  In this work, we present preliminary work on a novel method for Human-Swarm
Interaction (HSI) that can be used to change the macroscopic behavior of a
swarm of robots with decentralized sensing and control. By integrating a small
yet capable hand gesture recognition convolutional neural network (CNN) with
the next-generation Robot Operating System \emph{ros2}, which enables
decentralized implementation of robot software for multi-robot applications, we
demonstrate the feasibility of programming a swarm of robots to recognize and
respond to a sequence of hand gestures that capable of correspond to different
types of swarm behaviors. We test our approach using a sequence of gestures
that modifies the target inter-robot distance in a group of three Turtlebot3
Burger robots in order to prevent robot collisions with obstacles. The approach
is validated in three different Gazebo simulation environments and in a
physical testbed that reproduces one of the simulated environments.

Robotics;Multiagent Systems