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Estimating Forces of Robotic Pouring Using a LSTM RNN
In machine learning, it is very important for a robot to be able to esti...
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Pouring Sequence Prediction using Recurrent Neural Network
Human does their daily activity and cooking by teaching and imitating wi...
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CloudScan - A configuration-free invoice analysis system using recurrent neural networks
We present CloudScan; an invoice analysis system that requires zero conf...
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Character-level Recurrent Neural Networks in Practice: Comparing Training and Sampling Schemes
Recurrent neural networks are nowadays successfully used in an abundance...
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Accurate Robotic Pouring for Serving Drinks
Pouring is the second most frequently executed motion in cooking scenari...
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Deep Recurrent Neural Network for Multi-target Filtering
This paper addresses the problem of fixed motion and measurement models ...
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A Basic Recurrent Neural Network Model
We present a model of a basic recurrent neural network (or bRNN) that in...
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Dynamics Estimation Using Recurrent Neural Network
There is a plenty of research going on in field of robotics. One of the most important task is dynamic estimation of response during motion. One of the main applications of this research topics is the task of pouring, which is performed daily and is commonly used while cooking. We present an approach to estimate response to a sequence of manipulation actions. We are experimenting with pouring motion and the response is the change of the amount of water in the pouring cup. The pouring motion is represented by rotation angle and the amount of water is represented by its weight. We are using recurrent neural networks for building the neural network model to train on sequences which represents 1307 trails of pouring. The model gives great results on unseen test data which does not too different with training data in terms of dimensions of the cup used for pouring and receiving. The loss obtained with this test data is 4.5920. The model does not give good results on generalization experiments when we provide a test set which has dimensions of the cup very different from those in training data.
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