LSTM Learning of Inverse Dynamics with Contact in Various Environments

A machine learning method has been introduced to solve the problem of inverse dynamics with contact in various environments. Conventional methods need multiple contact models to switch according to situations, while such methods have a difficulty in dealing with different environments. We propose a machine learning method that can handle various environments with a single learning model. We use long short-term memory as a learning model with high expression ability. From the verification, the proposed method showed higher performance than Gaussian processes. In addition, the performance of the model was improved by using training data collected under various environmental conditions.