发表机构
TU Dresden（德累斯顿工业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究利用高分辨率动态触觉和时间多模态模型预测灵巧手抓取稳定性，并在真实机器人上实现视觉-触觉引导的重新抓取，将成功率提升10.5个百分点。
AI 中文摘要
人类利用指尖触觉反馈几乎可以完美地抓取日常物体，但机器人抓取文献中大部分工作强调基于视觉的平行夹爪抓取选择。在这项工作中，我们系统地研究了高分辨率、动态触觉感知如何有助于灵巧机器人手的抓取稳定性预测和模型引导抓取。为此，我们使用配备四个Digit 360触觉传感器的多指机器人手收集了跨越200个物体的10,000次抓取试验的数据集，在每次抓取过程中记录外部视觉、本体感觉和触觉流。利用该数据集，我们训练了端到端的时间多模态模型，以从抓取前观察预测提升后的稳定性，并比较了感知模态和编码骨干网络。实验结果和受控输入消融表明，结合触觉，特别是高分辨率、动态触觉，提高了抓取稳定性预测。最后，我们将学习到的预测器作为在线稳定性门控部署在真实机器人上，其中视觉-触觉模型引导的重新抓取将执行提升的成功率比非触觉门控提高了10.5个百分点。这些结果表明，丰富的指尖感知和捕捉触觉动态的表达性时间模型可以支持多指手的抓取学习，而无需显式接触或力建模，为从触觉经验到稳定灵巧操作提供了一条可扩展的数据驱动路径。数据集在此https URL公开可用。
英文摘要
Humans can grasp everyday objects with almost perfect success rates using fingertip tactile feedback, yet much of the robotic grasping literature emphasizes vision-based grasp selection with parallel grippers. In this work, we systematically investigate how high-resolution, dynamic tactile sensing contributes to grasp stability prediction and model-guided grasping in dexterous robotic hands. To this end, we collected a dataset of 10,000 grasp trials across 200 objects using a multi-fingered robotic hand equipped with four Digit 360 tactile sensors, recording external vision, proprioception, and tactile streams throughout each grasp. With this dataset, we trained end-to-end temporal multimodal models to predict post-lift stability from pre-lift grasp observations and compared sensing modalities and encoding backbones. Experimental results and controlled input ablations show that incorporating touch, and particularly high-resolution, dynamic touch, improves grasp stability prediction. Finally, we deployed the learned predictor as an online stability gate on the real robot, where visuo-tactile model-guided regrasping improved the success rate among executed lifts by 10.5 percentage points over a non-tactile gate. These results show how rich fingertip sensing and expressive temporal models that capture the dynamics of touch can support learned grasping with multi-fingered hands without explicit contact or force modeling, providing a scalable data-driven path from tactile experience toward stable dexterous manipulation. The dataset is publicly available at https://lasr-lab.github.io/dexterous-grasp-stability/.
Comments12 Pages. Website: https://lasr-lab.github.io/dexterous-grasp-stability/
