外观
论文arXiv 2610.1028321 天前
用于灵巧抓取稳定性的时序视觉-触觉学习
原文链接：Temporal Visuo-Tactile Learning for Dexterous Grasp Stability（arXiv 2610.10283 · 发布日期：2026-10-07）
Temporal Visuo-Tactile Learning for Dexterous Grasp Stability
针对灵巧手抓取稳定性预测，训练时序视觉-触觉多模态模型，并在真实多指机器人重抓中验证。
核心要点
- 现有抓取研究多侧重平行夹爪视觉选取，较少系统研究指尖动态触觉。
- 采集200个物体的1万次抓取，记录视觉、本体与四个Digit 360触觉流。
- 端到端时序多模态模型由抬起前观测预测抬起后稳定性，并比较模态与骨干。
- 消融显示动态高分辨率触觉提升预测；数据集已公开供使用。
验证范围与局限：验证包括真实多指机器人上的在线稳定性门控，视觉-触觉重抓使执行抬起成功率较非触觉门控高10.5个百分点。
英文摘要 (Abstract)
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/.
作者：Ken Nakahara, Aleksei Buvailik, Prokhor Kotov, Roberto Calandra
来源
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