Deep reinforcement learning is a promising approach to learning policies in uncontrolled environments that do not require domain knowledge. Unfortunately, due to sample inefﬁciency, deep RL applications have primarily focused on simulated environments. In this work, we demonstrate that the recent advancements in machine learning algorithms and libraries combined with a carefully tuned robot controller lead to learning quadruped locomotion in only 20 minutes in the real world. We evaluate our approach on several indoor and outdoor terrains which are known to be challenging for classical model-based controllers. We observe the robot to be able to learn walking gait consistently on all of these terrains. Finally, we evaluate our design decisions in a simulated environment. We provide videos of all real-world training and code to reproduce our results on our website: https://sites.google.com/ berkeley.edu/walk-in-the-park in accessibility to the Minitaur, the A1 robot has also been used to study real-world deployment in recent works.
2022: Laura Smith, Ilya Kostrikov, S. Levine