  A smart city can be seen as a framework, comprised of Information and
Communication Technologies (ICT). An intelligent network of connected devices
that collect data with their sensors and transmit them using cloud technologies
in order to communicate with other assets in the ecosystem plays a pivotal role
in this framework. Maximizing the quality of life of citizens, making better
use of resources, cutting costs, and improving sustainability are the ultimate
goals that a smart city is after. Hence, data collected from connected devices
will continuously get thoroughly analyzed to gain better insights into the
services that are being offered across the city; with this goal in mind that
they can be used to make the whole system more efficient. Robots and physical
machines are inseparable parts of a smart city. Embodied AI is the field of
study that takes a deeper look into these and explores how they can fit into
real-world environments. It focuses on learning through interaction with the
surrounding environment, as opposed to Internet AI which tries to learn from
static datasets. Embodied AI aims to train an agent that can See (Computer
Vision), Talk (NLP), Navigate and Interact with its environment (Reinforcement
Learning), and Reason (General Intelligence), all at the same time. Autonomous
driving cars and personal companions are some of the examples that benefit from
Embodied AI nowadays. In this paper, we attempt to do a concise review of this
field. We will go through its definitions, its characteristics, and its current
achievements along with different algorithms, approaches, and solutions that
are being used in different components of it (e.g. Vision, NLP, RL). We will
then explore all the available simulators and 3D interactable databases that
will make the research in this area feasible. Finally, we will address its
challenges and identify its potentials for future research.
