No abstract

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, and 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 letter, 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 nonrobotic models. The analysis suggests that, first, research in both cognitive robotics implementations and nonrobotic 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 nonrobotics 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.

Embodied Cognitive Robotics focuses its attention on the design of artificial agents capable of performing cognitive tasks autonomously. A central issue in this consists in studying process by which agents learn through interaction with their environment. Embodied Cognitive Robotics aims to implement models of cognitive processes coming from Cognitive Sciences. The guidelines in this research area are a direct response to the shortcomings of Classical Artificial Intelligence, where high-level tasks and behaviors were studied. This article describes the work carried out in the Cognitive Robotics Laboratory at the Universidad Autónoma del Estado de Morelos (UAEM). Our work is based on the concept of low-level sensorimotor schemes coded by Internal Models, thus falling as a matter of course within the tenets of Embodied Cognition, particularly with the idea that cognition must be understood as occurring in agents that have a body with which they interact in a specific environment. It is through this interaction that learning emerges laying the ground for cognitive processes. Our research includes theoretical work laying the foundations of Embodied Cognitive Robotics, as well as work with artificial and with natural agents.

How do cognitive agents decide which is the relevant information to learn and how goals are selected to gain this knowledge? Cognitive agents need to be motivated to perform any action. We discuss that emotions arise when differences between expected and actual rates of progress toward a goal are experienced. Therefore, the tracking of prediction error dynamics has a tight relationship with emotions. Here, we suggest that the tracking of prediction error dynamics allows an artificial agent to be intrinsically motivated to seek new experiences but constrained to those that generate reducible prediction error. We present an intrinsic motivation architecture that generates behaviors towards self-generated and dynamic goals and that regulates goal selection and the balance between exploitation and exploration through multi-level monitoring of prediction error dynamics. This new architecture modulates exploration noise and leverages computational resources according to the dynamics of the overall performance of the learning system. Additionally, it establishes a possible solution to the temporal dynamics of goal selection. The results of the experiments presented here suggest that this architecture outperforms intrinsic motivation approaches where exploratory noise and goals are fixed and a greedy strategy is applied.