MAP-Elites enables Powerful Stepping Stones and Diversity for Modular Robotics
J{\o}rgen Nordmoen, Frank Veenstra, Kai Olav Ellefsen, Kyrre Glette

TL;DR
This paper demonstrates that MAP-Elites enhances diversity and performance in modular robotics optimization, especially in complex environments, by promoting diverse morphological solutions and analyzing genealogical stepping stones.
Contribution
It introduces the application of MAP-Elites to modular robotics, highlighting its ability to generate diverse high-performing solutions and improve adaptation across environments.
Findings
MAP-Elites produces higher morphological diversity and performance.
Diversity of solutions aids in adapting to difficult environments.
Genealogical analysis reveals the importance of stepping stones for high performance.
Abstract
In modular robotics, modules can be reconfigured to change the morphology of the robot, making it able to adapt for specific tasks. However, optimizing both the body and control is a difficult challenge due to the intricate relationship between fine-tuning control and morphological changes that can invalidate such optimizations. To solve this challenge we compare three different Evolutionary Algorithms on their capacity to optimize morphologies in modular robotics. We compare two objective-based search algorithms, with MAP-Elites. To understand the benefit of diversity we transition the evolved populations into two difficult environments to see if diversity can have an impact on solving complex environments. In addition, we analyse the genealogical ancestry to shed light on the notion of stepping stones as key to enable high performance. The results show that MAP-Elites is capable of…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
