Empirical assessment and comparison of neuro-evolutionary methods for the automatic off-line design of robot swarms.

ABSTRACT: Neuro-evolution is an appealing approach to generating collective behaviors for robot swarms. In its typical application, known as off-line automatic design, the neural networks controlling the robots are optimized in simulation. It is understood that the so-called reality gap, the unavoidable differences between simulation and reality, typically causes neural network to be less effective on real robots than what is predicted by simulation. In this paper, we present an empirical study on the extent to which the reality gap impacts the most popular and advanced neuro-evolutionary methods for the off-line design of robot swarms. The results show that the neural networks produced by the methods under analysis performed well in simulation, but not in real-robot experiments. Further, the rankin

SUBMITTER: Hasselmann K 

PROVIDER: S-EPMC8285396 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature