Personalized Human-Swarm Interaction through Hand Motion
Matteo Macchini, Ludovic De Matte\"is, Fabrizio Schiano, and Dario, Floreano

TL;DR
This paper presents a novel personalized hand motion-based interface for drone swarm teleoperation, leveraging machine learning to adapt to individual user preferences and improve control performance.
Contribution
It introduces a machine learning approach to create personalized human-swarm interfaces using hand motion tracking with LEAP Motion, enhancing portability and user experience.
Findings
Effective personalization of control interfaces for drone swarms.
LEAP Motion enables portable and intuitive hand tracking.
Promising results in simulated environments with user preference adaptation.
Abstract
The control of collective robotic systems, such as drone swarms, is often delegated to autonomous navigation algorithms due to their high dimensionality. However, like other robotic entities, drone swarms can still benefit from being teleoperated by human operators, whose perception and decision-making capabilities are still out of the reach of autonomous systems. Drone swarm teleoperation is only at its dawn, and a standard human-swarm interface (HRI) is missing to date. In this study, we analyzed the spontaneous interaction strategies of naive users with a swarm of drones. We implemented a machine-learning algorithm to define a personalized Body-Machine Interface (BoMI) based only on a short calibration procedure. During this procedure, the human operator is asked to move spontaneously as if they were in control of a simulated drone swarm. We assessed that hands are the most commonly…
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