Interactive Multi-Robot Flocking with Gesture Responsiveness and Musical Accompaniment
<|reference_start|>Interactive Multi-Robot Flocking with Gesture Responsiveness and Musical Accompaniment: For decades, robotics researchers have pursued various tasks for multi-robot systems, from cooperative manipulation to search and rescue. These tasks are multi-robot extensions of classical robotic tasks and often optimized on dimensions such as speed or efficiency. As robots transition from commercial and research settings into everyday environments, social task aims such as engagement or entertainment become increasingly relevant. This work presents a compelling multi-robot task, in which the main aim is to enthrall and interest. In this task, the goal is for a human to be drawn to move alongside and participate in a dynamic, expressive robot flock. Towards this aim, the research team created algorithms for robot movements and engaging interaction modes such as gestures and sound. The contributions are as follows: (1) a novel group navigation algorithm involving human and robot agents, (2) a gesture responsive algorithm for real-time, human-robot flocking interaction, (3) a weight mode characterization system for modifying flocking behavior, and (4) a method of encoding a choreographer's preferences inside a dynamic, adaptive, learned system. An experiment was performed to understand individual human behavior while interacting with the flock under three conditions: weight modes selected by a human choreographer, a learned model, or subset list. Results from the experiment showed that the perception of the experience was not influenced by the weight mode selection. This work elucidates how differing task aims such as engagement manifest in multi-robot system design and execution, and broadens the domain of multi-robot tasks.<|reference_end|>
@article{cuan2024interactive,
    title={Interactive Multi-Robot Flocking with Gesture Responsiveness and Musical
  Accompaniment},
    author={Catie Cuan, Kyle Jeffrey, Kim Kleiven, Adrian Li, Emre Fisher, Matt
  Harrison, Benjie Holson, Allison Okamura, and Matt Bennice},
    journal={arXiv preprint arXiv:2404.00442},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.00442},
    primaryClass={cs.RO cs.AI cs.HC}
}