Estimation of perturbations in robotic behavior using dynamic mode decomposition

Erik Berger, Mark Sastuba, David Vogt, Bernhard Jung, Heni Ben Amor

Research output: Contribution to journalArticle

29 Scopus citations

Abstract

Physical 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.

Original languageEnglish (US)
Pages (from-to)331-343
Number of pages13
JournalAdvanced Robotics
Volume29
Issue number5
DOIs
StatePublished - Mar 4 2015
Externally publishedYes

Keywords

  • dynamic mode decomposition
  • external perturbation
  • model learning
  • physical humanrobot interaction
  • usability in humanrobot interaction

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Human-Computer Interaction
  • Hardware and Architecture
  • Computer Science Applications

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