorithms. Experiments have been performed in emulation using on real data acquired by a swarm of robots. The results indicate that effectively exploiting the collective decision-making of the swarm is a viable way to rapidly achieve good learning performance.