  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.
