  One of the most commonly performed manipulation in a human's daily life is
pouring. Many factors have an effect on target accuracy, including pouring
velocity, rotation angle, geometric of the source, and the receiving
containers. This paper presents an approach to increase the repeatability and
accuracy of the robotic manipulator by estimating the change in the amount of
water of the pouring cup to a sequence of pouring actions using multiple layers
of the deep recurrent neural network, especially gated recurrent units (GRU).
The proposed GRU model achieved a validation mean squared error as low as 1e-4
(lbf) for the predicted value of weight f(t). This paper contains a
comprehensive evaluation and analysis of numerous experiments with various
designs of recurrent neural networks and hyperparameters fine-tuning.
