  We propose a novel Human-Swarm Interaction (HSI) framework which enables the
user to control a swarm shape and formation. The user commands the swarm
utilizing just arm gestures and motions which are recorded by an off-the-shelf
wearable armband. We propose a novel interpreter system, which acts as an
intermediary between the user and the swarm to simplify the user's role in the
interaction. The interpreter takes in a high level input drawn using gestures
by the user, and translates it into low level swarm control commands. This
interpreter employs machine learning, Kalman filtering and optimal control
techniques to translate the user input into swarm control parameters. A notion
of Human Interpretable dynamics is introduced, which is used by the interpreter
for planning as well as to provide feedback to the user. The dynamics of the
swarm are controlled using a novel decentralized formation controller based on
distributed linear iterations and dynamic average consensus. The framework is
demonstrated theoretically as well as experimentally in a 2D environment, with
a human controlling a swarm of simulated robots in real time.
