Terrain-Aware Risk-Assessment-Network-Aided Deep Reinforcement Learning for Quadrupedal Locomotion in Tough Terrain

When it comes to the control system of quadruped robots, deep reinforcement learning (DRL) is considered to be a promising solution. Despite years of development in this field, difficulties remain in guaranteeing the action stability of DRL-based quadruped robots' locomotion, especially in tough terrain. In this paper, a terrain-aware teacher-student controller integrating a risk assessment network (RAN) is proposed to alleviate this problem. During the training phase, the RAN can evaluate the risk level of historical observation or current state and further guide the update of the policy, thereby assisting the policy in selecting better actions and avoid risky ones. Furthermore, the real-time elevation map is transmitted to the controller as visual information, so that it can perceive the terrain to produce higher performance locomotion. With the aforementioned configuration, we enable a robot to traverse various challenging terrain in simulation and bound or trot stably in the real environment.