  We present a model-based framework for robot locomotion that achieves walking
based on only 4.5 minutes (45,000 control steps) of data collected on a
quadruped robot. To accurately model the robot's dynamics over a long horizon,
we introduce a loss function that tracks the model's prediction over multiple
timesteps. We adapt model predictive control to account for planning latency,
which allows the learned model to be used for real time control. Additionally,
to ensure safe exploration during model learning, we embed prior knowledge of
leg trajectories into the action space. The resulting system achieves fast and
robust locomotion. Unlike model-free methods, which optimize for a particular
task, our planner can use the same learned dynamics for various tasks, simply
by changing the reward function. To the best of our knowledge, our approach is
more than an order of magnitude more sample efficient than current model-free
methods.
