  Reinforcement learning (RL) for bipedal locomotion has recently demonstrated
robust gaits over moderate terrains using only proprioceptive sensing. However,
such blind controllers will fail in environments where robots must anticipate
and adapt to local terrain, which requires visual perception. In this paper, we
propose a fully-learned system that allows bipedal robots to react to local
terrain while maintaining commanded travel speed and direction. Our approach
first trains a controller in simulation using a heightmap expressed in the
robot's local frame. Next, data is collected in simulation to train a heightmap
predictor, whose input is the history of depth images and robot states. We
demonstrate that with appropriate domain randomization, this approach allows
for successful sim-to-real transfer with no explicit pose estimation and no
fine-tuning using real-world data. To the best of our knowledge, this is the
first example of sim-to-real learning for vision-based bipedal locomotion over
challenging terrains.
