  Learning robot control policies from physics simulations is of great interest
to the robotics community as it may render the learning process faster,
cheaper, and safer by alleviating the need for expensive real-world
experiments. However, the direct transfer of learned behavior from simulation
to reality is a major challenge. Optimizing a policy on a slightly faulty
simulator can easily lead to the maximization of the `Simulation Optimization
Bias` (SOB). In this case, the optimizer exploits modeling errors of the
simulator such that the resulting behavior can potentially damage the robot. We
tackle this challenge by applying domain randomization, i.e., randomizing the
parameters of the physics simulations during learning. We propose an algorithm
called Simulation-based Policy Optimization with Transferability Assessment
(SPOTA) which uses an estimator of the SOB to formulate a stopping criterion
for training. The introduced estimator quantifies the over-fitting to the set
of domains experienced while training. Our experimental results on two
different second order nonlinear systems show that the new simulation-based
policy search algorithm is able to learn a control policy exclusively from a
randomized simulator, which can be applied directly to real systems without any
additional training.
