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

A central question in both cognitive science and artificial intelligence is how humans and artificial systems can acquire competencies for language and motor command in a co-developmental manner, despite having access to only limited learning experiences. This question is exemplified in human infants, who achieve remarkable generalization with sparse input. This is a stark contrast to large-scale models which rely on massive training corpora, to reach similar capabilities. This raises the issue of what mechanisms enable such efficient developmental learning. From the perspective of developmental psychology, infants acquire language through rich interaction with their embodied environments. T omasello's "verb-island" hypothesis argues that children initially learn verbs in specific, isolated contexts before generalizing across broader linguistic structures (1). He also emphasized the importance of embodiment in language acquisition, suggesting that grounding linguistic symbols in sensorimotor experiences is fundamental to language learning (2).