Perception of Tactile Directionality via Artificial Fingerpad Deformation and Convolutional Neural Networks

被引:7
作者
Gutierrez, Kenneth [1 ]
Santos, Veronica J. [2 ]
机构
[1] Univ Calif Los Angeles, Los Angeles, CA 90095 USA
[2] Univ Calif Los Angeles, Mech & Aerosp Engn Dept, Los Angeles, CA 90095 USA
基金
美国国家科学基金会;
关键词
Perturbation methods; Tactile sensors; Force; Strain; Electrodes; Convolutional neural networks; manipulation; robot; skin displacement; tactile directionality; tactile images; tactile perception; tactile sensors; THUMB RESPONSES; OBJECT HELD; GRIP FORCE; REPRESENTATION; AFFERENTS; RESTRAINT; TEXTURES; SIGNALS; SLIP; SKIN;
D O I
10.1109/TOH.2020.2975555
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Humans can perceive tactile directionality with angular perception thresholds of 14-40 degrees 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 degrees 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.
引用
收藏
页码:831 / 839
页数:9
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