A Method for Embodied Co-Learning in Interdependent Human-Robot Teams

Master thesis (2023)

Authors

H.W. Loopik Mechanical Engineering

Contributors

L. Peternel Human-Robot Interaction - Mechanical, Maritime and Materials Engineering (supervisor 1)
E.M. van Zoelen Interactive Intelligence - EEMCS (supervisor 1)
David Abbink Human-Robot Interaction - Mechanical, Maritime and Materials Engineering (supervisor 1)
Faculty
Mechanical Engineering
Copyright: © 2023 Hugo Loopik
Reinforcement Learning (RL) Q-Learning Interdependence Human-Robot Interaction Human-Robot Collaboration Co-Learning Handover
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Published Date

06-07-2023

Language

English

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Faculty

Mechanical Engineering

Abstract

This paper addresses the research question: “How can a human-robot team achieve co-learning, and interdependence in physically embodied tasks?”
A method has been developed that enables a human-robot team to co-learn the handover of an object from the robot to the human. Five design requirements were composed to address the challenges of human-robot co-learning in physically embodied environments. The method is based on a Q-learning algorithm that was adapted and extended to meet these requirements. An experiment was conducted with six participants. For every human-robot team, each design requirement was qualitatively evaluated. Interdependent co-learning was identified in three of the six teams. The limitation of the design, and how this method can be improved further, was discussed. The method, presented in this paper, demonstrates how human-robot co-learning and interdependence can be enabled in physically embodied tasks.