  Data-efficient learning of manipulation policies from visual observations is
an outstanding challenge for real-robot learning. While deep reinforcement
learning (RL) algorithms have shown success learning policies from visual
observations, they still require an impractical number of real-world data
samples to learn effective policies. However, recent advances in unsupervised
representation learning and data augmentation significantly improved the sample
efficiency of training RL policies on common simulated benchmarks. Building on
these advances, we present a Framework for Efficient Robotic Manipulation
(FERM) that utilizes data augmentation and unsupervised learning to achieve
extremely sample-efficient training of robotic manipulation policies with
sparse rewards. We show that, given only 10 demonstrations, a single robotic
arm can learn sparse-reward manipulation policies from pixels, such as
reaching, picking, moving, pulling a large object, flipping a switch, and
opening a drawer in just 15-50 minutes of real-world training time. We include
videos, code, and additional information on the project website --
https://sites.google.com/view/efficient-robotic-manipulation.
