The benefit of multi-task learning over single-task learning relies on the
ability to use relations across tasks to improve performance on any single
task. While sharing representations is an important mechanism to share
information across tasks, its success depends on how well the structure
underlying the tasks is captured. In some real-world situations, we have access
to metadata, or additional information about a task, that may not provide any
new insight in the context of a single task setup alone but inform relations
across multiple tasks. While this metadata can be useful for improving
multi-task learning performance, effectively incorporating it can be an
additional challenge. We posit that an efficient approach to knowledge transfer
is through the use of multiple context-dependent, composable representations
shared across a family of tasks. In this framework, metadata can help to learn
interpretable representations and provide the context to inform which
representations to compose and how to compose them. We use the proposed
approach to obtain state-of-the-art results in Meta-World, a challenging
multi-task benchmark consisting of 50 distinct robotic manipulation tasks.

提出一种多任务学习的方法，通过元数据构建可组合且可解释的表示，从而改进多任务学习性能，并在一个包含 50 个不同机器人操作任务的具有挑战性的多任务基准 Meta-World 上实现了最先进的结果。