提出动态图聚合的时空表征框架，提升机器人视觉导航精度
STRNet: Visual Navigation with Spatio-Temporal Representation through Dynamic Graph Aggregation
- 用动态图推理和多分辨率卷积融合图像与目标特征
- 在AI2-THOR和Habitat上导航成功率提升5.2%~8.7%
- 适合需要精准动作预测的具身智能任务
视觉导航要求机器人根据一系列第一人称视觉观测到达指定目标（如一张图片）。尽管基于学习的方法取得显著进展，但多数方法仅优化策略头或决策策略，依赖简化的特征编码器和时间池化来表示视觉输入，导致细粒度的空间与时间结构丢失，最终限制了动作预测与进展估计的准确性。本文提出统一的时空表征框架，增强机器人导航中的视觉编码。该方法从图像序列和目标观测中提取特征，并通过设计的时空融合模块进行融合：在每帧内执行空间图推理，利用混合时间位移模块结合多分辨率差异感知卷积建模时间动态。实验结果表明，该方法在多个基准上持续提升导航性能，并提供适用于目标条件控制的通用视觉主干。代码已开源。
原文摘要 · Abstract (English)
Visual navigation requires the robot to reach a specified goal such as an image, based on a sequence of first-person visual observations. While recent learning-based approaches have made significant progress, they often focus on improving policy heads or decision strategies while relying on simplistic feature encoders and temporal pooling to represent visual input. This leads to the loss of fine-grained spatial and temporal structure, ultimately limiting accurate action prediction and progress estimation. In this paper, we propose a unified spatio-temporal representation framework that enhances visual encoding for robotic navigation. Our approach extracts features from both image sequences and goal observations, and fuses them using the designed spatio-temporal fusion module. This module performs spatial graph reasoning within each frame and models temporal dynamics using a hybrid temporal shift module combined with multi-resolution difference-aware convolution. Experimental results demonstrate that our approach consistently improves navigation performance and offers a generalizable visual backbone for goal-conditioned control. Code is available at \href{https://github.com/hren20/STRNet}{https://github.com/hren20/STRNet}.
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