Zobrazeno 1 - 6
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pro vyhledávání: '"Kokic, Mia"'
Publikováno v:
IEEE Robotics and Automation Letters 5 (2020) 3352-3359
We propose to leverage a real-world, human activity RGB dataset to teach a robot Task-Oriented Grasping (TOG). We develop a model that takes as input an RGB image and outputs a hand pose and configuration as well as an object pose and a shape. We fol
Externí odkaz:
http://arxiv.org/abs/1910.11669
Publikováno v:
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2019
We develop a system for modeling hand-object interactions in 3D from RGB images that show a hand which is holding a novel object from a known category. We design a Convolutional Neural Network (CNN) for Hand-held Object Pose and Shape estimation call
Externí odkaz:
http://arxiv.org/abs/1903.03340
We develop an approach that benefits from large simulated datasets and takes full advantage of the limited online data that is most relevant. We propose a variant of Bayesian optimization that alternates between using informed and uninformed kernels.
Externí odkaz:
http://arxiv.org/abs/1810.04438
Autor:
Kokic, Mia
Task-oriented grasping refers to the problem of computing stable grasps on objects that allow for a subsequent execution of a task. Although grasping objects in a task-oriented manner comes naturally to humans, it is still very challenging for robots
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::b2fe59b4ac7c4240aca6ed3b3a5813da
http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-282832
http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-282832
Akademický článek
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Publikováno v:
Annual Review of Control, Robotics & Autonomous Systems; 2019, Vol. 2, p161-179, 19p
