We present an end-to-end procedure for embodied exploration based on two
biologically inspired computations: predictive coding and uncertainty
minimization. The procedure can be applied to any exploration setting in a
task-independent and intrinsically driven manner. We first demonstrate our
approach in a maze navigation task and show that our model is capable of
discovering the underlying transition distribution and reconstructing the
spatial features of the environment. Second, we apply our model to the more
complex task of active vision, where an agent must actively sample its visual
environment to gather information. We show that our model is able to build
unsupervised representations that allow it to actively sample and efficiently
categorize sensory scenes. We further show that using these representations as
input for downstream classification leads to superior data efficiency and
learning speed compared to other baselines, while also maintaining lower
parameter complexity. Finally, the modularity of our model allows us to analyze
its internal mechanisms and to draw insight into the interactions between
perception and action during exploratory behavior.

运用预测编码和不确定性最小化两种生物启发式计算方式，提出了一种端到端的嵌入式探索过程，可用于解决任务无关的探索问题。在迷宫导航和积极视觉任务中，我们的模型均能够通过建立无监督表示，对感知场景进行主动采样、高效分类，学习效率更高、数据效率更高。此外，模型的模块化设计允许分析其内部机制以及在探索行为中，感知与行动之间的相互作用。