  To solve tasks in complex environments, robots need to learn from experience.
Deep reinforcement learning is a common approach to robot learning but requires
a large amount of trial and error to learn, limiting its deployment in the
physical world. As a consequence, many advances in robot learning rely on
simulators. On the other hand, learning inside of simulators fails to capture
the complexity of the real world, is prone to simulator inaccuracies, and the
resulting behaviors do not adapt to changes in the world. The Dreamer algorithm
has recently shown great promise for learning from small amounts of interaction
by planning within a learned world model, outperforming pure reinforcement
learning in video games. Learning a world model to predict the outcomes of
potential actions enables planning in imagination, reducing the amount of trial
and error needed in the real environment. However, it is unknown whether
Dreamer can facilitate faster learning on physical robots. In this paper, we
apply Dreamer to 4 robots to learn online and directly in the real world,
without simulators. Dreamer trains a quadruped robot to roll off its back,
stand up, and walk from scratch and without resets in only 1 hour. We then push
the robot and find that Dreamer adapts within 10 minutes to withstand
perturbations or quickly roll over and stand back up. On two different robotic
arms, Dreamer learns to pick and place multiple objects directly from camera
images and sparse rewards, approaching human performance. On a wheeled robot,
Dreamer learns to navigate to a goal position purely from camera images,
automatically resolving ambiguity about the robot orientation. Using the same
hyperparameters across all experiments, we find that Dreamer is capable of
online learning in the real world, establishing a strong baseline. We release
our infrastructure for future applications of world models to robot learning.
