We show that goal-directed action planning and generation in a teleological
framework can be formulated using the free energy principle. The proposed
model, which is built on a variational recurrent neural network model, is
characterized by three essential features. These are that (1) goals can be
specified for both static sensory states, e.g., for goal images to be reached
and dynamic processes, e.g., for moving around an object, (2) the model can not
only generate goal-directed action plans, but can also understand goals by
sensory observation, and (3) the model generates future action plans for given
goals based on the best estimate of the current state, inferred using past
sensory observations. The proposed model is evaluated by conducting experiments
on a simulated mobile agent as well as on a real humanoid robot performing
object manipulation.

本研究基于自由能原理，使用变分递归神经网络模型，提出了一种目标导向的行动规划和生成模型，具有静态感知状态和动态运动过程目标的指定和理解，以及根据过去的感知观察推断当前状态的最优估计，为特定目标生成未来行动计划，并在移动代理上进行了实验验证。