Training effective embodied AI agents often involves manual reward
engineering, expert imitation, specialized components such as maps, or
leveraging additional sensors for depth and localization. Another approach is
to use neural architectures alongside self-supervised objectives which
encourage better representation learning. In practice, there are few guarantees
that these self-supervised objectives encode task-relevant information. We
propose the Scene Graph Contrastive (SGC) loss, which uses scene graphs as
general-purpose, training-only, supervisory signals. The SGC loss does away
with explicit graph decoding and instead uses contrastive learning to align an
agent's representation with a rich graphical encoding of its environment. The
SGC loss is generally applicable, simple to implement, and encourages
representations that encode objects' semantics, relationships, and history.
Using the SGC loss, we attain significant gains on three embodied tasks: Object
Navigation, Multi-Object Navigation, and Arm Point Navigation. Finally, we
present studies and analyses which demonstrate the ability of our trained
representation to encode semantic cues about the environment.

利用 Scene Graph Contrastive (SGC) loss 来训练具有实际效果的体验式人工智能代理，其利用神经体系结构和自我监督目标以提高表现学习，即时不利于任务相关信息的编码。这种方法广泛适用且简单易行，可以鼓励代表的编码对象的语义，关系和历史，通过 三个体验式任务的实验，验证了其有效性，并证明其能够编码关于环境的语义提示。