Interactive Multi-Robot Flocking with Gesture Responsiveness and Musical Accompaniment
CoRR(2024)
摘要
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.
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