  In recent years, policy learning methods using either reinforcement or
imitation have made significant progress. However, both techniques still suffer
from being computationally expensive and requiring large amounts of training
data. This problem is especially prevalent in real-world robotic manipulation
tasks, where access to ground truth scene features is not available and
policies are instead learned from raw camera observations. In this paper, we
demonstrate the efficacy of learning image keypoints via the Dense
Correspondence pretext task for downstream policy learning. Extending prior
work to challenging multi-object scenes, we show that our model can be trained
to deal with important problems in representation learning, primarily
scale-invariance and occlusion. We evaluate our approach on diverse robot
manipulation tasks, compare it to other visual representation learning
approaches, and demonstrate its flexibility and effectiveness for
sample-efficient policy learning.
