Predictive Processing in Cognitive Robotics: a Review
Alejandra Ciria, Guido Schillaci, Giovanni Pezzulo, Verena V. Hafner,, Bruno Lara

TL;DR
This review analyzes the application of predictive processing frameworks in cognitive robotics, highlighting gaps in integration, learning models, control mechanisms, and dynamic prediction error tracking for advancing robotic cognition.
Contribution
It clarifies the distinctions among related schemes and identifies key research gaps in implementing predictive processing in cognitive robotics.
Findings
Robotics research needs to better integrate multiple sensory modalities.
Robotics models focus more on generative model learning than non-robotic models.
Limited exploration of control mechanisms and prediction error dynamics in robotics.
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, 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…
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