  Emerging object-based SLAM algorithms can build a graph representation of an
environment comprising nodes for robot poses and object landmarks. However,
while this map will contain static objects such as furniture or appliances,
many moveable objects (e.g. the car keys, the glasses, or a magazine), are not
suitable as landmarks and will not be part of the map due to their non-static
nature. We show that Graph Convolutional Networks can learn navigation policies
to find such unmapped objects by learning to exploit the hidden probabilistic
model that governs where these objects appear in the environment. The learned
policies can generalise to object classes unseen during training by using word
vectors that express semantic similarity as representations for object nodes in
the graph. Furthermore, we show that the policies generalise to unseen
environments with only minimal loss of performance. We demonstrate that
pre-training the policy network with a proxy task can significantly speed up
learning, improving sample efficiency.
