Learning motion and impedance behaviors from human demonstrations

Human-robot skill transfer has been deeply investigated from a kinematic point of view, generating various approaches to increase the robot knowledge in a simple and compact way. Nevertheless, social robotics applications require a close and active interaction with humans in a safe and natural manne...

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Bibliographic Details
Published in2014 11th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI) pp. 368 - 373
Main Authors Saveriano, Matteo, Dongheui Lee
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2014
Subjects
Computational modeling
Covariance matrices
Eigenvalues and eigenfunctions
Force
Impedance
Learning from Demonstrations
Robots
state-dependent behavior
Trajectory
variable impedance control
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Summary:Human-robot skill transfer has been deeply investigated from a kinematic point of view, generating various approaches to increase the robot knowledge in a simple and compact way. Nevertheless, social robotics applications require a close and active interaction with humans in a safe and natural manner. Torque controlled robots, with their variable impedance capabilities, seem a viable option toward a safe and profitable human-robot interaction. In this paper, an approach is proposed to simultaneously learn motion and impedance behaviors from tasks demonstrations. Kinematic aspects of the task are represented in a statistical way, while the variability along the demonstrations is used to define a variable impedance behavior. The effectiveness of our approach is validated with simulations on real and synthetic data.
DOI:10.1109/URAI.2014.7057371