  Recent advances in unsupervised representation learning significantly
improved the sample efficiency of training Reinforcement Learning policies in
simulated environments. However, similar gains have not yet been seen for
real-robot reinforcement learning. In this work, we focus on enabling
data-efficient real-robot learning from pixels. We present Contrastive
Pre-training and Data Augmentation for Efficient Robotic Learning (CoDER), a
method that utilizes data augmentation and unsupervised learning to achieve
sample-efficient training of real-robot arm policies from sparse rewards. While
contrastive pre-training, data augmentation, demonstrations, and reinforcement
learning are alone insufficient for efficient learning, our main contribution
is showing that the combination of these disparate techniques results in a
simple yet data-efficient method. 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 30 minutes of mean real-world training time. We
include videos and code on the project website:
https://sites.google.com/view/efficient-robotic-manipulation/home
