Correspondence learning is a fundamental problem in robotics, which aims to
learn a mapping between state, action pairs of agents of different dynamics or
embodiments. However, current correspondence learning methods either leverage
strictly paired data -- which are often difficult to collect -- or learn in an
unsupervised fashion from unpaired data using regularization techniques such as
cycle-consistency -- which suffer from severe misalignment issues. We propose a
weakly supervised correspondence learning approach that trades off between
strong supervision over strictly paired data and unsupervised learning with a
regularizer over unpaired data. Our idea is to leverage two types of weak
supervision: i) temporal ordering of states and actions to reduce the
compounding error, and ii) paired abstractions, instead of paired data, to
alleviate the misalignment problem and learn a more accurate correspondence.
The two types of weak supervision are easy to access in real-world
applications, which simultaneously reduces the high cost of annotating strictly
paired data and improves the quality of the learned correspondence.

本文提出了一种基于弱监督的通信学习方法，结合了严格匹配数据和非匹配数据学习，利用状态和动作的时间关系和配对抽象来降低成本和提高通信准确性。