Learning Efficient and Robust Multi-Modal Quadruped Locomotion: A Hierarchical Approach

Abstract

Four-legged animals are able to change their gaits adaptively for lower energy consumption. However, designing a robust controller for their robot counterparts with multi-modal locomotion remains challenging. In this paper, we present a hierarchical control framework that decomposes this challenge into two kinds of problems: high-level decision-making for gait selection and robust low-level control in complex application environments. For gait transitions, we use reinforcement learning (RL) to design a gait policy that selects the optimal gaits in different environments. After the gait is decided, model predictive control (MPC) is applied to implement the desired gait. To improve the robustness of the locomotion, a model adaptation policy is developed to optimize the input parameters of our MPC controller adaptively. The control framework is first trained and tested in simulation, and then it is applied directly to a quadruped robot in real without any fine-tuning. We show that our control framework is more energy efficient by choosing different gaits and is more robust by adjusting model parameters compared to baseline controllers.