Reinforced iLQR: A Sample-Efficient Robot Locomotion Learning

Robot locomotion is a major challenge in robotics. Model-based approaches are vulnerable to model errors, and incur high computation overhead resulted from long control horizon. Model-free approaches are trained with a large number of training samples, which are expensive to obtain. In this paper, we develop a hybrid control and learning framework, called Reinforced iLQR (RiLQR), which combines the advantages of model-based iLQR control with model-free RL policy learning to simultaneously achieve high sample efficiency, low computation overhead, and high robustness against model errors in robot locomotion. Through extensive evaluation on the Mujoco platform, we demonstrate that RiLQR outperforms the state-of-the-art model-based and model-free baselines by big margins in a set of tasks with different complexities.