论文标题
通过主动推断，目标定向的计划和目标理解：通过模拟和物理机器人实验进行评估
Goal-directed Planning and Goal Understanding by Active Inference: Evaluation Through Simulated and Physical Robot Experiments
论文作者
论文摘要
我们表明，可以使用自由能原理制定目标框架中目标的行动计划和生成。所提出的模型是建立在各种复发性神经网络模型上的，其特征是三个基本特征。这些是（1）可以为静态感觉状态指定目标，例如，要达到目标图像和动态过程，例如，对于围绕对象而移动，（2）模型不仅可以通过感官观察来生成目标定向的行动计划，而且可以通过感官观察来理解目标，并且（3）模型基于最佳估算的最佳估算，该模型生成了未来的动作计划。通过对模拟移动剂以及执行对象操纵的真实人形机器人进行实验来评估所提出的模型。
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.
