GI-NNet \& RGI-NNet: Development of Robotic Grasp Pose Models, Trainable with Large as well as Limited Labelled Training Datasets, under supervised and semi supervised paradigms

no code implementations • 15 Jul 2021 • Priya Shukla, Nilotpal Pramanik, Deepesh Mehta, G. C. Nandi

It is trained on Cornell Grasping Dataset (CGD) and attained 98. 87% grasp pose accuracy for detecting both regular and irregular shaped objects from RGB-Depth (RGB-D) images while requiring only one third of the network trainable parameters as compared to the existing approaches.