Robotic touch, particularly when using soft optical tactile sensors, suffers from distortion caused by motion-dependent shear. The manner in which the sensor contacts a stimulus is entangled with the tactile information about the geometry of the stimulus. In this work, we propose a supervised convolutional deep neural network model that learns to disentangle, in the latent space, the components of sensor deformations caused by contact geometry from those due to sliding-induced shear. The approach is validated by reconstructing unsheared tactile images from sheared images and showing they match unsheared tactile images collected with no sliding motion. In addition, the unsheared tactile images give a faithful reconstruction of the contact geometry that is not possible from the sheared data, and robust estimation of the contact pose that can be used for servo control sliding around various 2D shapes. Finally, the contact geometry reconstruction in conjunction with servo control sliding were used for faithful full object reconstruction of various 2D shapes. The methods have broad applicability to deep learning models for robots with a shear-sensitive sense of touch.
翻译:机器人触摸,特别是使用软光学触动传感器时,会受到运动依赖剪切的扭曲。感应器接触刺激的方式与刺激的几何学的触动信息缠绕在一起。在这项工作中,我们提议了一个受监督的深神经网络模型,该模型在潜伏空间中学会解开接触与滑动诱发剪切剪的剪切机产生的感应变形的成分。该方法通过从剪切图像中重建未听的触动图像加以验证,并显示它们与没有滑动动作而收集的未听触动图像相匹配。此外,未听的触动图像使人们忠实地重建了从剪切取数据中不可能的接触几何学模型,并对可被用于在2D型形状周围移动控制滑动的感应变形的感应变变形物进行了精确估计。最后,与Servo控制滑动一起进行接触的几何测量重建被用于对各种2D型形状进行忠实的全方位重建。这些方法具有广泛应用性,可以用来对机器人进行深层感知的感应模型进行深思。