Predictive processing has become an influential framework in cognitive sciences. This framework turns the traditional view of perception upside down, claiming that the main flow of information processing is realized in a top-down hierarchical manner. Furthermore, it aims at unifying perception, cognition, and action as a single inferential process. However, in the related literature, the predictive processing framework and its associated schemes such as predictive coding, active inference, perceptual inference, free-energy principle, tend to be used interchangeably. In the field of cognitive robotics there is no clear-cut distinction on which schemes have been implemented and under which assumptions. In this paper, working definitions are set with the main aim of analyzing the state of the art in cognitive robotics research working under the predictive processing framework as well as some related non-robotic models. The analysis suggests that, first, both research in cognitive robotics implementations and non-robotic models needs to be extended to the study of how multiple exteroceptive modalities can be integrated into prediction error minimization schemes. Second, a relevant distinction found here is that cognitive robotics implementations tend to emphasize the learning of a generative model, while in non-robotics models it is almost absent. Third, despite the relevance for active inference, few cognitive robotics implementations examine the issues around control and whether it should result from the substitution of inverse models with proprioceptive predictions. Finally, limited attention has been placed on precision weighting and the tracking of prediction error dynamics. These mechanisms should help to explore more complex behaviors and tasks in cognitive robotics research under the predictive processing framework.
翻译:在认知科学中,预测处理框架及其相关计划,如预测编码、主动推断、感知推断、自由能源原则,往往可以互换使用。在认知机器人领域,没有明确区分哪些计划已经实施,哪些假设已经实施。在本文中,工作定义的主要目的是分析在预测处理框架下进行的认知机器人研究中的艺术状态,以及一些相关的非机器人模型。在相关文献中,预测处理框架及其相关计划,如预测编码、主动推断、感知推断、自由能源原则,往往会被互换使用。首先,在认知机器人领域,没有明确区分哪些计划已经实施,哪些计划是假设之下。在本文中,工作定义的主要目的是分析在预测处理框架内进行的认知机器人研究的艺术状况以及一些相关的非机器人模型。首先,认知机器人实施和非机器人模式的研究应该扩展到如何将多重感知模式纳入预测错误最小化计划。第二,这里发现的一个相关区别是,认知性机器人应用中的一些模型的精确度是,尽管没有进行精确性研究,但最终研究研究的结果是,这些模型的正确性研究结果是,这些模型中的大多数是没有进行精确的。