IEEE Robotics & Automation Magazine - June 2020 - 86

gravity-equilibrium equations of the system, which may not
be straightforward for complex kinematic configurations.
This work proposes a gravity compensation strategy based
on ML methods as an easy and fast approach to the problem.
It describes the strategy's implementation and compares the
method with the traditional approach, which is also
described in detail. In general, the results show that the traditional analytical solution performs better than the ML
approach. However, in situations where it is difficult to derive
an analytical solution and obtaining training data for ML
approaches is considerably easier, the results show that ML
models offer a promising alternative with certain limitations
regarding workspace coverage. The results can also be
extended to other robotic and haptic domains. Furthermore,
we believe that ML can be applied to other rehabilitation
tasks, such as monitoring patients' progress by gathering and
analyzing multiple robot sensor measures during exercise
and personalizing the assistance control algorithms for each
individual patient and exercise.
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Axier Ugartemendia, Ceit and Tecnun, University of Navarra,
Donostia-San Sebastián, Spain. Email: augartemendia@
ceit.es.
Daniel Rosquete, Ceit and Tecnun, University of Navarra, Donostia-San Sebastián, Spain. Email: drosquete@ceit.es.
Jorge Juan Gil, Ceit and Tecnun, University of Navarra, Donostia-San Sebastián, Spain. Email: jjgil@tecnun.es.
Iñaki Díaz, Ceit and Tecnun, University of Navarra, Donostia-
San Sebastián, Spain. Email: idiaz@ceit.es.
Diego Borro, Ceit and Tecnun, University of Navarra, Donostia-San Sebastián, Spain. Email: dborro@ceit.es.
IEEE Robotics & Automation Magazine - June 2020

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