Curiosity-Driven Development of Action and Language in Robots Through Self-Exploration

Human infants acquire language and action gradually through development, achieving strong generalization from minimal experience, whereas large language models require exposure to billions of training tokens. What mechanisms underlie such efficient developmental learning in humans? This study investigates this question through robot simulation experiments in which agents learn to perform actions associated with imperative sentences (e.g., \textit{push red cube}) via curiosity-driven self-exploration. Our approach integrates the active inference framework with reinforcement learning, enabling intrinsically motivated developmental learning. The simulations reveal several key findings: i) Generalization improves markedly as the scale of compositional elements increases. ii) Curiosity combined with motor noise yields substantially better learning than exploration without curiosity. iii) Rote pairing of sentences and actions precedes the emergence of compositional generalization. iv) Simpler, prerequisite-like actions develop earlier than more complex actions that depend on them. v) When exception-handling rules were introduced -- where certain imperative sentences required executing inconsistent actions -- the robots successfully acquired these exceptions through exploration and displayed a U-shaped performance curve characteristic of representational redescription in child language learning. Together, these results suggest that curiosity-driven exploration and active inference provide a powerful account of how intrinsic motivation and hierarchical sensorimotor learning can jointly support scalable compositional generalization and exception handling in both humans and artificial agents.