Embodied Cognition Simulation with Simulated Sensorimotor Loops

Abstract

This paper explores the potential of simulating embodied cognition through the construction of simulated sensorimotor loops. The core claim is that creating more realistic artificial intelligence agents requires modeling the embodied experience of the agent, specifically by replicating the feedback mechanisms inherent in human sensorimotor processes. The proposed mechanism involves an agent possessing internal representations of its body, receiving simulated sensory input, and executing motor actions, all within a closed-loop system. This approach directly models the physical interaction and feedback processes that are considered fundamental to human cognition. The simulation aims to move beyond traditional AI approaches that often operate in abstraction, by grounding intelligence in a physically plausible representation of the world. The paper will detail the framework for building these simulations, focusing on the mathematical formulation of the sensorimotor loop dynamics and the representation of internal states. We will investigate how varying parameters within the loop – such as sensory noise, motor control precision, and the delay between action and feedback – impact the agent's behavior and its ability to learn and adapt. Ultimately, this research contributes to a deeper understanding of how embodiment shapes cognition and provides a new paradigm for AI development.