Cognitive architectures for autonomous robots: Towards human-level autonomy and beyond.

Abstract

Achieving human-level autonomy in robots is a complex and multifaceted challenge that requires the development of advanced cognitive architectures. This paper proposes a comprehensive cognitive architecture designed to integrate perception, memory, and decision-making processes, thereby enhancing the adaptability and intelligence of autonomous robots. The proposed architecture is critically examined in terms of its ability to address limitations in existing models, particularly in industrial and social applications. Through a detailed analysis, the paper explores the innovative features of this architecture, such as multimodal perception and continuous learning. It discusses its scalability and flexibility across various domains. The paper also takes a look at the ethical and societal implications of achieving human-level autonomy, emphasizing the need for robust safety protocols and thoughtful integration into human environments. Finally, the paper outlines the ongoing challenges in the field and suggests future research directions to advance the development of autonomous systems further.