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 language | English (US) |
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Pages (from-to) | 331-343 |
Number of pages | 13 |
Journal | Advanced Robotics |
Volume | 29 |
Issue number | 5 |
DOIs | |
State | Published - Mar 4 2015 |
Externally published | Yes |
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