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Perception of Tactile Directionality via Artificial Fingerpad Deformation and Convolutional Neural Networks
IEEE Transactions on Haptics ( IF 2.9 ) Pub Date : 2020-10-01 , DOI: 10.1109/toh.2020.2975555
Kenneth Gutierrez , Veronica J. Santos

Humans can perceive tactile directionality with angular perception thresholds of 14-40° via fingerpad skin displacement. Using deformable, artificial tactile sensors, the ability to perceive tactile directionality was developed for a robotic system to aid in object manipulation tasks. Two convolutional neural networks (CNNs) were trained on tactile images created from fingerpad deformation measurements during perturbations to a handheld object. A primary CNN regression model provided a point estimate of tactile directionality over a range of grip forces, perturbation angles, and perturbation speeds. A secondary CNN model provided a variance estimate that was used to determine uncertainty about the point estimate. A 5-fold cross-validation was performed to evaluate model performance. The primary CNN produced tactile directionality point estimates with an error rate of 4.3% for a 20° angular resolution and was benchmarked against an open-source force estimation network. The model was implemented in real-time for interactions with an external agent and the environment with different object shapes and widths. The perception of tactile directionality could be used to enhance the situational awareness of human operators of telerobotic systems and to develop decision-making algorithms for context-appropriate responses by semi-autonomous robots.

中文翻译:

通过人工指垫变形和卷积神经网络感知触觉方向性

人类可以通过指垫皮肤位移以 14-40° 的角度感知阈值感知触觉方向性。使用可变形的人工触觉传感器,为机器人系统开发了感知触觉方向的能力,以帮助执行对象操作任务。两个卷积神经网络 (CNN) 在对手持物体进行扰动期间通过指垫变形测量创建的触觉图像进行训练。主要的 CNN 回归模型提供了在一系列握力、扰动角度和扰动速度范围内的触觉方向性的点估计。次要 CNN 模型提供了方差估计,用于确定点估计的不确定性。进行了 5 折交叉验证以评估模型性能。主要的 CNN 产生触觉方向点估计,20°角分辨率的错误率为 4.3%,并以开源力估计网络为基准。该模型是实时实现的,用于与外部代理和具有不同对象形状和宽度的环境进行交互。触觉方向性的感知可用于增强遥控机器人系统的人类操作员的情境意识,并为半自主机器人的上下文适当响应开发决策算法。
更新日期:2020-10-01
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