  This paper presents a framework for learning visual representations from
unlabeled video demonstrations captured from multiple viewpoints. We show that
these representations are applicable for imitating several robotic tasks,
including pick and place. We optimize a recently proposed self-supervised
learning algorithm by applying contrastive learning to enhance task-relevant
information while suppressing irrelevant information in the feature embeddings.
We validate the proposed method on the publicly available Multi-View Pouring
and a custom Pick and Place data sets and compare it with the TCN triplet
baseline. We evaluate the learned representations using three metrics:
viewpoint alignment, stage classification and reinforcement learning, and in
all cases the results improve when compared to state-of-the-art approaches,
with the added benefit of reduced number of training iterations.
