TiltXter: ピペット遠隔操作のためのCNNによる傾斜角電気触覚レンダリング
TiltXter: CNN-based Electro-tactile Rendering of Tilt Angle for Telemanipulation of Pasteur Pipettes
ロボットグリッパで把持した変形可能なピペットの傾きをCNNで推定し、その情報を電気触覚刺激として提示することで、遠隔操作の精度と成功率を向上させるシステムを提案した。
著者: Miguel Altamirano Cabrera, Jonathan Tirado, Aleksey Fedoseev, Oleg Sautenkov, Vladimir Poliakov, Pavel Kopanev, Dzmitry Tsetserukou
分類: cs.RO, cs.AI
原文アブストラクト
The shape of deformable objects can change drastically during grasping by robotic grippers, causing an ambiguous perception of their alignment and hence resulting in errors in robot positioning and telemanipulation. Rendering clear tactile patterns is fundamental to increasing users' precision and dexterity through tactile haptic feedback during telemanipulation. Therefore, different methods have to be studied to decode the sensors' data into haptic stimuli. This work presents a telemanipulation system for plastic pipettes that consists of a Force Dimension Omega.7 haptic interface endowed with two electro-stimulation arrays and two tactile sensor arrays embedded in the 2-finger Robotiq gripper. We propose a novel approach based on convolutional neural networks (CNN) to detect the tilt of deformable objects. The CNN generates a tactile pattern based on recognized tilt data to render further electro-tactile stimuli provided to the user during the telemanipulation. The study has shown that using the CNN algorithm, tilt recognition by users increased from 23.13\% with the downsized data to 57.9%, and the success rate during teleoperation increased from 53.12% using the downsized data to 92.18% using the tactile patterns generated by the CNN.
