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Comparing Semi-Parametric Model Learning Algorithms for Dynamic Model Estimation in Robotics

Riedel, Sebastian and Stulp, Freek (2019) Comparing Semi-Parametric Model Learning Algorithms for Dynamic Model Estimation in Robotics. Other.

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Official URL: https://arxiv.org/abs/1906.11909


Physical modeling of robotic system behavior is the foundation for controlling many robotic mechanisms to a satisfactory degree. Mechanisms are also typically designed in a way that good model accuracy can be achieved with relatively simple models and model identification strategies. If the modeling accuracy using physically based models is not enough or too complex, model-free methods based on machine learning techniques can help. Of particular interest to us was therefore the question to what degree semi-parametric modeling techniques, meaning combinations of physical models with machine learning, increase the modeling accuracy of inverse dynamics models which are typically used in robot control. To this end, we evaluated semi-parametric Gaussian process regression and a novel model-based neural network architecture, and compared their modeling accuracy to a series of naive semi-parametric, parametric-only and non-parametric-only regression methods. The comparison has been carried out on three test scenarios, one involving a real test-bed and two involving simulated scenarios, with the most complex scenario targeting the modeling a simulated robot's inverse dynamics model. We found that in all but one case, semi-parametric Gaussian process regression yields the most accurate models, also with little tuning required for the training procedure.

Item URL in elib:https://elib.dlr.de/136059/
Document Type:Monograph (Other)
Title:Comparing Semi-Parametric Model Learning Algorithms for Dynamic Model Estimation in Robotics
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Riedel, SebastianUNSPECIFIEDhttps://orcid.org/0000-0002-3655-2486UNSPECIFIED
Stulp, FreekUNSPECIFIEDhttps://orcid.org/0000-0001-9555-9517UNSPECIFIED
Date:27 June 2019
Open Access:No
Gold Open Access:No
In ISI Web of Science:No
Keywords:robotics, supervise learning, Gaussian process
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Space System Technology
DLR - Research area:Raumfahrt
DLR - Program:R SY - Space System Technology
DLR - Research theme (Project):R - Vorhaben Intelligente Mobilität (old)
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Robotics and Mechatronics (since 2013) > Cognitive Robotics
Deposited By: Stulp, Freek
Deposited On:28 Mar 2023 16:41
Last Modified:28 Mar 2023 16:42

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