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Convolutional neural networks (CNNs) have recently been very successful in a variety of computer vision tasks, especially on those linked to recognition. Optical flow estimation has not been among the tasks CNNs succeeded at. In this paper we construct CNNs which are capable of solving the optical flow estimation problem as a supervised learning task. We(More)
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Recurrent Neural Networks (RNNs) are rich models for the processing of sequential data. Recent work on advancing the state of the art has been focused on the optimization or modelling of RNNs, mostly motivated by adressing the problems of the vanishing and exploding gradients. The control of overfitting has seen considerably less attention. This paper(More)
In this paper we describe and practically demonstrate a robotic arm/hand system that is controlled in realtime in 6D Cartesian space through measured human muscular activity. The soft-robotics control architecture of the robotic system ensures safe physical human robot interaction as well as stable behaviour while operating in an unstructured environment.(More)
A novel approach to antagonism in robotic systems is introduced and investigated as the basis for an un-equalled, highly anthropomorphic hand–arm system currently being developed. This hand–arm system, consisting of a 19-d.o.f. hand and a 7-d.o.f. flexible arm, will be based on antagonistic principles in order to study and mimic the human musculoskeletal(More)
One of the major problems when dealing with highly dexterous, active hand prostheses is their control by the patient wearing them. With the advances in mechatronics, building prosthetic hands with multiple active degrees of freedom is realisable, but actively controlling the position and especially the exerted force of each finger cannot yet be done(More)
D ecades of research into the str ucture and function of the cerebellum have led to a clear understanding of many of its cells, as well as how lear ning takes place. Furthermore, there are many theories on what signals the cerebellum operates on, and how it works in concert with other parts of the ner vous system. Nevertheless, the application of(More)
We present a computationally ecient architecture for image super-resolution that achieves state-of-the-art results on images with large spatial extend. Apart from utilizing Convolutional Neural Networks , our approach leverages recent advances in fast approximate inference for sparse coding. We empirically show that upsampling methods work much better on(More)
Cerebellar models have long been advocated as viable models for robot dynamics control. Building on an increasing insight in and knowledge of the biological cerebellum, many models have been greatly refined, of which some computational models have emerged with useful properties with respect to robot dynamics control. Looking at the application side,(More)
Motor synergies have been investigated since the 1980s as a simplifying representation of motor control by the nervous system. This way of representing finger positional data is in particular useful to represent the kinematics of the human hand. Whereas, so far, the focus has been on kinematic synergies, that is common patterns in the motion of the hand and(More)