The principal aim is to contribute to the identification of the inverse dynamics of the bipedal
robot by training several Artificial Neural Network (ANN) models. These include Feedfor-
ward Neural Networks (FFNN), Long Short-Term Memory(LSTM), and Recurrent Neural
Networks (RNN). The project will compare these models to determine the best performer in
terms of learning the inverse dynamics of the bipedal robot, considering both performance and
computational complexity. The main tools for the achievement of the task are CoppeliaSim
simulation and Python programming language