用触觉变形场检测并量化机械臂滑动严重程度，提升抓取稳定性。
Learned Slip-Detection-Severity Framework using Tactile Deformation Field Feedback for Robotic Manipulation
- 基于凝胶触觉传感器的形变向量场分析提取滑移特征。
- 滑移检测准确率达92%，严重程度估计误差仅0.6 cm/s。
- 适合需要精细力控的抓取任务，如易碎品操作。
安全抓取与防滑是机器人操作的核心挑战，传统方法常将滑移视为二元事件。本文提出一种框架，可同时识别滑移事件并评估其严重程度。基于GelSight Mini传感器捕获的触觉形变数据，构建了向量场分析特征集。采用两个独立机器学习模型：一个用于滑移检测，另一个评估滑移严重程度（即物体相对于传感器表面的滑动速度）。滑移检测模型平均准确率达92%，滑移严重程度估计模型在未见物体上的平均绝对误差（MAE）为0.6 cm/s。通过在垂直滑动任务中融合两模型，利用高精度检测作为基础校正，并将严重程度估计引入反馈控制，实现精准补偿而不过度调整。
原文摘要 · Abstract (English)
Safely handling objects and avoiding slippage are fundamental challenges in robotic manipulation, yet traditional techniques often oversimplify the issue by treating slippage as a binary occurrence. Our research presents a framework that both identifies slip incidents and measures their severity. We introduce a set of features based on detailed vector field analysis of tactile deformation data captured by the GelSight Mini sensor. Two distinct machine learning models use these features: one focuses on slip detection, and the other evaluates the slip's severity, which is the slipping velocity of the object against the sensor surface. Our slip detection model achieves an average accuracy of 92%, and the slip severity estimation model exhibits a mean absolute error (MAE) of 0.6 cm/s for unseen objects. To demonstrate the synergistic approach of this framework, we employ both the models in a tactile feedback-guided vertical sliding task. Leveraging the high accuracy of slip detection, we utilize it as the foundational and corrective model and integrate the slip severity estimation into the feedback control loop to address slips without overcompensating.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。