Loss Function Considering Dead Zone for Neural Networks
Koki Inami Koki Yamane Sho Sakaino
It is important to reveal the inverse dynamics of manipulators to improve
control performance of model-based control. Neural networks (NNs) are promising
techniques to represent complicated inverse dynamics while they require a large
amount of motion data. However, motion data in dead zones of actuators is not
suitable for training models decreasing the number of useful training data. In
this study, based on the fact that the manipulator joint does not work
irrespective of input torque in dead zones, we propose a new loss function that
considers only errors of joints not in dead zones. The proposed method enables
to increase in the amount of motion data available for training and the
accuracy of the inverse dynamics computation. Experiments on actual equipment
using a three-degree-of-freedom (DOF) manipulator showed higher accuracy than
conventional methods. We also confirmed and discussed the behavior of the model
of the proposed method in dead zones.