Abstract
In this paper, we focus on estimating the forward kinematic equation of robots with multilayer feed-forward neural networks. The effectiveness of this approach is tested on a simulated kinematic model of the 7-DOF Sawyer Robotic Arm. In the initial sections of the paper, we discuss related work that associates with the creation of model agnostic control schemes on a kinematic level. Moreover, we formalize the kinematic problem as a supervised problem and we propose an MLP architecture to solve the problem. Lastly, we present experimental results and discuss the potential and importance to create model agnostic control schemes with machine learning.
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Acknowledgments
This work is supported in part by the National Science Foundation under award numbers 1338118 and 1719031. Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
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Theofanidis, M., Sayed, S.I., Cloud, J., Brady, J., Makedon, F. (2018). Kinematic Estimation with Neural Networks for Robotic Manipulators. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds) Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science(), vol 11141. Springer, Cham. https://doi.org/10.1007/978-3-030-01424-7_77
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DOI: https://doi.org/10.1007/978-3-030-01424-7_77
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