  While humans are aware of their body and capabilities, robots are not. To
address this, we present in this paper a neural network architecture that
enables a dual-arm robot to get a sense of itself in an environment. Our
approach is inspired by human self-awareness developmental levels and serves as
the underlying building block for a robot to achieve awareness of itself while
carrying out tasks in an environment. We assume that a robot has to know itself
before interacting with the environment in order to be able to support
different robotic tasks. Hence, we implemented a neural network architecture to
enable a robot to differentiate its limbs from the environment using visual and
proprioception sensory inputs. We demonstrate experimentally that a robot can
distinguish itself with an accuracy of 88.7% on average in cluttered
environmental settings and under confounding input signals.
