Recently human–robot collaboration (HRC) has emerged as a promising paradigm for mass
personalization in manufacturing owing to the potential to fully exploit the strength of human …

Grasping is the process of picking up an object by applying forces and torques at a set of
contacts. Recent advances in deep learning methods have allowed rapid progress in robotic …

BACKGROUND Humans have a fantastic ability to manipulate objects of various shapes,
sizes, and materials and can control the objects' position in confined spaces with the …

To reduce data collection time for deep learning of robust robotic grasp plans, we explore
training from a synthetic dataset of 6.7 million point clouds, grasps, and analytic grasp …

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 …

We propose AffordanceNet, a new deep learning approach to simultaneously detect multiple
objects and their affordances from RGB images. Our AffordanceNet has two branches: an …

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 …

Learning how to interact with objects is an important step towards embodied visual
intelligence, but existing techniques suffer from heavy supervision or sensing requirements …

Accurate affordance detection and segmentation with pixel precision is an important piece in
many complex systems based on interactions, such as robots and assitive devices. We …

We present a new method to detect object affordances in real-world scenes using deep
Convolutional Neural Networks (CNN), an object detector and dense Conditional Random …