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Predictive Processing in Cognitive Robotics: A Review.

Alejandra Ciria¹, Guido Schillaci², Giovanni Pezzulo³

¹Facultad de Psicología, Universidad Nacional Autónoma de México, Mexico City, CP 04510, Mexico ciriacontacto@gmail.com.

Neural Computation

|September 8, 2021

Summary

This summary is machine-generated.

Predictive processing unifies perception, cognition, and action via top-down information flow. Cognitive robotics research needs to integrate multimodal sensory data and focus on control mechanisms within this framework.

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Area of Science:

Cognitive Science
Robotics
Computational Neuroscience

Background:

Predictive processing is a key cognitive science framework, proposing hierarchical, top-down information flow.
It unifies perception, cognition, and action as a single inferential process.
Related schemes like predictive coding and active inference are often used interchangeably, lacking clear distinctions in cognitive robotics.

Purpose of the Study:

To define working terms for predictive processing schemes.
To analyze the current state of cognitive robotics research using predictive processing.
To compare robotic implementations with non-robotic models.

Main Methods:

Literature review and analysis of predictive processing implementations in cognitive robotics and related non-robotic models.
Setting working definitions for predictive processing, predictive coding, active inference, and the free-energy principle.
Comparative analysis of research trends and identified gaps.

Main Results:

Cognitive robotics and non-robotic models require further research on integrating multiple exteroceptive modalities into prediction error minimization.
Cognitive robotics emphasizes generative model learning, unlike non-robotic models where it's often absent.
Few cognitive robotics implementations explore control mechanisms derived from proprioceptive predictions, and limited attention is given to precision weighting and prediction error dynamics.

Conclusions:

Future cognitive robotics research should focus on multimodal integration and control mechanisms within the predictive processing framework.
Developing generative models and exploring precision weighting are crucial for advancing complex behaviors in predictive robotics.
Clarifying distinctions between predictive processing schemes is essential for consistent application in cognitive robotics.