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Found 9 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.
Development of Human Motion Prediction Strategy using Inception Residual Block
Subsequently, we further propose to feed the output of the inception residual block as an input to the Graph Convolution Neural Network (GCN) due to its better spatial feature learning capability.
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.
Human Gait State Prediction Using Cellular Automata and Classification Using ELM
In this research article, we have reported periodic cellular automata rules for different gait state prediction and classification of the gait data using extreme machine Leaning (ELM).
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).
Supervised Q-walk for Learning Vector Representation of Nodes in Networks
Automatic feature learning algorithms are at the forefront of modern day machine learning research.
Development of a Fuzzy Expert System based Liveliness Detection Scheme for Biometric Authentication
We have used two testing parameters, (a) under bad illumination and (b) less movement in eyes and mouth in case of real user to evaluate the performance of the system.
