Intuitive physics learning in a deep-learning model inspired by developmental psychology

Current AI systems lack the 'common sense' understanding of intuitive physics that even young children possess. This paper addresses this gap by drawing inspiration from developmental psychology. A new machine-learning dataset, the Physical Concepts dataset, is introduced to evaluate conceptual understanding of intuitive physics using the violation-of-expectation (VoE) paradigm. A deep-learning system, PLATO, is built to learn intuitive physics directly from visual data, inspired by studies of visual cognition in children. PLATO learns a diverse set of physical concepts, critically depending on object-level representations, mirroring findings from developmental psychology. The implications for both AI and human cognition research are discussed.

Luis S. Piloto, Ari Weinstein, Peter Battaglia, Matthew Botvinick