发表机构
Imperial College London（帝国理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出带校准数字孪生的机器人辅助滑动触诊框架，结合时空图神经网络实现浅血管定位，在三类数据集上验证了模拟到真实的定位精度，为可信触觉触诊提供了进展。
AI 中文摘要
可靠的浅表层皮下血管定位对于安全的机器人辅助静脉穿刺及血管感知操作至关重要，但在物理硬件上采集多样化的触觉数据成本高、耗时长，还可能损坏基于视觉的软触觉传感器。本文提出一种机器人辅助滑动触诊框架，其中校准后的数字孪生可生成带标签的触觉序列，减少对真实世界数据的依赖。该孪生模型对传感器-血管接触进行建模，采用基于贝叶斯优化的域适应方法针对真实触诊轨迹进行校准，并在滑动方向和接触条件上进行随机化处理。在模拟标记物轨迹上训练的时空图神经网络执行逐节点血管分类，并通过2D-3D-2D几何投影生成可人工验证的俯视图定位图。我们评估了三个数据集：Sim（模拟数据集）、Silicone（硅胶数据集）和Meat（生肉体模，血管模型标称深度为0至30毫米），采用四种训练-测试配置：Sim→Sim、Sim→Silicone、Sim→Meat和Meat→Silicone。校准后的孪生模型在四种典型交互下，最深接触处的模拟到真实标记物对齐平均绝对误差为0.50毫米。在重投影至1毫米俯视图网格后，四种模型的预测血管像素与最近真实血管像素的平均距离为1.05至5.49毫米，除Sim→Meat外，其余配置的距离为1.05至1.31毫米。Sim→Meat的误差更大，反映了更大的域偏移以及当前模拟迁移的局限性。这些结果表明，通过校准模拟、可解释定位和透明跨域评估，在实现可信触觉触诊方面取得了进展。代码、模型权重和数据可在GitHub和Zenodo上公开获取。
英文摘要
Reliable localisation of shallow subsurface vessels is important for safe robot-assisted venous access and vessel-aware manipulation, but collecting diverse tactile data on physical hardware is costly, time-consuming, and can degrade soft vision-based tactile sensors. We present a robot-assisted sliding-palpation framework in which a calibrated digital twin generates labelled tactile sequences, reducing reliance on real-world data. The twin models sensor-vessel contact, is calibrated against real palpation trajectories using Bayesian-optimisation-based domain adaptation, and is randomised over sliding direction and contact conditions. A spatio-temporal graph neural network trained on simulated marker trajectories performs per-node vessel classification and produces a human-verifiable top-view localisation map through 2D-to-3D-to-2D geometric projection. We evaluate three datasets: Sim, Silicone, and Meat, the latter a raw-meat phantom with vessel models at nominal depths of 0 to 30 mm, using four train-to-test configurations: Sim to Sim, Sim to Silicone, Sim to Meat, and Meat to Silicone. The calibrated twin achieves a simulated-to-real marker-alignment mean absolute error of 0.50 mm at deepest contact across four canonical interactions. After reprojection onto a 1 mm top-view grid, predicted vessel pixels lie on average 1.05 to 5.49 mm from the nearest true vessel pixel across the four models, with 1.05 to 1.31 mm for all except Sim to Meat. The larger error for Sim to Meat reflects the greater domain shift and current limit of simulation transfer. These results demonstrate progress toward trustworthy tactile palpation through calibrated simulation, interpretable localisation, and transparent cross-domain evaluation. Code, model weights, and data are publicly available on GitHub and Zenodo.
CommentsECCV workshop paper
