Hierarchical Terrain-Aware Control for Quadrupedal Locomotion by Combining Deep Reinforcement Learning and Optimal Control

Quadruped robots possess advantages on different terrains over other types of mobile robots by virtue of their flexible choices of foothold points. It is crucial to integrate terrain perception with motion planning to exploit the potential of quadruped robots. We propose a novel hierarchical terrain-aware control (HTC) framework, which leverages deep reinforcement learning (DRL) for the high-level planner and optimal control for the low-level controller. In general, traditional control methods yield better stability by using an optimization algorithm. In addition, DRL is able to offer more adaptive behavior. Our approach makes full use of the advantages of these two methods and possesses better adaptability and stability in challenging natural environments. Furthermore, the global height map of the terrain serves as visual information for the DRL, which determines the desired footholds for the robot's leg swings and body postures. Optimal control calculates the torque of the joints on the standing legs to maintain body balance. Our method is tested on various terrains both simulated and real environments. The experimental results show that HTC can effectively enhance the adaptability of the quadruped robot by coordinating body posture