  Domain adaptation is a common problem in robotics, with applications such as
transferring policies from simulation to real world and lifelong learning.
Performing such adaptation, however, requires informative data about the
environment to be available during the adaptation. In this paper, we present
domain curiosity -- a method of training exploratory policies that are
explicitly optimized to provide data that allows a model to learn about the
unknown aspects of the environment. In contrast to most curiosity methods, our
approach explicitly rewards learning, which makes it robust to environment
noise without sacrificing its ability to learn. We evaluate the proposed method
by comparing how much a model can learn about environment dynamics given data
collected by the proposed approach, compared to standard curious and random
policies. The evaluation is performed using a toy environment, two simulated
robot setups, and on a real-world haptic exploration task. The results show
that the proposed method allows data-efficient and accurate estimation of
dynamics.
