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

Human infants acquire language and action co-developmentally, achieving remarkable generalization capabilities from only a minimal number of learning examples. In contrast, recent large language models require exposure to billions of training tokens to achieve such generalization. What mechanisms underlie such efficient developmental learning in humans? This study addresses this question through simulation experiments in which robots learn to perform various actions corresponding to imperative sentences (e.g., \textit{push red cube}) via trials of self-guided exploration. Our approach integrates the active inference framework with reinforcement learning, enabling curiosity-driven developmental learning. The simulations yielded several nontrivial findings: i) Curiosity-driven exploration combined with motor noise substantially outperforms learning without curiosity. ii) Simpler, prerequisite-like actions emerge earlier in development, while more complex actions involving these prerequisites develop later. iii) Rote pairing of sentences and actions occurs before the emergence of compositional generalization. iv) Generalization is drastically improved as the number of compositional elements increases. These results shed light into possible mechanisms underlying efficient co-developmental learning in infants and provide computational parallels to findings in developmental psychology.