How do humans learn to master an ever-expanding repertoire of skills without being told what to learn? The Intrinsically Motivated Open-ended Learning (IMOL) workshop tackles this fundamental question at the intersection of artificial intelligence and cognitive science. While recent breakthroughs in curiosity-driven learning have shown promise, artificial agents still lack the remarkable flexibility of human learners who continuously generate their own goals, explore efficiently, and build upon previous knowledge. By bringing together perspectives from reinforcement learning, developmental psychology, and robotics, IMOL aims to uncover the key mechanisms – from autonomous goal generation to incremental skill building – that enable truly open-ended learning in both natural and artificial systems.