The progress in the development of anthropomorphic hands for robotic and prosthetic
applications has not been followed by a parallel development of objective methods to …

Large, high-capacity models trained on diverse datasets have shown remarkable successes
on efficiently tackling downstream applications. In domains from NLP to Computer Vision …

Objective: Deep learning-based object recognition, 6D pose estimation, and semantic scene
understanding require a large amount of training data to achieve generalization. Time …

Benchmarking of robotic manipulations is one of the open issues in robotic research. An
important factor that has enabled progress in this area in the last decade is the existence of …

Grasping and manipulation with anthropomorphic robotic and prosthetic hands presents a
scientific challenge regarding mechanical design, sensor system, and control. Apart from the …

We propose a novel model-based method for estimating and tracking the six-degrees-of-
freedom (6DOF) pose of rigid objects of arbitrary shapes in real-time. By combining dense …

Deep learning has been widely used for inferring robust grasps. Although human-labeled
RGB-D datasets were initially used to learn grasp configurations, preparation of this kind of …

We present an object pose estimation approach exploiting both geometric depth and
photometric color information available from an RGB-D sensor. In contrast to various efforts …

In this letter, we proposea novel vision-based grasp system for closed-loop 6-degrees of
freedom grasping of unknown objects in cluttered environments. The key factor in our …

Large, high-capacity models trained on diverse datasets have shown remarkable successes
on efficiently tackling downstream applications. In domains from NLP to Computer Vision …