Demonstrating A Walk in the Park: Learning to Walk in 20 Minutes With Model-Free Reinforcement Learning

Laura M Smith
University of California, Berkeley
Ilya Kostrikov
University of California, Berkeley
Sergey Levine
University of California, Berkeley
Paper Website

Paper ID 56

Session 7. Mobile Manipulation and Locomotion


Poster Session Wednesday, July 12

Poster 24

Abstract: Deep reinforcement learning is a promising approach to learning policies in unstructured environments. Due to its sample inefficiency, though, 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 careful MDP formulation lead to learning quadruped locomotion in only 20 minutes in the real world. We evaluate our approach on several indoor and outdoor terrains that are known to be challenging for classical, model-based controllers and observe that the robot consistently learns a walking gait on all of these terrains. Finally, we evaluate our design decisions in a simulated environment.