  We propose to learn tasks directly from visual demonstrations by learning to
predict the outcome of human and robot actions on an environment. We enable a
robot to physically perform a human demonstrated task without knowledge of the
thought processes or actions of the human, only their visually observable state
transitions. We evaluate our approach on two table-top, object manipulation
tasks and demonstrate generalisation to previously unseen states. Our approach
reduces the priors required to implement a robot task learning system compared
with the existing approaches of Learning from Demonstration, Reinforcement
Learning and Inverse Reinforcement Learning.
