Learning Quadrupedal Locomotion over Challenging Terrain was published in Science Robotics and featured on the journal’s cover. The paper presents a radically robust legged locomotion controller for rough terrain and demonstrates remarkable zero-shot generalization from simulation to natural environments. The controller retains its robustness under conditions that have never been encountered during training: deformable terrain such as mud and snow, dynamic footholds such as rubble, and overground impediments such as thick vegetation and gushing water.
