Current research in Visual Navigation reveals opportunities for improvement. First, the direct adoption of RNNs and Transformers often overlooks the specific differences between Embodied AI and traditional sequential data modelling, potentially limiting its performance in Embodied AI tasks. Second, the reliance on task-specific configurations, such as pre-trained modules and dataset-specific logic, compromises the generalizability of these methods. We address these constraints by initially exploring the unique differences between Navigation tasks and other sequential data tasks through the lens of Causality, presenting a causal framework to elucidate the inadequacies of conventional sequential methods for Navigation. By leveraging this causal perspective, we propose Causality-Aware Transformer (CAT) Networks for Navigation, featuring a Causal Understanding Module to enhance the models's Environmental Understanding capability. Meanwhile, our method is devoid of task-specific inductive biases and can be trained in an End-to-End manner, which enhances the method's generalizability across various contexts. Empirical evaluations demonstrate that our methodology consistently surpasses benchmark performances across a spectrum of settings, tasks and simulation environments. Extensive ablation studies reveal that the performance gains can be attributed to the Causal Understanding Module, which demonstrates effectiveness and efficiency in both Reinforcement Learning and Supervised Learning settings.

翻译：当前视觉导航领域的研究揭示了若干改进空间。首先，直接采用RNN和Transformer的方法往往忽视了具身人工智能与传统序列数据建模之间的本质差异，这可能限制其在具身AI任务中的性能表现。其次，对任务特定配置（如预训练模块和数据集专用逻辑）的依赖，影响了这些方法的泛化能力。我们通过因果关系的视角，首先探究了导航任务与其他序列数据任务之间的本质区别，提出了一个因果框架以阐明传统序列方法在导航任务中的局限性。基于这种因果视角，我们提出了用于导航任务的因果感知Transformer（CAT）网络，该网络通过因果理解模块增强了模型的环境理解能力。同时，我们的方法不包含任务特定的归纳偏置，可采用端到端方式进行训练，从而提升了方法在不同场景下的泛化性能。实证评估表明，我们的方法在多种设置、任务和仿真环境中均持续超越基准性能。大量消融实验证实，性能提升可归因于因果理解模块，该模块在强化学习和监督学习设置中均展现出卓越的有效性与效率。