  Effective human-robot interaction, such as in robot learning from human
demonstration, requires the learning agent to be able to ground abstract
concepts (such as those contained within instructions) in a corresponding
high-dimensional sensory input stream from the world. Models such as deep
neural networks, with high capacity through their large parameter spaces, can
be used to compress the high-dimensional sensory data to lower dimensional
representations. These low-dimensional representations facilitate symbol
grounding, but may not guarantee that the representation would be
human-interpretable. We propose a method which utilises the grouping of
user-defined symbols and their corresponding sensory observations in order to
align the learnt compressed latent representation with the semantic notions
contained in the abstract labels. We demonstrate this through experiments with
both simulated and real-world object data, showing that such alignment can be
achieved in a process of physical symbol grounding.
