  In machine learning, it is very important for a robot to be able to estimate
dynamics from sequences of input data. This problem can be solved using a
recurrent neural network. In this paper, we will discuss the preprocessing of
10 states of the dataset, then the use of a LSTM recurrent neural network to
estimate one output state (dynamics) from the other 9 input states. We will
discuss the architecture of the recurrent neural network, the data collection
and preprocessing, the loss function, the results of the test data, and the
discussion of changes that could improve the network. The results of this paper
will be used for artificial intelligence research and identify the capabilities
of a LSTM recurrent neural network architecture to estimate dynamics of a
system.
