We propose SplitNet, a method for decoupling visual perception and policy
learning. By incorporating auxiliary tasks and selective learning of portions
of the model, we explicitly decompose the learning objectives for visual
navigation into perceiving the world and acting on that perception. We show
dramatic improvements over baseline models on transferring between simulators,
an encouraging step towards Sim2Real. Additionally, SplitNet generalizes better
to unseen environments from the same simulator and transfers faster and more
effectively to novel embodied navigation tasks. Further, given only a small
sample from a target domain, SplitNet can match the performance of traditional
end-to-end pipelines which receive the entire dataset. Code is available
this https URL

SplitNet 是一种用于分离视觉感知和策略学习的方法，通过辅助任务和选择性学习模型的一部分，明确将视觉导航的学习目标分解为感知世界并对其进行操作。通过在不同的模拟器之间进行转移学习，SplitNet 相对于基线模型展现了极大的改进，这是迈向 Sim2Real 的一个有益的步骤。此外，SplitNet 在相同模拟器中具有更好的泛化能力，并且更快、更有效地将其应用于新的身体导航任务。此外，仅仅给定目标领域的少量样本，SplitNet 就能够达到接收整个数据集的传统端到端流水线的性能，其代码已在 https URL 上提供。