自适应分辨率拼贴提升视觉伺服精度与鲁棒性，无需训练即可实现高成功率抓取
ART-VS: Adaptive Resolution Tiling for Vision Transformer Visual Servoing
- 分两阶段自适应调整特征粒度：粗略对齐后局部高分辨率匹配
- 扰动下收敛率达95.4%，误差降低53%，速度超10倍、显存降27%
- 适用于未见物体的实时抓取，支持多种ViT模型
基于自监督视觉变压器（ViT）特征的视觉伺服可实现无需训练的机器人定位，具备强泛化能力，但面临鲁棒性与精度的根本权衡。粗粒度块级描述符提供稳定对应关系，但限制定位精度；提高图像分辨率虽能提升精度，但在扰动下鲁棒性提升有限——尽管使用12倍多的ViT块，收敛成功率仅从76.6%提升至81.0%。为此，我们提出自适应分辨率拼贴视觉伺服（ART-VS），一种两阶段方法：先在原始ViT分辨率下进行粗略对齐，再通过局部拼贴高分辨率匹配提升定位精度。无需任务特定训练，ART-VS在扰动下达到95.4%收敛率，较标准和全分辨率ViT方法分别提升18.8和14.4个百分点。相比前者，定位误差减少53%，运行速度超过10倍，显存占用降低27%。我们在三种ViT主干网络上验证该方法，并实现对未见物体实例的类别级抓取，在透明瓶和鞋子上分别取得95/100和98/100的成功率。代码已公开于 https://art-vs.github.io/。
原文摘要 · Abstract (English)
Visual servoing with self-supervised Vision Transformer (ViT) features enables training-free robotic positioning with strong generalization, but faces a fundamental trade-off between robustness and precision. Coarse patch-level descriptors provide stable correspondences yet limit positioning accuracy. Increasing image resolution improves precision but yields only marginal robustness gains - under perturbation, high-resolution processing improves convergence success rate from 76.6% to just 81.0% despite 12x more ViT patches. Therefore, we propose Adaptive Resolution Tiling Visual Servoing (ART-VS), a two-phase method that adapts feature granularity to servoing progress: a coarse phase at native ViT resolution for stable alignment, then a tiled high-resolution phase that restricts matching to local neighborhoods improving positioning accuracy. Without any task-specific training, ART-VS achieves 95.4% convergence under perturbation, outperforming standard and full-resolution ViT-based servoing by 18.8 and 14.4 percentage points. Over the former it reduces positioning error by 53%, while running at over 10x higher speed and 27% lower VRAM than the latter. We validate ART-VS across three ViT backbones and demonstrate real-world category-level grasping of unseen object instances, achieving 95/100 on transparent bottles and 98/100 on shoes. Code available under https://art-vs.github.io/.
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