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.

翻译：安全抓取物体并避免滑移是机器人操作中的基本挑战，然而传统技术常将滑移视为二元事件，从而过度简化了该问题。本研究提出了一个既能识别滑移事件又能评估其严重程度的框架。我们基于GelSight Mini传感器捕获的触觉形变数据进行详细矢量场分析，引入了一组特征。两个不同的机器学习模型利用这些特征：一个专注于滑移检测，另一个评估滑移的严重性，即物体相对于传感器表面的滑移速度。我们的滑移检测模型平均准确率达到92%，滑移严重性估计模型对未见物体的平均绝对误差为0.6厘米/秒。为展示该框架的协同方法，我们在触觉反馈引导的垂直滑动任务中同时使用了这两个模型。凭借滑移检测的高精度，我们将其作为基础校正模型，并将滑移严重性估计集成到反馈控制回路中，从而在不产生过度补偿的情况下处理滑移。