Loss Function Considering Dead Zone for Neural Networks
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 13 of its 43 references that are in the map. First posted on arXiv 1 Feb 2024.
Builds on
13 of 43 references.
References · 13
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Citing papers per year: 2026: 2.
2 papers.