Improving Input-Output Linearizing Controllers for Bipedal Robots via Reinforcement Learning
This work addresses control robustness issues for bipedal robots, but it is incremental as it builds on existing controller structures with RL enhancements.
The paper tackled the challenges of model uncertainty and input constraints in input-output linearizing controllers for bipedal robots by using reinforcement learning to add a learned compensation term and incorporate constraints, demonstrating effectiveness on the RABBIT robot across different uncertainty levels.
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.