  Model-free or learning-based control, in particular, reinforcement learning
(RL), is expected to be applied for complex robotic tasks. Traditional RL
requires a policy to be optimized is state-dependent, that means, the policy is
a kind of feedback (FB) controllers. Due to the necessity of correct state
observation in such a FB controller, it is sensitive to sensing failures. To
alleviate this drawback of the FB controllers, feedback error learning
integrates one of them with a feedforward (FF) controller. RL can be improved
by dealing with the FB/FF policies, but to the best of our knowledge, a
methodology for learning them in a unified manner has not been developed. In
this paper, we propose a new optimization problem for optimizing both the FB/FF
policies simultaneously. Inspired by control as inference, the optimization
problem considers minimization/maximization of divergences between trajectory,
predicted by the composed policy and a stochastic dynamics model, and
optimal/non-optimal trajectories. By approximating the stochastic dynamics
model using variational method, we naturally derive a regularization between
the FB/FF policies. In numerical simulations and a robot experiment, we
verified that the proposed method can stably optimize the composed policy even
with the different learning law from the traditional RL. In addition, we
demonstrated that the FF policy is robust to the sensing failures and can hold
the optimal motion. Attached video is also uploaded on youtube:
https://youtu.be/zLL4uXIRmrE
