An embodied agent constantly influences its environment and is influenced by it. We use the sensorimotor loop to model these interactions and thereby we can quantify different information flows in the system by various information theoretic measures. This includes a measure for the interaction among the agent's body and its environment, called Morphological Computation. Additionally, we examine the controller complexity by two measures, one of which can be seen in the context of the Integrated Information Theory of consciousness. Applying this framework to an experimental setting with simulated agents allows us to analyze the interaction between an agent and its environment, as well as the complexity of its controller, the brain of the agent. Previous research reveals an antagonistic relationship between the controller complexity and Morphological Computation. A morphology adapted well to a task can reduce the necessary complexity of the controller significantly. This creates the problem that embodied intelligence is correlated with a reduced necessity of a controller, a brain. However, in order to interact well with their surroundings, the agents first have to understand the relevant dynamics of the environment. By analyzing learning agents we observe that an increased controller complexity can facilitate a better interaction between an agent's body and its environment. Hence, learning requires an increased controller complexity and the controller complexity and Morphological Computation influence each other.

翻译：一个具身智能体持续影响其环境，并受到环境的影响。我们利用感知运动环路对这些交互进行建模，从而可以通过各种信息论度量来量化系统中的不同信息流。其中包括一种度量智能体身体与环境之间相互作用的指标，称为形态计算。此外，我们通过两种度量来考察控制器的复杂性，其中一种可置于意识整合信息理论的背景下进行理解。将该框架应用于模拟智能体的实验设置，使我们能够分析智能体与环境之间的交互，以及其控制器（即智能体的大脑）的复杂性。先前的研究揭示了控制器复杂性与形态计算之间的拮抗关系。适应任务良好的形态可以显著降低控制器所需的复杂性。这导致了一个问题：具身智能与控制器（大脑）的必要性降低相关联。然而，为了与环境良好交互，智能体首先需要理解环境的相关动态。通过分析学习中的智能体，我们观察到提高控制器复杂性可以促进智能体身体与环境之间更好的交互。因此，学习需要提高控制器复杂性，并且控制器复杂性与形态计算相互影响。