MAP-Elites Enables Powerful Stepping Stones and Diversity for Modular Robotics.

ABSTRACT: In modular robotics modules can be reconfigured to change the morphology of the robot, making it able to adapt to specific tasks. However, optimizing both the body and control of such robots is a difficult challenge due to the intricate relationship between fine-tuning control and morphological changes that can invalidate such optimizations. These challenges can trap many optimization algorithms in local optima, halting progress towards better solutions. To solve this challenge we compare three different Evolutionary Algorithms on their capacity to optimize high performing and diverse morphologies and controllers in modular robotics. We compare two objective-based search algorithms, with and without a diversity promoting objective, with a Quality Diversity algorithm-MAP-Elites. The results

SUBMITTER: Nordmoen J 

PROVIDER: S-EPMC8115726 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature