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.

该论文提出了一个从多个视角捕捉的无标注视频演示中学习视觉表示的框架，优化了最近提出的自监督学习算法，应用对比学习来增强与任务相关的信息和抑制特征嵌入中的无关信息，验证了所提出的方法在模拟几种机器人任务，包括 pick and place 任务中的应用，评估了学习表示的三个指标：视点对齐，阶段分类和强化学习，在所有情况下，结果都表明与现有方法相比，该方法水平更高，而且训练轮数更少。