Bidirectional Interaction between Visual and Motor Generative Models using Predictive Coding and Active Inference
This work addresses robotic control challenges by enabling bidirectional interaction between sensory and motor modules, though it appears incremental as it builds upon existing AIF and PC frameworks.
The authors tackled the problem of integrating visual and motor generative models for robotic control by proposing a neural architecture based on Active Inference and Predictive Coding, and demonstrated its effectiveness on a simulated robotic arm learning to reproduce handwritten letters.
In this work, we build upon the Active Inference (AIF) and Predictive Coding (PC) frameworks to propose a neural architecture comprising a generative model for sensory prediction, and a distinct generative model for motor trajectories. We highlight how sequences of sensory predictions can act as rails guiding learning, control and online adaptation of motor trajectories. We furthermore inquire the effects of bidirectional interactions between the motor and the visual modules. The architecture is tested on the control of a simulated robotic arm learning to reproduce handwritten letters.