Control of Wheeled-Legged Quadrupeds Using Deep Reinforcement Learning

The control problem of wheeled-legged locomotion is still an open problem in the robotics community. Each leg has multiple discrete control modes (rolling, point-foot mode, swing phase), which results in highly nonlinear system dynamics. Most existing works rely on model-based control approaches, and they reduce the complexity of the problem by introducing handcrafted contact sequences or simplified dynamics models. In this work, we attempt to develop a locomotion controller for a wheeled-legged robot using model-free Reinforcement Learning (RL). We train a control policy in simulation, where we simulate the full dynamics of the system and random external disturbances. We then deploy the trained policy on the real robot. Like recent state-of-the-arts in legged locomotion using RL, our preliminary results show that RL is a promising framework for wheeled-legged robots. The policy learns to dynamically switch between driving mode and walking mode in response to the user command and terrain.