2 citations · 2 across the 9 of their papers we have counts for
9 papers
UltrasODM: A Dual Stream Optical Flow Mamba Network for 3D Freehand Ultrasound Reconstruction
Mayank Anand, Ujair Alam, Surya Prakash +3
Clinical ultrasound acquisition is highly operator-dependent, where rapid probe motion and brightness fluctuations often lead to reconstruction errors that reduce trust and clinica…
GRIM: Task-Oriented Grasping with Conditioning on Generative Examples
Shailesh, Alok Raj, Nayan Kumar +4
Task-Oriented Grasping (TOG) requires robots to select grasps that are functionally appropriate for a specified task - a challenge that demands an understanding of task semantics,…
Vision-Based Intelligent Robot Grasping Using Sparse Neural Network
Priya Shukla, Vandana Kushwaha, G C Nandi
In the modern era of Deep Learning, network parameters play a vital role in models efficiency but it has its own limitations like extensive computations and memory requirements, wh…
Context-aware 6D Pose Estimation of Known Objects using RGB-D data
Ankit Kumar, Priya Shukla, Vandana Kushwaha +1
6D object pose estimation has been a research topic in the field of computer vision and robotics. Many modern world applications like robot grasping, manipulation, autonomous navig…
Generating Quality Grasp Rectangle using Pix2Pix GAN for Intelligent Robot Grasping
Vandana Kushwaha, Priya Shukla, G C Nandi
Intelligent robot grasping is a very challenging task due to its inherent complexity and non availability of sufficient labelled data. Since making suitable labelled data available…
Development of a robust cascaded architecture for intelligent robot grasping using limited labelled data
Priya Shukla, Vandana Kushwaha, G. C. Nandi
Grasping objects intelligently is a challenging task even for humans and we spend a considerable amount of time during our childhood to learn how to grasp objects correctly. In the…
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
Priya Shukla, Nilotpal Pramanik, Deepesh Mehta +1
Our way of grasping objects is challenging for efficient, intelligent and optimal grasp by COBOTs. To streamline the process, here we use deep learning techniques to help robots le…
Semi-supervised Grasp Detection by Representation Learning in a Vector Quantized Latent Space
Mridul Mahajan, Tryambak Bhattacharjee, Arya Krishnan +2
For a robot to perform complex manipulation tasks, it is necessary for it to have a good grasping ability. However, vision based robotic grasp detection is hindered by the unavaila…
Robotic Grasp Manipulation Using Evolutionary Computing and Deep Reinforcement Learning
Priya Shukla, Hitesh Kumar, G. C. Nandi
Intelligent Object manipulation for grasping is a challenging problem for robots. Unlike robots, humans almost immediately know how to manipulate objects for grasping due to learni…