  Creating autonomous robots that can actively explore the environment, acquire
knowledge and learn skills continuously is the ultimate achievement envisioned
in cognitive and developmental robotics. Their learning processes should be
based on interactions with their physical and social world in the manner of
human learning and cognitive development. Based on this context, in this paper,
we focus on the two concepts of world models and predictive coding. Recently,
world models have attracted renewed attention as a topic of considerable
interest in artificial intelligence. Cognitive systems learn world models to
better predict future sensory observations and optimize their policies, i.e.,
controllers. Alternatively, in neuroscience, predictive coding proposes that
the brain continuously predicts its inputs and adapts to model its own dynamics
and control behavior in its environment. Both ideas may be considered as
underpinning the cognitive development of robots and humans capable of
continual or lifelong learning. Although many studies have been conducted on
predictive coding in cognitive robotics and neurorobotics, the relationship
between world model-based approaches in AI and predictive coding in robotics
has rarely been discussed. Therefore, in this paper, we clarify the
definitions, relationships, and status of current research on these topics, as
well as missing pieces of world models and predictive coding in conjunction
with crucially related concepts such as the free-energy principle and active
inference in the context of cognitive and developmental robotics. Furthermore,
we outline the frontiers and challenges involved in world models and predictive
coding toward the further integration of AI and robotics, as well as the
creation of robots with real cognitive and developmental capabilities in the
future.
