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.

中文翻译：

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复杂机器人的自举神经仿真

摘要 机器人模拟器通常用于加速进化机器人 (ER) 过程。大多数仿真方法都基于物理建模。然而，基于物理的模拟器的开发可能变得复杂，并且需要机器人系统的先验知识。机器人模拟器可以使用机器学习技术构建，例如人工神经网络 (ANN)。基于 ANN 的模拟器开发通常需要一个漫长的行为数据收集期，然后才能训练模拟器并用于在 ER 过程中评估控制器。Bootstrapped Neuro-Simulation (BNS) 方法可用于同时收集行为数据、训练基于 ANN 的模拟器并针对特定机器人问题开发控制器。