# Predictive Processing in Cognitive Robotics: a Review

arXiv: https://arxiv.org/abs/2101.06611 · HTML: https://mlsanity.com/paper/2101.06611/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jan 2021.

## Built on by (most cited first, 13 of 13)

- [Active Inference in Robotics and Artificial Agents: Survey and Challenges](https://mlsanity.com/paper/2112.01871/) (2021)
- [World Models and Predictive Coding for Cognitive and Developmental Robotics: Frontiers and Challenges](https://mlsanity.com/paper/2301.05832/) (2023)
- [Emergent Communication through Metropolis-Hastings Naming Game with Deep Generative Models](https://mlsanity.com/paper/2205.12392/) (2022)
- [Double Articulation Analyzer with Prosody for Unsupervised Word and Phoneme Discovery](https://mlsanity.com/paper/2103.08199/) (2021)
- [Adaptation through prediction: multisensory active inference torque control](https://mlsanity.com/paper/2112.06752/) (2021)
- [Neuroscience-inspired perception-action in robotics: applying active inference for state estimation, control and self-perception](https://mlsanity.com/paper/2105.04261/) (2021)
- [A Whole Brain Probabilistic Generative Model: Toward Realizing Cognitive Architectures for Developmental Robots](https://mlsanity.com/paper/2103.08183/) (2021)
- [Utility Theory based Cognitive Modeling in the Application of Robotics: A Survey](https://mlsanity.com/paper/2306.09445/) (2023)
- [INSIGHT: INference-time Sequence Introspection for Generating Help Triggers in Vision-Language-Action Models](https://mlsanity.com/paper/2510.01389/) (2025)
- [Bidirectional Interaction between Visual and Motor Generative Models using Predictive Coding and Active Inference](https://mlsanity.com/paper/2104.09163/) (2021)
- [Symbol Emergence as Inter-personal Categorization with Head-to-head Latent Word](https://mlsanity.com/paper/2205.15027/) (2022)
- [Learning Low-Level Causal Relations using a Simulated Robotic Arm](https://mlsanity.com/paper/2410.07751/) (2024)
- [Efficient Deep Reinforcement Learning with Predictive Processing Proximal Policy Optimization](https://mlsanity.com/paper/2211.06236/) (2022)

## Abstract (arXiv)

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.