Bootstrapped Neuro-Simulation for complex robots

Abstract Robotic simulators are often used to speed up the Evolutionary Robotics (ER) process. Most simulation approaches are based on physics modelling. However, physics-based simulators can become complex to develop and require prior knowledge of the robotic system. Robotics simulators can be constructed using Machine Learning techniques, such as Artificial Neural Networks (ANNs). ANN-based simulator development usually requires a lengthy behavioural data collection period before the simulator can be trained and used to evaluate controllers during the ER process. The Bootstrapped Neuro-Simulation (BNS) approach can be used to simultaneously collect behavioural data, train an ANN-based simulator and evolve controllers for a particular robotic problem. This paper investigates proposed improvements to the BNS approach and demonstrates the viability of the approach by optimising gait controllers for a Hexapod and Snake robot platform.