标题： PIG-Nav：预训练图像目标导航模型的关键见解 显示英文标题

标题： PIG-Nav: Key Insights for Pretrained Image Goal Navigation Models

摘要： 最近的研究探索了用于基于视觉的机器人导航的预训练（基础）模型，旨在实现跨不同环境的可泛化导航和正向迁移，同时提高在未见过设置中的零样本性能。 在本工作中，我们引入了PIG-Nav（预训练图像目标导航），一种新的方法，进一步研究了基于视觉的导航模型的预训练策略，并在两个关键领域做出了贡献。 在模型方面，我们确定了两个关键的设计选择，这些选择始终能提高预训练导航模型的性能：(1) 集成早期融合网络结构，通过适当预训练的视觉变换器（ViT）图像编码器结合视觉观察和目标图像，以及(2) 引入适当的辅助任务以增强全局导航表示学习，从而进一步提高导航性能。 在数据集方面，我们提出了一种新颖的数据预处理流程，以高效标记大规模游戏视频数据集用于导航模型训练。 我们证明了通过多样化的游戏视频增强现有的开放导航数据集可以提高模型性能。 我们的模型在两个复杂的模拟环境和一个真实环境中，相对于现有的视觉导航基础模型，在零样本设置中平均提高了22.6%，在微调设置中提高了37.5%。 这些结果推进了预训练图像目标导航模型的最先进水平。 值得注意的是，我们的模型在需要显著较少微调数据的情况下保持了具有竞争力的性能，突显了其在最小标注监督下进行实际部署的潜力。 显示英文摘要

摘要： Recent studies have explored pretrained (foundation) models for vision-based robotic navigation, aiming to achieve generalizable navigation and positive transfer across diverse environments while enhancing zero-shot performance in unseen settings. In this work, we introduce PIG-Nav (Pretrained Image-Goal Navigation), a new approach that further investigates pretraining strategies for vision-based navigation models and contributes in two key areas. Model-wise, we identify two critical design choices that consistently improve the performance of pretrained navigation models: (1) integrating an early-fusion network structure to combine visual observations and goal images via appropriately pretrained Vision Transformer (ViT) image encoder, and (2) introducing suitable auxiliary tasks to enhance global navigation representation learning, thus further improving navigation performance. Dataset-wise, we propose a novel data preprocessing pipeline for efficiently labeling large-scale game video datasets for navigation model training. We demonstrate that augmenting existing open navigation datasets with diverse gameplay videos improves model performance. Our model achieves an average improvement of 22.6% in zero-shot settings and a 37.5% improvement in fine-tuning settings over existing visual navigation foundation models in two complex simulated environments and one real-world environment. These results advance the state-of-the-art in pretrained image-goal navigation models. Notably, our model maintains competitive performance while requiring significantly less fine-tuning data, highlighting its potential for real-world deployment with minimal labeled supervision.