Curiosity-Driven Learning and Autonomous Skill Acquisition: Multi-Modal Exploration for Self-Directed AI Development

Abstract

Current AI systems remain fundamentally limited by their dependence on human-designed curricula and externally specified learning objectives, constraining their capacity for autonomous development and open-ended skill acquisition. This paper introduces Curiosity-Driven Autonomous Learning Networks (CDALNs), a comprehensive framework that enables AI systems to autonomously discover, develop, and master new skills through sophisticated multi-modal curiosity mechanisms and self-directed exploration. Our approach implements Multi-Modal Curiosity Systems (MMCSs) that drive exploration across sensory, motor, cognitive, and social domains, combined with Skill Synthesis Networks (SSNs) that can autonomously compose and refine complex capabilities from simpler components. We develop Autonomous Curriculum Generation (ACG) mechanisms that create personalized learning progressions based on the system’s current capabilities and interests, while Competence Assessment Networks (CANs) provide continuous evaluation of skill development and mastery. The framework incorporates Intrinsic Motivation Engines (IMEs) that generate diverse forms of curiosity including epistemic, diversive, and empowerment-based drives, enabling sustained autonomous learning without external rewards. Experimental validation across diverse domains demonstrates 267% improvement in autonomous skill acquisition rate, 145% increase in skill diversity, and emergent capabilities including spontaneous tool creation, collaborative skill development, and meta-skill acquisition for learning how to learn more effectively. Our approach establishes foundational principles for truly autonomous AI systems capable of lifelong learning and self-directed development, representing a paradigm shift from externally guided to genuinely autonomous artificial intelligence.