Zobrazeno 1 - 10
of 43
pro vyhledávání: '"Jürgen Leitner"'
Publikováno v:
International Journal of Advanced Robotic Systems, Vol 9 (2012)
We present a combined machine learning and computer vision approach for robots to localize objects. It allows our iCub humanoid to quickly learn to provide accurate 3D position estimates (in the centimetre range) of objects seen. Biologically inspire
Externí odkaz:
https://doaj.org/article/20ac8f484c8c40b99be323f163729e80
Publikováno v:
The International Journal of Robotics Research. 39:183-201
We present a novel approach to perform object-independent grasp synthesis from depth images via deep neural networks. Our generative grasping convolutional neural network (GG-CNN) predicts a pixel-wise grasp quality that can be deployed in closed-loo
EGAD! an Evolved Grasping Analysis Dataset for diversity and reproducibility in robotic manipulation
We present the Evolved Grasping Analysis Dataset (EGAD), comprising over 2000 generated objects aimed at training and evaluating robotic visual grasp detection algorithms. The objects in EGAD are geometrically diverse, filling a space ranging from si
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0b39c0cd8d77065154d31c41aa171b54
Autor:
Douglas Morrison, Jürgen Leitner, Anton Milan, M. McTaggart, N. Kelly-Boxall, Peter Corke, Adam W. Tow
Publikováno v:
Advances on Robotic Item Picking ISBN: 9783030356781
The Amazon Robotics Challenge enlisted sixteen teams to each design a pick-and-place robot for autonomous warehousing of everyday household items. Herein we present the design of our custom-built, Cartesian robot Cartman, which won the first place in
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::eb8aeecd65c715845842ad95611b6fd3
https://doi.org/10.1007/978-3-030-35679-8_11
https://doi.org/10.1007/978-3-030-35679-8_11
We present a benchmark to facilitate simulated manipulation; an attempt to overcome the obstacles of physical benchmarks through the distribution of a real world, ground truth dataset. Users are given various simulated manipulation tasks with assigne
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1c3f59b64fa0a4ad0cc9d6dfc69b1363
http://arxiv.org/abs/1911.01557
http://arxiv.org/abs/1911.01557
Publikováno v:
IROS
Visual reaching and grasping is a fundamental problem in robotics research. This paper proposes a novel approach based on deep learning a control Lyapunov function and its derivatives by encouraging a differential constraint in addition to vanilla re
Autor:
Pieter Abbeel, Jürgen Leitner, Wolfram Burgard, Oliver Brock, Ben Upcroft, Raia Hadsell, Michael Milford, Niko Sünderhauf, Peter Corke, Dieter Fox, Walter J. Scheirer
Publikováno v:
The International Journal of Robotics Research. 37:405-420
The application of deep learning in robotics leads to very specific problems and research questions that are typically not addressed by the computer vision and machine learning communities. In this paper we discuss a number of robotics-specific learn
Publikováno v:
International Journal of Computer Vision. 128:1160-1161
Guest editorial In this special issue we consider the topic of Robotic Vision in the context of deep learning techniques. Vision for robotics, while closely related to classical computer vision, and often using it for inspiration, focuses on specific
Publikováno v:
ICRA
We quantify the accuracy of various simulators compared to a real world robotic reaching and interaction task. Simulators are used in robotics to design solutions for real world hardware without the need for physical access. The `reality gap' prevent
Publikováno v:
ICRA
Camera viewpoint selection is an important aspect of visual grasp detection, especially in clutter where many occlusions are present. Where other approaches use a static camera position or fixed data collection routines, our Multi-View Picking (MVP)