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Found 5 papers, 0 papers with code
Context-aware 6D Pose Estimation of Known Objects using RGB-D data
In this paper, we present an architecture that, unlike prior work, is context-aware.
Development of a robust cascaded architecture for intelligent robot grasping using limited labelled data
In the case of robots, we can not afford to spend that much time on making it to learn how to grasp objects effectively.
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
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.
Semi-supervised Grasp Detection by Representation Learning in a Vector Quantized Latent Space
However, vision based robotic grasp detection is hindered by the unavailability of sufficient labelled data.
Robotic Grasp Manipulation Using Evolutionary Computing and Deep Reinforcement Learning
Further for grasp orientation learning, we develop a deep reinforcement learning (DRL) model which we name as Grasp Deep Q-Network (GDQN) and benchmarked our results with Modified VGG16 (MVGG16).
