604 citations · 608 across the 2 of their papers we have counts for
5 papers
Unsupervised preprocessing for Tactile Data
Maximilian Karl, Justin Bayer, Patrick van der Smagt
Tactile information is important for gripping, stable grasp, and in-hand manipulation, yet the complexity of tactile data prevents widespread use of such sensors. We make use of an…
ML-based tactile sensor calibration: A universal approach
Maximilian Karl, Artur Lohrer, Dhananjay Shah +5
We study the responses of two tactile sensors, the fingertip sensor from the iCub and the BioTac under different external stimuli. The question of interest is to which degree both…
A Differentiable Transition Between Additive and Multiplicative Neurons
Wiebke Köpp, Patrick van der Smagt, Sebastian Urban
Existing approaches to combine both additive and multiplicative neural units either use a fixed assignment of operations or require discrete optimization to determine what function…
FlowNet: Learning Optical Flow with Convolutional Networks
Philipp Fischer, Alexey Dosovitskiy, Eddy Ilg +6
Convolutional neural networks (CNNs) have recently been very successful in a variety of computer vision tasks, especially on those linked to recognition. Optical flow estimation ha…
Unsupervised Feature Learning for low-level Local Image Descriptors
Christian Osendorfer, Justin Bayer, Sebastian Urban +1
Unsupervised feature learning has shown impressive results for a wide range of input modalities, in particular for object classification tasks in computer vision. Using a large amo…