Bootstrapped Neuro-Simulation as a method of concurrent neuro-evolution and damage recovery

摘要

Bootstrapped Neuro-Simulation (BNS) is a method of concurrent simulator and robot controller evolution. The algorithm requires little domain knowledge and no pre-investigation data gathering. Additionally, it bridges the reality gap effectively, rapidly evolves functional controllers, and recovers from damage automatically. In this paper, the first evidence of the ability of BNS to evolve closed-loop controllers is shown; in this case to solve a light-following problem. The algorithm is then evaluated for its damage recovery ability for these closed-loop controllers and shown to be very effective, with only minor adaptations. (C) 2019 Elsevier B.V. All rights reserved.