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Multi-users online recognition of technical gestures for natural human–robot collaboration in manufacturing

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Abstract

Human–robot collaboration in industrial context requires a smooth, natural and efficient coordination between robot and human operators. The approach we propose to achieve this goal is to use online recognition of technical gestures. In this paper, we present together, and analyze, parameterize and evaluate much more thoroughly, three findings previously unveiled separately by us in several conference presentations: (1) we show on a real prototype that multi-users continuous real-time recognition of technical gestures on an assembly-line is feasible (\(\approx \) 90% recall and precision in our case-study), using only non-intrusive sensors (depth-camera with a top-view, plus inertial sensors placed on tools); (2) we formulate an end-to-end methodology for designing and developing such a system; (3) we propose a method for adapting to new users our gesture recognition. Furthermore we present here two new findings: (1) by comparing recognition performances using several sets of features, we highlight the importance of choosing features that focus on the effective part of gestures, i.e. usually hands movements; (2) we obtain new results suggesting that enriching a multi-users training set can lead to higher precision than using a separate training dataset for each operator.

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Notes

  1. http://nickgillian.com/grt/.

  2. Note that in modern factories, many tools such as screwing guns are actually connected to the assembly-line information system, so that binary information such as “moving” or “in use” can be readily available even without having to place inertial sensors on them.

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Acknowledgements

This research benefited from the support of the Chair ‘PSA Peugeot Citroën Robotics and Virtual Reality’, led by MINES ParisTech and supported by PEUGEOT S.A. The partners of the Chair cannot be held accountable for the content of this paper, which engages the authors’ responsibility only.

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Authors and Affiliations

  1. Center for Robotics, MINES ParisTech, PSL Research University, 60 boulevard Saint Michel, 75006, Paris, France

    Eva Coupeté, Fabien Moutarde & Sotiris Manitsaris

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  1. Eva Coupeté
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  2. Fabien Moutarde
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  3. Sotiris Manitsaris
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Correspondence to Fabien Moutarde.

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This is one of the several papers published in Autonomous Robots comprising the Special Issue on Learning for Human–Robot Collaboration.

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Coupeté, E., Moutarde, F. & Manitsaris, S. Multi-users online recognition of technical gestures for natural human–robot collaboration in manufacturing. Auton Robot 43, 1309–1325 (2019). https://doi.org/10.1007/s10514-018-9704-y

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