  We investigate policy transfer using image-to-semantics translation to
mitigate learning difficulties in vision-based robotics control agents. This
problem assumes two environments: a simulator environment with semantics, that
is, low-dimensional and essential information, as the state space, and a
real-world environment with images as the state space. By learning mapping from
images to semantics, we can transfer a policy, pre-trained in the simulator, to
the real world, thereby eliminating real-world on-policy agent interactions to
learn, which are costly and risky. In addition, using image-to-semantics
mapping is advantageous in terms of the computational efficiency to train the
policy and the interpretability of the obtained policy over other types of
sim-to-real transfer strategies. To tackle the main difficulty in learning
image-to-semantics mapping, namely the human annotation cost for producing a
training dataset, we propose two techniques: pair augmentation with the
transition function in the simulator environment and active learning. We
observed a reduction in the annotation cost without a decline in the
performance of the transfer, and the proposed approach outperformed the
existing approach without annotation.
