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Autonomous Robots

, Volume 42, Issue 5, pp 1053–1065 | Cite as

One-shot learning of human–robot handovers with triadic interaction meshes

  • David Vogt
  • Simon Stepputtis
  • Bernhard Jung
  • Heni Ben Amor
Article
Part of the following topical collections:
  1. Special Issue: Learning for Human-Robot Collaboration

Abstract

We propose an imitation learning methodology that allows robots to seamlessly retrieve and pass objects to and from human users. Instead of hand-coding interaction parameters, we extract relevant information such as joint correlations and spatial relationships from a single task demonstration of two humans. At the center of our approach is an interaction model that enables a robot to generalize an observed demonstration spatially and temporally to new situations. To this end, we propose a data-driven method for generating interaction meshes that link both interaction partners to the manipulated object. The feasibility of the approach is evaluated in a within user study which shows that human–human task demonstration can lead to more natural and intuitive interactions with the robot.

Keywords

Human–human demonstration Human–robot interaction Handover Interaction mesh 

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Faculty of Mathematics and InformaticsTechnical University Bergakademie FreibergFreibergGermany
  2. 2.School of Computing, Informatics, Decision Systems EngineeringArizona State UniversityTempeUSA

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