Improving Input-Output Linearizing Controllers for Bipedal Robots via Reinforcement Learning

The need of precise dynamics models and not being able to account for input constraints are two of the main drawbacks of input-output linearizing controllers. 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 robots’ control by the use of reinforcement learning techniques. We demonstrate the performance of the designed controller for different uncertain scenarios on the five-link planar robot RABBIT. The advantages of the designed controller are highlighted and a comparison with a known effective adaptive controller is presented.