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Teaching robots to cooperate with humans in dynamic manipulation tasks based on multi-modal human-in-the-loop approach

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Abstract

We propose an approach to efficiently teach robots how to perform dynamic manipulation tasks in cooperation with a human partner. The approach utilises human sensorimotor learning ability where the human tutor controls the robot through a multi-modal interface to make it perform the desired task. During the tutoring, the robot simultaneously learns the action policy of the tutor and through time gains full autonomy. We demonstrate our approach by an experiment where we taught a robot how to perform a wood sawing task with a human partner using a two-person cross-cut saw. The challenge of this experiment is that it requires precise coordination of the robot’s motion and compliance according to the partner’s actions. To transfer the sawing skill from the tutor to the robot we used Locally Weighted Regression for trajectory generalisation, and adaptive oscillators for adaptation of the robot to the partner’s motion.

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Notes

  1. Stiffness is the inverse of compliance.

  2. Error is equal to the difference between the demonstrated (desired) and learnt performance.

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Acknowledgments

We thank L. Žlajpah for discussions and for providing us with the robot control infrastructure. This work was supported by the Slovenian Research Agency, programme P2-0076 and grant J2-2272, and the Slovenian Ministry of Higher Education, Science and Technology.

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Authors and Affiliations

  1. Department of Automation, Biocybernetics and Robotics, Jožef Stefan Institute, Jamova cesta 39, 1000 , Ljubljana, Slovenia

    Luka Peternel, Tadej Petrič & Jan Babič

  2. Computer Science, Özyeğin University, Nisantepe Mah. Orman Sok. No:13, 34794 , Alemdag, Cekmekoy, Istanbul, Turkey

    Erhan Oztop

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  1. Luka Peternel
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  2. Tadej Petrič
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Correspondence to Luka Peternel.

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Peternel, L., Petrič, T., Oztop, E. et al. Teaching robots to cooperate with humans in dynamic manipulation tasks based on multi-modal human-in-the-loop approach. Auton Robot 36, 123–136 (2014). https://doi.org/10.1007/s10514-013-9361-0

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