Exploring Behavior Discovery Methods for Heterogeneous Swarms of
  Limited-Capability Robots
Connor Mattson Jeremy C. Clark Daniel S. Brown
We study the problem of determining the emergent behaviors that are possible
given a functionally heterogeneous swarm of robots with limited capabilities.
Prior work has considered behavior search for homogeneous swarms and proposed
the use of novelty search over either a hand-specified or learned behavior
space followed by clustering to return a taxonomy of emergent behaviors to the
user. In this paper, we seek to better understand the role of novelty search
and the efficacy of using clustering to discover novel emergent behaviors.
Through a large set of experiments and ablations, we analyze the effect of
representations, evolutionary search, and various clustering methods in the
search for novel behaviors in a heterogeneous swarm. Our results indicate that
prior methods fail to discover many interesting behaviors and that an iterative
human-in-the-loop discovery process discovers more behaviors than random
search, swarm chemistry, and automated behavior discovery. The combined
discoveries of our experiments uncover 23 emergent behaviors, 18 of which are
novel discoveries. To the best of our knowledge, these are the first known
emergent behaviors for heterogeneous swarms of computation-free agents. Videos,
code, and appendix are available at the project website:
https://sites.google.com/view/heterogeneous-bd-methods