  The main drawbacks of input-output linearizing controllers are the need for
precise dynamics models and not being able to account for input constraints.
Model uncertainty is common in almost every robotic application and input
saturation is present in every real world system. In this paper, we address
both challenges for the specific case of bipedal robot control by the use of
reinforcement learning techniques. Taking the structure of a standard
input-output linearizing controller, we use an additive learned term that
compensates for model uncertainty. Moreover, by adding constraints to the
learning problem we manage to boost the performance of the final controller
when input limits are present. We demonstrate the effectiveness of the designed
framework for different levels of uncertainty on the five-link planar walking
robot RABBIT.
