  We propose to address quadrupedal locomotion tasks using Reinforcement
Learning (RL) with a Transformer-based model that learns to combine
proprioceptive information and high-dimensional depth sensor inputs. While
learning-based locomotion has made great advances using RL, most methods still
rely on domain randomization for training blind agents that generalize to
challenging terrains. Our key insight is that proprioceptive states only offer
contact measurements for immediate reaction, whereas an agent equipped with
visual sensory observations can learn to proactively maneuver environments with
obstacles and uneven terrain by anticipating changes in the environment many
steps ahead. In this paper, we introduce LocoTransformer, an end-to-end RL
method that leverages both proprioceptive states and visual observations for
locomotion control. We evaluate our method in challenging simulated
environments with different obstacles and uneven terrain. We transfer our
learned policy from simulation to a real robot by running it indoors and in the
wild with unseen obstacles and terrain. Our method not only significantly
improves over baselines, but also achieves far better generalization
performance, especially when transferred to the real robot. Our project page
with videos is at https://rchalyang.github.io/LocoTransformer/ .
