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pro vyhledávání: '"Wyatt, P. L."'
Dexterous grasping of a novel object given a single view is an open problem. This paper makes several contributions to its solution. First, we present a simulator for generating and testing dexterous grasps. Second we present a data set, generated by
Externí odkaz:
http://arxiv.org/abs/1908.04293
Publikováno v:
Workshop on Crossmodal Learning for Intelligent Robotics 2nd Edition. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2018
The hype about sensorimotor learning is currently reaching high fever, thanks to the latest advancement in deep learning. In this paper, we present an open-source framework for collecting large-scale, time-synchronised synthetic data from highly disp
Externí odkaz:
http://arxiv.org/abs/1907.09775
This paper concerns the problem of how to learn to grasp dexterously, so as to be able to then grasp novel objects seen only from a single view-point. Recently, progress has been made in data-efficient learning of generative grasp models which transf
Externí odkaz:
http://arxiv.org/abs/1907.06053
We present a parametric formulation for learning generative models for grasp synthesis from a demonstration. We cast new light on this family of approaches, proposing a parametric formulation for grasp synthesis that is computationally faster compare
Externí odkaz:
http://arxiv.org/abs/1906.11548
Autor:
Zito, Claudio, Ortenzi, Valerio, Adjigble, Maxime, Kopicki, Marek, Stolkin, Rustam, Wyatt, Jeremy L.
Belief space planning is a viable alternative to formalise partially observable control problems and, in the recent years, its application to robot manipulation problems has grown. However, this planning approach was tried successfully only on simpli
Externí odkaz:
http://arxiv.org/abs/1903.05517
Akademický článek
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Autor:
Arruda, Ermano, Mathew, Michael J, Kopicki, Marek, Mistry, Michael, Azad, Morteza, Wyatt, Jeremy L
Planning robust robot manipulation requires good forward models that enable robust plans to be found. This work shows how to achieve this using a forward model learned from robot data to plan push manipulations. We explore learning methods (Gaussian
Externí odkaz:
http://arxiv.org/abs/1710.04005
Akademický článek
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Recent advances have been made in learning of grasps for fully actuated hands. A typical approach learns the target locations of finger links on the object. When a new object must be grasped, new finger locations are generated, and a collision free r
Externí odkaz:
http://arxiv.org/abs/1609.07592
Hierarchical feature learning based on convolutional neural networks (CNN) has recently shown significant potential in various computer vision tasks. While allowing high-quality discriminative feature learning, the downside of CNNs is the lack of exp
Externí odkaz:
http://arxiv.org/abs/1609.03795