Cognitive Control Systems for Autonomous Robotic Manipulation Using Deep Imitation Learning and Sensor Fusion

Abstract

This paper explores the integration of deep imitation learning and sensor fusion in cognitive control systems for autonomous robotic manipulation. The convergence of these technologies allows robots to learn complex behaviors from human demonstrations while effectively perceiving and interacting with dynamic environments through multisensory data. By incorporating cognitive architectures and deep neural networks, we address key challenges in robotic autonomy, including perception, decision-making, and motor execution. This study highlights current advances, provides a comparative literature review, and proposes a modular system for manipulation tasks that emphasizes generalizability, accuracy, and adaptability.