Internal models (IMs) can represent relations between sensors and actuators in natural and artificial agents. In autonomous robots, the adaptation of IMs and the adaptation of the behavior are interdependent processes which have been studied under paradigms for self-organization of behavior such as homeokinesis. We compare the effect of various types of IMs on the generation of behavior in order to evaluate model quality across different behaviors. The considered IMs differ in the degree of flexibility and expressivity related to, respectively, learning speed and structural complexity of the model. We show that the different IMs generate different error characteristics which in turn lead to variations of the self-generated behavior of the robot. Due to the tradeoff between error minimization and complexity of the explored environment, we compare the models in the sense of Pareto optimality. Among the linear and nonlinear models that we analyze, echo-state networks achieve a particularly high performance which we explain as a result of the combination of fast learning and complex internal dynamics. More generally, we provide evidence that Pareto optimization is preferable in autonomous learning as it allows that a special solution can be negotiated in any particular environment.

中文翻译：

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自主学习中内部模型的评估

内部模型 (IM) 可以表示自然和人工代理中传感器和执行器之间的关系。在自主机器人中，IM 的适应和行为的适应是相互依赖的过程，已经在行为的自组织范式下进行了研究，例如顺势运动。我们比较了各种类型的 IM 对行为生成的影响，以评估不同行为的模型质量。所考虑的 IM 在灵活性和表达性方面有所不同，分别与模型的学习速度和结构复杂性相关。我们表明，不同的 IM 会产生不同的错误特征，进而导致机器人自生成行为的变化。由于错误最小化和探索环境的复杂性之间的权衡，我们在帕累托最优的意义上比较模型。在我们分析的线性和非线性模型中，回声状态网络实现了特别高的性能，我们将其解释为快速学习和复杂内部动态相结合的结果。更一般地说，我们提供的证据表明帕累托优化在自主学习中更可取，因为它允许在任何特定环境中协商一个特殊的解决方案。