Cognitive behavior emerging in mechanical systems through reservoir encoding and active inference

This paper introduces a computational framework enabling mechanical systems to develop cognitive behaviors through reservoir encoding and active inference. Traditional mechanical systems treat sensing, analysis, and control as decoupled processes relying on predefined mechanistic models and supervised learning with offline optimization. The proposed approach draws inspiration from biological cognition, allowing mechanical systems to learn, adapt, and exhibit emergent cognitive capabilities in changing environments.