An Advanced Reinforcement Learning Control Method for Quadruped Robots in Typical Urban Terrains

Quadruped robots, with their exceptional flexibility and stable structure, are highly suitable for traversing the complex unstructured terrains in urban environments. However, the current flexibility and stability of quadruped robots based on reinforcement learning are still not ideal in these terrains. To address this limitation, a large-scale parallel technology-based end-to-end teacher-student learning network framework is proposed, where the Gated Recurrent Unit achieves a potential estimation of the heights surrounding the robot. Meanwhile, by introducing an omnidirectional terrain learning curriculum, the robot can move in any commanded direction, achieving smooth output and tracking of motor joint angles. By utilizing state machines, the model trained from the simulation is deployed in the Unitree Go1 robot via zero-shot learning. Simulation and real-world experiments have demonstrated that this approach significantly enhances the robot's adaptability and mobility across various urban terrains such as gravel, grass, slopes, and steps.