Abstract Purpose of Review This review provides a comprehensive overview of machine
learning approaches for vision-based robotic grasping and manipulation. Current trends and …

This article surveys the literature on human–robot object handovers. A handover is a
collaborative joint action, where an agent, the giver, gives an object to another agent, the …

Birds take off and land on a wide range of complex surfaces. In contrast, current robots are
limited in their ability to dynamically grasp irregular objects. Leveraging recent findings on …

As the basis for prehensile manipulation, it is vital to enable robots to grasp as robustly as
humans. Our innate grasping system is prompt, accurate, flexible, and continuous across …

In this paper, we present a modular robotic system to tackle the problem of generating and
performing antipodal robotic grasps for unknown objects from the n-channel image of the …

Object recognition is among the basic survival skills of human beings and other animals. To
date, artificial intelligence (AI) assisted high-performance object recognition is primarily …

For robots to navigate and interact more richly with the world around them, they will likely
require a deeper understanding of the world in which they operate. In robotics and related …

In this letter, a novel grasp detection neural network Squeeze-and-Excitation ResUNet (SE-
ResUNet) is developed, where the residual block with the channel attention is integrated …

We present the Evolved Grasping Analysis Dataset (EGAD), comprising over 2000
generated objects aimed at training and evaluating robotic visual grasp detection …

In this letter, we present a transformer-based architecture, namely TF-Grasp, for robotic
grasp detection. The developed TF-Grasp framework has two elaborate designs making it …