Active Inference and Functional Parametrisation: Differential Flatness and Smooth Random Realisation.

Area of Science:

• Robotics and Control Systems
• Computational Neuroscience
• Machine Learning

Background:

• Active inference models decision-making using generative models.
• Constructive nonlinear control theory offers systematic design methods.
• Bridging these fields is crucial for advanced autonomous systems.

Purpose of the Study:

• To explore the connection between differential flatness and active inference generative models.
• To investigate how pathwise properties of differentially flat systems inform control design.
• To apply these concepts to oculomotor control as a case study.

Main Methods:

• Formulating continuous-time generative models using generalized coordinates.
• Analyzing pathwise properties derived from temporal derivatives of differentially flat systems.
• Integrating constructive nonlinear control techniques within the active inference framework.

Main Results:

• Demonstrated a novel framework marrying control theory and active inference.
• Highlighted the utility of differential flatness in designing generative models for control.
• Provided a conceptual illustration using oculomotor control dynamics.

Conclusions:

• Differential flatness offers a promising pathway for designing robust active inference controllers.
• The integration facilitates the development of more sophisticated autonomous systems.
• Further research can extend this framework to more complex control problems.