Learning-based adaption of robotic friction models
- URL: http://arxiv.org/abs/2310.16688v1
- Date: Wed, 25 Oct 2023 14:50:15 GMT
- Title: Learning-based adaption of robotic friction models
- Authors: Philipp Scholl, Maged Iskandar, Sebastian Wolf, Jinoh Lee, Aras Bacho,
Alexander Dietrich, Alin Albu-Sch\"affer and Gitta Kutyniok
- Abstract summary: We introduce a novel approach to adapt an existing friction model to new dynamics using as little data as possible.
Our proposed estimator outperforms the conventional model-based approach and the base neural network significantly.
Our method does not rely on data with external load during training, eliminating the need for external torque sensors.
- Score: 48.453527255659296
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: In the Fourth Industrial Revolution, wherein artificial intelligence and the
automation of machines occupy a central role, the deployment of robots is
indispensable. However, the manufacturing process using robots, especially in
collaboration with humans, is highly intricate. In particular, modeling the
friction torque in robotic joints is a longstanding problem due to the lack of
a good mathematical description. This motivates the usage of data-driven
methods in recent works. However, model-based and data-driven models often
exhibit limitations in their ability to generalize beyond the specific dynamics
they were trained on, as we demonstrate in this paper. To address this
challenge, we introduce a novel approach based on residual learning, which aims
to adapt an existing friction model to new dynamics using as little data as
possible. We validate our approach by training a base neural network on a
symmetric friction data set to learn an accurate relation between the velocity
and the friction torque. Subsequently, to adapt to more complex asymmetric
settings, we train a second network on a small dataset, focusing on predicting
the residual of the initial network's output. By combining the output of both
networks in a suitable manner, our proposed estimator outperforms the
conventional model-based approach and the base neural network significantly.
Furthermore, we evaluate our method on trajectories involving external loads
and still observe a substantial improvement, approximately 60-70\%, over the
conventional approach. Our method does not rely on data with external load
during training, eliminating the need for external torque sensors. This
demonstrates the generalization capability of our approach, even with a small
amount of data-only 43 seconds of a robot movement-enabling adaptation to
diverse scenarios based on prior knowledge about friction in different
settings.
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