Active inference is a mathematical framework which originated in computational neuroscience as a theory of how the brain implements action, perception and learning. Recently, it has been shown to be a promising approach to the problems of state-estimation and control under uncertainty, as well as a foundation for the construction of goal-driven behaviours in robotics and artificial agents in general. Here, we review the state-of-the-art theory and implementations of active inference for state-estimation, control, planning and learning; describing current achievements with a particular focus on robotics. We showcase relevant experiments that illustrate its potential in terms of adaptation, generalization and robustness. Furthermore, we connect this approach with other frameworks and discuss its expected benefits and challenges: a unified framework with functional biological plausibility using variational Bayesian inference.

翻译：主动推论是一个数学框架,它起源于计算神经科学,是大脑如何采取行动、认识和学习的理论。最近,它被证明是解决不确定性下国家估计和控制问题的有希望的方法,也是在机器人和一般人造物剂中构建目标驱动行为的基础。在这里,我们审查最先进的理论以及国家估计、控制、规划和学习积极推论的实施;描述当前的成就,特别侧重于机器人。我们展示了相关实验,从适应、概括和稳健的角度来说明其潜力。此外,我们将这一方法与其他框架联系起来,并讨论其预期的好处和挑战:利用变异的巴耶斯推理,建立一个具有实用生物概率的统一框架。