  Human-robot interaction often occurs in the form of instructions given from a
human to a robot. For a robot to successfully follow instructions, a common
representation of the world and objects in it should be shared between humans
and the robot so that the instructions can be grounded. Achieving this
representation can be done via learning, where both the world representation
and the language grounding are learned simultaneously. However, in robotics
this can be a difficult task due to the cost and scarcity of data. In this
paper, we tackle the problem by separately learning the world representation of
the robot and the language grounding. While this approach can address the
challenges in getting sufficient data, it may give rise to inconsistencies
between both learned components. Therefore, we further propose Bayesian
learning to resolve such inconsistencies between the natural language grounding
and a robot's world representation by exploiting spatio-relational information
that is implicitly present in instructions given by a human. Moreover, we
demonstrate the feasibility of our approach on a scenario involving a robotic
arm in the physical world.
