能力限定型異種ロボット群における創発行動の発見手法の検討
Exploring Behavior Discovery Methods for Heterogeneous Swarms of Limited-Capability Robots
限られた能力を持つ異種ロボット群で可能な創発行動を探索するため、新規性探索やクラスタリングなどの手法を大規模実験で比較し、人間参加型の反復的発見プロセスがより多くの新規行動を発見できることを示した。
著者: Connor Mattson, Jeremy C. Clark, Daniel S. Brown
分類: cs.RO, cs.LG, cs.MA
原文アブストラクト
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
