动态调整视觉导航的特征与层，提升效率与可解释性。
DynaNav: Dynamic Feature and Layer Selection for Efficient Visual Navigation
- 根据场景复杂度动态选择特征和网络层，减少计算量。
- 相比ViNT，FLOPs降低2.26倍，推理时间减少42.3%。
- 适合资源受限的机器人导航场景，兼顾性能与效率。
视觉导航对机器人与具身智能至关重要。然而，现有基础模型尤其是带有Transformer解码器的模型存在计算开销高、可解释性差的问题，限制了其在资源受限场景中的部署。为此，我们提出DynaNav——一种动态视觉导航框架，依据场景复杂度自适应选择特征与网络层。该框架采用可训练的硬特征选择器实现稀疏操作，提升效率与可解释性；同时将特征选择融入早退出机制，通过贝叶斯优化确定最优退出阈值，进一步降低计算成本。在基于真实世界的数据集与模拟环境中的大量实验表明，DynaNav相较ViNT，在四个公开数据集上实现2.26倍的FLOPs降低、42.3%的推理时间减少、32.8%的内存占用下降，同时提升导航性能。
原文摘要 · Abstract (English)
Visual navigation is essential for robotics and embodied AI. However, existing foundation models, particularly those with transformer decoders, suffer from high computational overhead and lack interpretability, limiting their deployment in resource-tight scenarios. To address this, we propose DynaNav, a Dynamic Visual Navigation framework that adapts feature and layer selection based on scene complexity. It employs a trainable hard feature selector for sparse operations, enhancing efficiency and interpretability. Additionally, we integrate feature selection into an early-exit mechanism, with Bayesian Optimization determining optimal exit thresholds to reduce computational cost. Extensive experiments in real-world-based datasets and simulated environments demonstrate the effectiveness of DynaNav. Compared to ViNT, DynaNav achieves a 2.26x reduction in FLOPs, 42.3% lower inference time, and 32.8% lower memory usage, while improving navigation performance across four public datasets.
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