Search Results for author:
Found 5 papers, 1 papers with code
Semi-Supervised Disentanglement of Tactile Contact~Geometry from Sliding-Induced Shear
The sense of touch is fundamental to human dexterity.
Tactile Image-to-Image Disentanglement of Contact Geometry from Motion-Induced Shear
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.
Spatio-temporal encoding improves neuromorphic tactile texture classification
With the increase in interest in deployment of robots in unstructured environments to work alongside humans, the development of human-like sense of touch for robots becomes important.
Investigating Convolutional Neural Networks using Spatial Orderness
Convolutional Neural Networks (CNN) have been pivotal to the success of many state-of-the-art classification problems, in a wide variety of domains (for e. g. vision, speech, graphs and medical imaging).
Scale Steerable Filters for Locally Scale-Invariant Convolutional Neural Networks
Augmenting transformation knowledge onto a convolutional neural network's weights has often yielded significant improvements in performance.
