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• W1981193003 abstract "AbstractPhysical human–robot interaction tasks require robots that can detect and react to external perturbations caused by the human partner. In this contribution, we present a machine learning approach for detecting, estimating, and compensating for such external perturbations using only input from standard sensors. This machine learning approach makes use of Dynamic Mode Decomposition (DMD), a data processing technique developed in the field of fluid dynamics, which is applied to robotics for the first time. DMD is able to isolate the dynamics of a nonlinear system and is therefore well suited for separating noise from regular oscillations in sensor readings during cyclic robot movements. In a training phase, a DMD model for behavior-specific parameter configurations is learned. During task execution, the robot must estimate the external forces exerted by a human interaction partner. We compare the DMD-based approach to other interpolation schemes. A variant, sparsity promoting DMD, is particularly well suited for high-noise sensors. Results of a user study show that our DMD-based machine learning approach can be used to design physical human–robot interaction techniques that not only result in robust robot behavior but also enjoy a high usability.Keywords: physical human–robot interactiondynamic mode decompositionmodel learningexternal perturbationusability in human–robot interaction NotesSupplemental data for this article can be accessed at http://dx.doi.org/10.1080/01691864.2014.981292.1 http://youtu.be/wHZYx6Dzswk." @default.
• W1981193003 created "2016-06-24" @default.
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• W1981193003 date "2015-03-04" @default.
• W1981193003 modified "2023-10-17" @default.
• W1981193003 title "Estimation of perturbations in robotic behavior using dynamic mode decomposition" @default.
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• W1981193003 doi "https://doi.org/10.1080/01691864.2014.981292" @default.
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