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.

翻译：我们提出了一种基于两种生物启发计算机制——预测编码与不确定性最小化——的具身探索端到端框架。该框架能以任务无关且内在驱动的方式应用于任意探索场景。我们首先在迷宫导航任务中验证该方法，证明模型能够发现潜在的状态转移分布并重构环境空间特征。其次，我们将模型应用于更复杂的主动视觉任务，其中智能体需主动采样视觉环境以收集信息。研究表明，该模型能构建无监督表征，使智能体能够主动采样并对感知场景进行高效分类。我们还发现，将此类表征作为下游分类任务的输入时，相比其他基线方法，在保持更低参数复杂度的同时，实现了更优的数据效率与学习速度。最后，模型的模块化特性使我们能够分析其内部机制，并深入理解探索行为中感知与行动之间的交互作用。