Model Learning for Robotic Manipulators using Recurrent Neural Networks

Abstract

Reliability of the traditional analytical model building techniques for Robotic Manipulators is debatable with higher Degrees of Freedom (DoF) and under dynamic, uncertain environments. Keeping these uncertainties and inaccuracies in the backdrop, the researchers have been encouraged to use supervised machine learning techniques as a better alternative for data-driven model learning. The main advantage of data-driven models lies in their adaptability to cope with the model variations in real-time. Considering the proven superiority of the Recurrent Neural Networks (RNN) family in sequence modelling, this paper projects three members of this family, namely Simple RNN (SRNN), Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) as promising candidates for Robotic manipulator model learning tasks. Simulation results obtained by using some publicly available data sets of KUKA LWR and SARCOS Robot Arm with 7-DoF, clearly show that model learning performance of both LSTM and GRU are better than other classical regression based techniques.